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Basaia S, Agosta F, Sarasso E, Balestrino R, Stojković T, Stanković I, Tomić A, Marković V, Vignaroli F, Stefanova E, Kostić VS, Filippi M. Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease. Radiology 2024; 311:e232454. [PMID: 38916507 DOI: 10.1148/radiol.232454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.
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Affiliation(s)
- Silvia Basaia
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Federica Agosta
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Elisabetta Sarasso
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Roberta Balestrino
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Tanja Stojković
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Iva Stanković
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Aleksandra Tomić
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Vladana Marković
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Francesca Vignaroli
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Elka Stefanova
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Vladimir S Kostić
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Division of Neuroscience (S.B., F.A., E. Sarasso, R.B., M.F.), Neurology Unit (F.A., M.F.), Department of Rehabilitation and Functional Recovery (E. Sarasso), Neurorehabilitation Unit (M.F.), and Neurophysiology Service (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy (F.A., R.B., M.F.); Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Maternal Child Health, University of Genoa, Genoa, Italy (E. Sarasso); Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (T.S., I.S., A.T., V.M., E. Stefanova, V.S.K.); and Neurology Unit, University Hospital Maggiore della Carità, Novara, Italy (F.V.)
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Han S, Fang K, Zheng R, Li S, Zhou B, Sheng W, Wen B, Liu L, Wei Y, Chen Y, Chen H, Cui Q, Cheng J, Zhang Y. Gray matter atrophy is constrained by normal structural brain network architecture in depression. Psychol Med 2024; 54:1318-1328. [PMID: 37947212 DOI: 10.1017/s0033291723003161] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
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Vorobyov V, Deev A, Chaprov K, Ninkina N. Disruption of Electroencephalogram Coherence between Cortex/Striatum and Midbrain Dopaminergic Regions in the Knock-Out Mice with Combined Loss of Alpha, Beta, and Gamma Synucleins. Biomedicines 2024; 12:881. [PMID: 38672235 PMCID: PMC11048202 DOI: 10.3390/biomedicines12040881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/10/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
The malfunctioning of the brain synucleins is associated with pathogenesis of Parkinson's disease. Synucleins' ability to modulate various pre-synaptic processes suggests their modifying effects on the electroencephalogram (EEG) recorded from different brain structures. Disturbances in interrelations between them are critical for the onset and evolution of neurodegenerative diseases. Recently, we have shown that, in mice lacking several synucleins, differences between the frequency spectra of EEG from different brain structures are correlated with specificity of synucleins' combinations. Given that EEG spectra are indirect characteristics of inter-structural relations, in this study, we analyzed a coherence of instantaneous values for EEGs recorded from different structures as a direct measure of "functional connectivity" between them. METHODS EEG data from seven groups of knock-out (KO) mice with combined deletions of alpha, beta, and gamma synucleins versus a group of wild-type (WT) mice were compared. EEG coherence was estimated between the cortex (MC), putamen (Pt), ventral tegmental area (VTA), and substantia nigra (SN) in all combinations. RESULTS EEG coherence suppression, predominantly in the beta frequency band, was observed in KO mice versus WT littermates. The suppression was minimal in MC-Pt and VTA-SN interrelations in all KO groups and in all inter-structural relations in mice lacking either all synucleins or only beta synuclein. In other combinations of deleted synucleins, significant EEG coherence suppression in KO mice was dominant in relations with VTA and SN. CONCLUSION Deletions of the synucleins produced significant attenuation of intra-cerebral EEG coherence depending on the imbalance of different types of synucleins.
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Affiliation(s)
- Vasily Vorobyov
- Institute of Cell Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia;
| | - Alexander Deev
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia;
| | - Kirill Chaprov
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 142432 Chernogolovka, Russia;
- School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK
| | - Natalia Ninkina
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, 142432 Chernogolovka, Russia;
- School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff CF10 3AX, UK
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Wang G, Jiang N, Ma Y, Suo D, Liu T, Funahashi S, Yan T. Using a deep generation network reveals neuroanatomical specificity in hemispheres. PATTERNS (NEW YORK, N.Y.) 2024; 5:100930. [PMID: 38645770 PMCID: PMC11026975 DOI: 10.1016/j.patter.2024.100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 04/23/2024]
Abstract
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
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Zhang Y, Wang T, Wang S, Zhuang X, Li J, Guo S, Lei J. Gray matter atrophy and white matter lesions burden in delayed cognitive decline following carbon monoxide poisoning. Hum Brain Mapp 2024; 45:e26656. [PMID: 38530116 DOI: 10.1002/hbm.26656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/21/2024] [Accepted: 02/25/2024] [Indexed: 03/27/2024] Open
Abstract
Gray matter (GM) atrophy and white matter (WM) lesions may contribute to cognitive decline in patients with delayed neurological sequelae (DNS) after carbon monoxide (CO) poisoning. However, there is currently a lack of evidence supporting this relationship. This study aimed to investigate the volume of GM, cortical thickness, and burden of WM lesions in 33 DNS patients with dementia, 24 DNS patients with mild cognitive impairment, and 51 healthy controls. Various methods, including voxel-based, deformation-based, surface-based, and atlas-based analyses, were used to examine GM structures. Furthermore, we explored the connection between GM volume changes, WM lesions burden, and cognitive decline. Compared to the healthy controls, both patient groups exhibited widespread GM atrophy in the cerebral cortices (for volume and cortical thickness), subcortical nuclei (for volume), and cerebellum (for volume) (p < .05 corrected for false discovery rate [FDR]). The total volume of GM atrophy in 31 subregions, which included the default mode network (DMN), visual network (VN), and cerebellar network (CN) (p < .05, FDR-corrected), independently contributed to the severity of cognitive impairment (p < .05). Additionally, WM lesions impacted cognitive decline through both direct and indirect effects, with the latter mediated by volume reduction in 16 subregions of cognitive networks (p < .05). These preliminary findings suggested that both GM atrophy and WM lesions were involved in cognitive decline in DNS patients following CO poisoning. Moreover, the reduction in the volume of DMN, VN, and posterior CN nodes mediated the WM lesions-induced cognitive decline.
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Affiliation(s)
- Yanli Zhang
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
| | - Tianhong Wang
- Department of Neurology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Shuaiwen Wang
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
| | - Xin Zhuang
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
| | - Jianlin Li
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
| | - Shunlin Guo
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
| | - Junqiang Lei
- Deparment of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China
- The Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou, Gansu, China
- Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou, Gansu, China
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6
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Reddy S, Giri D, Patel R. Artificial Intelligence Diagnosis of Parkinson's Disease From MRI Scans. Cureus 2024; 16:e58841. [PMID: 38784299 PMCID: PMC11114626 DOI: 10.7759/cureus.58841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, affecting approximately 6.1 million people worldwide, according to estimates from the Parkinson's Foundation. Early and accurate diagnosis of PD is crucial for effective management and treatment. In this study, we aimed to develop an artificial intelligence (AI) model capable of distinguishing between magnetic resonance imaging (MRI) scans of individuals with PD and those without PD. A total of 442 MRI scans were utilized for training the AI model, comprising 221 scans of individuals diagnosed with PD and 221 scans of healthy controls. The dataset, obtained from a publicly available image dataset on Kaggle.com, was randomly split into three sets: training, validation, and testing, with 80%, 10%, and 10% of the data allocated to each set, respectively. Leveraging Google's Collaboration platform for model training, the AI model achieved exceptional performance, with accuracy, precision, recall (sensitivity), specificity, and F1-score all measuring at high levels. Additionally, the area under the receiver operating characteristic curve (AUC) for the model was found to be 1, indicating strong discrimination between PD and non-PD cases. This study presents a novel AI model capable of accurately identifying PD from MRI scans with high precision and reliability, offering promise for enhancing early diagnosis and personalized treatment strategies for individuals affected by PD.
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Affiliation(s)
- Shreya Reddy
- Biomedical Sciences, Creighton University, Omaha, USA
| | - Dinesh Giri
- Research, California Northstate University College of Medicine, Elk Grove, USA
| | - Rakesh Patel
- Internal Medicine, East Tennessee State University Quillen College of Medicine, Johnson City, USA
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7
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Pak V, Adewale Q, Bzdok D, Dadar M, Zeighami Y, Iturria-Medina Y. Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions. eLife 2024; 12:RP89368. [PMID: 38512130 PMCID: PMC10957173 DOI: 10.7554/elife.89368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.
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Affiliation(s)
- Veronika Pak
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Department of Biomedical Engineering, McGill UniversityMontrealCanada
- School of Computer Science, McGill UniversityMontrealCanada
- Mila – Quebec Artificial Intelligence InstituteMontrealCanada
| | | | | | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, McGill UniversityMontrealCanada
- McConnell Brain Imaging Centre, Montreal Neurological InstituteMontrealCanada
- Ludmer Centre for Neuroinformatics & Mental HealthMontrealCanada
- Department of Biomedical Engineering, McGill UniversityMontrealCanada
- McGill Centre for Studies in AgingMontrealCanada
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8
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Manera AL, Dadar M, Ducharme S, Collins DL. VentRa: distinguishing frontotemporal dementia from psychiatric disorders. Brain Commun 2024; 6:fcae069. [PMID: 38510209 PMCID: PMC10953623 DOI: 10.1093/braincomms/fcae069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 11/21/2023] [Accepted: 02/26/2024] [Indexed: 03/22/2024] Open
Abstract
The volume of the lateral ventricles is a reliable and sensitive indicator of brain atrophy and disease progression in behavioural variant frontotemporal dementia. In this study, we validate our previously developed automated tool using ventricular features (known as VentRa) for the classification of behavioural variant frontotemporal dementia versus a mixed cohort of neurodegenerative, vascular and psychiatric disorders from a clinically representative independent dataset. Lateral ventricles were segmented for 1110 subjects-14 behavioural variant frontotemporal dementia, 30 other frontotemporal dementia, 70 Lewy body disease, 898 Alzheimer's disease, 62 vascular brain injury and 36 primary psychiatric disorder from the publicly accessible National Alzheimer's Coordinating Center dataset to assess the performance of VentRa. Using ventricular features to discriminate behavioural variant frontotemporal dementia subjects from primary psychiatric disorders, VentRa achieved an accuracy rate of 84%, a sensitivity rate of 71% and a specificity rate of 89%. VentRa was able to identify behavioural variant frontotemporal dementia from a mixed age-matched cohort (i.e. other frontotemporal dementia, Lewy body disease, Alzheimer's disease, vascular brain injury and primary psychiatric disorders) and to correctly classify other disorders as 'not compatible with behavioral variant frontotemporal dementia' with a specificity rate of 83%. The specificity rates against each of the other individual cohorts were 80% for other frontotemporal dementia, 83% for Lewy body disease, 83% for Alzheimer's disease, 84% for vascular brain injury and 89% for primary psychiatric disorders. VentRa is a robust and generalizable tool with potential usefulness for improving the diagnostic certainty of behavioural variant frontotemporal dementia, particularly for the differential diagnosis with primary psychiatric disorders.
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Affiliation(s)
- Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, QC H4H 1R3, Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, QC H4H 1R3, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
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9
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Pan G, Jiang Y, Zhang W, Zhang X, Wang L, Cheng W. Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression. PSYCHORADIOLOGY 2024; 4:kkae002. [PMID: 38666137 PMCID: PMC10953620 DOI: 10.1093/psyrad/kkae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 02/23/2024] [Indexed: 04/28/2024]
Abstract
Background Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = -0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1. Conclusion The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.
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Affiliation(s)
- Guoqing Pan
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua 321004, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Xuejuan Zhang
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua 321004, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai 200032, China
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10
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Sheng W, Cui Q, Guo Y, Tang Q, Fan YS, Wang C, Guo J, Lu F, He Z, Chen H. Cortical thickness reductions associate with brain network architecture in major depressive disorder. J Affect Disord 2024; 347:175-182. [PMID: 38000466 DOI: 10.1016/j.jad.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture. METHODS Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it. RESULTS We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe. CONCLUSION Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.
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Affiliation(s)
- Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - YuanHong Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; MOE Key Lab for Neuroinformation, HighField Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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11
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry 2024:10.1038/s41380-024-02442-7. [PMID: 38336840 DOI: 10.1038/s41380-024-02442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/08/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
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Affiliation(s)
- Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
| | - Sara Larivière
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - David Glahn
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Carmel Loughland
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Patricia T Michie
- School of Psychological Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - Paul E Rasser
- School of Medicine and Public Health, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Stanley Catts
- Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Paul Tooney
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J Scott
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Vaughan Carr
- School of Clinical Medicine, Discipline of Psychiatry, UNSW Sydney, Sydney, NSW, Australia
| | - Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Axel Krug
- University Hospital Bonn, Department of Psychiatry and Psychotherapy, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Igor Nenadić
- Department. of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Raquel Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | | | - Aristotle Voineskos
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Diana Tordesillas Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technischen Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Caroline Demro
- University of Minnesota Department of Psychology, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Ian S Ramsay
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Andre Schmidt
- University of Basel, Department of Psychiatry, Basel, Switzerland
| | | | | | - Irina Lebedeva
- Mental Health Research Center, Moscow, Russian Federation
| | - Cyril Höschl
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Germany
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Fleur Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Ana M Diaz Zuluaga
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Carlos López Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Felice Iasevoli
- University of Naples, Department of Neuroscience, Naples, Italy
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Bratislav Misic
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Sofie L Valk
- Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA
| | - Boris Bernhardt
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
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12
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Samantaray T, Saini J, Pal PK, Gupta CN. Brain connectivity for subtypes of parkinson's disease using structural MRI. Biomed Phys Eng Express 2024; 10:025012. [PMID: 38224618 DOI: 10.1088/2057-1976/ad1e77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/15/2024] [Indexed: 01/17/2024]
Abstract
Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.
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Affiliation(s)
- Tanmayee Samantaray
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, 560029, India
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health & Neuro Sciences, Bengaluru, 560029, India
| | - Cota Navin Gupta
- Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, India
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13
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Nogay HS, Adeli H. Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning. J Med Syst 2024; 48:15. [PMID: 38252192 PMCID: PMC10803393 DOI: 10.1007/s10916-023-02032-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/31/2023] [Indexed: 01/23/2024]
Abstract
The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.
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Affiliation(s)
- Hidir Selcuk Nogay
- Electrical and Energy Department, Bursa Uludag University, Bursa, Turkey
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, College of Medicine, The Ohio State University Neurology, 370 W. 9th Avenue, Columbus, OH, 43210, USA.
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14
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Denmon C, Wakeman DG, Vernon M, McGregor K, Krishnamurthy V, Krishnamurthy L, Stevens J, Vaughan CP. Urinary incontinence-related effects on functional connectivity circuits in persons with Parkinson's disease. Neurourol Urodyn 2023; 42:1694-1701. [PMID: 37528804 PMCID: PMC10902789 DOI: 10.1002/nau.25258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/07/2023] [Accepted: 07/24/2023] [Indexed: 08/03/2023]
Abstract
INTRODUCTION Urinary incontinence (UI) is a common and disruptive symptom of Parkinson's disease (PD). This study aimed to identify neural correlates associated with UI among PD patients with UI (UI-PD) compared to those PD patients without UI (nonUI-PD) with the expectation of demonstrating increased functional connectivity (FC) between areas in the striatum and limbic system and decreased FC in executive areas. METHODS rsfMRI and T1w data (n = 119) were retrieved from the Parkinson's Progression Markers Initiative (PPMI). Resting-state FC analyses assessed temporal covariance with anterior cingulate gyrus, precuneus, and putamen seed regions. RESULTS The UI-PD group (n = 32, 16 females) showed significantly greater positive FC between the bilateral putamen seed and the right caudate and right thalamus (p < 0.01), relative to individuals with PD but who did not have UI (n = 87, 18 females). The UI-PD group showed greater negative FC between the anterior cingulate seed and right angular gyrus (p < 0.01) relative to nonUI-PD. CONCLUSION Individuals with PD and UI display stronger FC within neural circuits likely affected by PD such as between the putamen and caudate, as well as within those associated with brain bladder control, compared to persons with PD and without UI. Clinical application based on this study's results can provide greater discernment of treatment strategies for UI-PD patients.
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Affiliation(s)
- Chanse Denmon
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
| | - Daniel G Wakeman
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
| | - Mark Vernon
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
| | - Keith McGregor
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
- Birmingham/Atlanta VA Geriatric Research Education and Clinical Center, Birmingham, Alabama, USA
| | - Venkatagiri Krishnamurthy
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Lisa Krishnamurthy
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
- Department of Physics & Astronomy, Georgia State University, Atlanta, Georgia, USA
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
| | - Jennifer Stevens
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Camille P Vaughan
- Center for Visual & Neurocognitive Rehabilitation, Atlanta VA Medical Center, Decatur, Georgia, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
- Birmingham/Atlanta VA Geriatric Research Education and Clinical Center, Atlanta, Georgia, USA
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15
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Vo A, Tremblay C, Rahayel S, Shafiei G, Hansen JY, Yau Y, Misic B, Dagher A. Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease. Neuroimage Clin 2023; 40:103523. [PMID: 38016407 PMCID: PMC10687705 DOI: 10.1016/j.nicl.2023.103523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/30/2023] [Accepted: 10/05/2023] [Indexed: 11/30/2023]
Abstract
Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.
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Affiliation(s)
- Andrew Vo
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Christina Tremblay
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Shady Rahayel
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada; Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montréal, Canada
| | - Golia Shafiei
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yvonne Yau
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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16
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Abdelgawad A, Rahayel S, Zheng YQ, Tremblay C, Vo A, Misic B, Dagher A. Predicting longitudinal brain atrophy in Parkinson's disease using a Susceptible-Infected-Removed agent-based model. Netw Neurosci 2023; 7:906-925. [PMID: 37781140 PMCID: PMC10473281 DOI: 10.1162/netn_a_00296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 11/20/2022] [Indexed: 10/03/2023] Open
Abstract
Parkinson's disease is a progressive neurodegenerative disorder characterized by accumulation of abnormal isoforms of alpha-synuclein. Alpha-synuclein is proposed to act as a prion in Parkinson's disease: In its misfolded pathologic state, it favors the misfolding of normal alpha-synuclein molecules, spreads trans-neuronally, and causes neuronal damage as it accumulates. This theory remains controversial. We have previously developed a Susceptible-Infected-Removed (SIR) computational model that simulates the templating, propagation, and toxicity of alpha-synuclein molecules in the brain. In this study, we test this model with longitudinal MRI collected over 4 years from the Parkinson's Progression Markers Initiative (1,068 T1 MRI scans, 790 Parkinson's disease scans, and 278 matched control scans). We find that brain deformation progresses in subcortical and cortical regions. The SIR model recapitulates the spatiotemporal distribution of brain atrophy observed in Parkinson's disease. We show that connectome topology and geometry significantly contribute to model fit. We also show that the spatial expression of two genes implicated in alpha-synuclein synthesis and clearance, SNCA and GBA, also influences the atrophy pattern. We conclude that the progression of atrophy in Parkinson's disease is consistent with the prion-like hypothesis and that the SIR model is a promising tool to investigate multifactorial neurodegenerative diseases over time.
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Affiliation(s)
- Alaa Abdelgawad
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
| | - Shady Rahayel
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Canada
| | - Ying-Qiu Zheng
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Christina Tremblay
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
| | - Andrew Vo
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
| | - Bratislav Misic
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
| | - Alain Dagher
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Canada
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17
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Vandendoorent B, Nackaerts E, Zoetewei D, Hulzinga F, Gilat M, Orban de Xivry JJ, Nieuwboer A. Effect of transcranial direct current stimulation on learning in older adults with and without Parkinson's disease: A systematic review with meta-analysis. Brain Cogn 2023; 171:106073. [PMID: 37611344 DOI: 10.1016/j.bandc.2023.106073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/26/2023] [Accepted: 07/31/2023] [Indexed: 08/25/2023]
Abstract
Older adults with and without Parkinson's disease show impaired retention after training of motor or cognitive skills. This systematic review with meta-analysis aims to investigate whether adding transcranial direct current stimulation (tDCS) to motor or cognitive training versus placebo boosts motor sequence and working memory training. The effects of interest were estimated between three time points, i.e. pre-training, post-training and follow-up. This review was conducted according to the PRISMA guidelines (PROSPERO: CRD42022348885). Electronic databases were searched from conception to March 2023. Following initial screening, 24 studies were eligible for inclusion in the qualitative synthesis and 20 could be included in the meta-analysis, of which 5 studies concerned motor sequence learning (total n = 186) and 15 working memory training (total n = 650). Results were pooled using an inverse variance random effects meta-analysis. The findings showed no statistically significant additional effects of tDCS over placebo on motor sequence learning outcomes. However, there was a strong trend showing that tDCS boosted working memory training, although methodological limitations and some heterogeneity were also apparent. In conclusion, the present findings do not support wide implementation of tDCS as an add-on to motor sequence training at the moment, but the promising results on cognitive training warrant further investigations.
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Affiliation(s)
- Britt Vandendoorent
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium.
| | - Evelien Nackaerts
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Demi Zoetewei
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Femke Hulzinga
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Moran Gilat
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Jean-Jacques Orban de Xivry
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Alice Nieuwboer
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
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18
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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19
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Khan AF, Adewale Q, Lin SJ, Baumeister TR, Zeighami Y, Carbonell F, Palomero-Gallagher N, Iturria-Medina Y. Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease. Nat Commun 2023; 14:6009. [PMID: 37752107 PMCID: PMC10522603 DOI: 10.1038/s41467-023-41677-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Sue-Jin Lin
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Tobias R Baumeister
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Yashar Zeighami
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen, and JARA - Translational Brain Medicine, Aachen, Germany
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada.
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20
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Liu CF, Younes L, Tong XJ, Hinkle JT, Wang M, Phatak S, Xu X, Bu X, Looi V, Bang J, Tabrizi SJ, Scahill RI, Paulsen JS, Georgiou-Karistianis N, Faria AV, Miller MI, Ratnanather JT, Ross CA. Longitudinal imaging highlights preferential basal ganglia circuit atrophy in Huntington's disease. Brain Commun 2023; 5:fcad214. [PMID: 37744022 PMCID: PMC10516592 DOI: 10.1093/braincomms/fcad214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/09/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Huntington's disease is caused by a CAG repeat expansion in the Huntingtin gene (HTT), coding for polyglutamine in the Huntingtin protein, with longer CAG repeats causing earlier age of onset. The variable 'Age' × ('CAG'-L), where 'Age' is the current age of the individual, 'CAG' is the repeat length and L is a constant (reflecting an approximation of the threshold), termed the 'CAG Age Product' (CAP) enables the consideration of many individuals with different CAG repeat expansions at the same time for analysis of any variable and graphing using the CAG Age Product score as the X axis. Structural MRI studies have showed that progressive striatal atrophy begins many years prior to the onset of diagnosable motor Huntington's disease, confirmed by longitudinal multicentre studies on three continents, including PREDICT-HD, TRACK-HD and IMAGE-HD. However, previous studies have not clarified the relationship between striatal atrophy, atrophy of other basal ganglia structures, and atrophy of other brain regions. The present study has analysed all three longitudinal datasets together using a single image segmentation algorithm and combining data from a large number of subjects across a range of CAG Age Product score. In addition, we have used a strategy of normalizing regional atrophy to atrophy of the whole brain, in order to determine which regions may undergo preferential degeneration. This made possible the detailed characterization of regional brain atrophy in relation to CAG Age Product score. There is dramatic selective atrophy of regions involved in the basal ganglia circuit-caudate, putamen, nucleus accumbens, globus pallidus and substantia nigra. Most other regions of the brain appear to have slower but steady degeneration. These results support (but certainly do not prove) the hypothesis of circuit-based spread of pathology in Huntington's disease, possibly due to spread of mutant Htt protein, though other connection-based mechanisms are possible. Therapeutic targets related to prion-like spread of pathology or other mechanisms may be suggested. In addition, they have implications for current neurosurgical therapeutic approaches, since delivery of therapeutic agents solely to the caudate and putamen may miss other structures affected early, such as nucleus accumbens and output nuclei of the striatum, the substantia nigra and the globus pallidus.
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Affiliation(s)
- Chin-Fu Liu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xiao J Tong
- Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore MD 21287, USA
| | - Jared T Hinkle
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Maggie Wang
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sanika Phatak
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xin Xu
- Division of Magnetic Resonance, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Xuan Bu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Vivian Looi
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jee Bang
- Division of Neurobiology, Department of Psychiatry, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Sarah J Tabrizi
- HD Research Centre, University College London Queen Square Institute of Neurology, UCL, London, UK
| | - Rachael I Scahill
- HD Research Centre, University College London Queen Square Institute of Neurology, UCL, London, UK
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin, Madison, WI 53705, USA
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and The Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria 3800, Australia
| | - Andreia V Faria
- Division of Magnetic Resonance, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Christopher A Ross
- Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore MD 21287, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA
- Division of Neurobiology, Department of Psychiatry, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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21
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Li J, Wang R, Mao N, Huang M, Qiu S, Wang J. Multimodal and multiscale evidence for network-based cortical thinning in major depressive disorder. Neuroimage 2023; 277:120265. [PMID: 37414234 DOI: 10.1016/j.neuroimage.2023.120265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/26/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is associated with widespread, irregular cortical thickness (CT) reductions across the brain. However, little is known regarding mechanisms that govern spatial distribution of the reductions. METHODS We combined multimodal MRI and genetic, cytoarchitectonic and chemoarchitectonic data to examine structural covariance, functional synchronization, gene co-expression, cytoarchitectonic similarity and chemoarchitectonic covariance between regions atrophied in MDD. RESULTS Regions atrophied in MDD were associated with significantly higher structural covariance, functional synchronization, gene co-expression and chemoarchitectonic covariance. These results were robust against methodological variations in brain parcellation and null model, reproducible in patients and controls, and independent of age at onset of MDD. Despite no significant differences in the cytoarchitectonic similarity, MDD-related CT reductions were susceptible to specific cytoarchitectonic class of association cortex. Further, we found that nodal shortest path lengths to disease epicenters derived from structural (right supramarginal gyrus) and chemoarchitectonic covariance (right sulcus intermedius primus) networks of healthy brains were correlated with the extent to which a region was atrophied in MDD, supporting the transneuronal spread hypothesis that regions closer to the epicenters are more susceptible to MDD. Finally, we showed that structural covariance and functional synchronization among regions atrophied in MDD were mainly related to genes enriched in metabolic and membrane-related processes, driven by genes in excitatory neurons, and associated with specific neurotransmitter transporters and receptors. CONCLUSIONS Altogether, our findings provide empirical evidence for and genetic and molecular insights into connectivity-constrained CT thinning in MDD.
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Affiliation(s)
- Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Rui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Manli Huang
- Department of Psychiatry, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China; The Key Laboratory of Mental Disorder's Management of Zhejiang Province, Hangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
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22
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Raheel K, Deegan G, Di Giulio I, Cash D, Ilic K, Gnoni V, Chaudhuri KR, Drakatos P, Moran R, Rosenzweig I. Sex differences in alpha-synucleinopathies: a systematic review. Front Neurol 2023; 14:1204104. [PMID: 37545736 PMCID: PMC10398394 DOI: 10.3389/fneur.2023.1204104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/13/2023] [Indexed: 08/08/2023] Open
Abstract
Background Past research indicates a higher prevalence, incidence, and severe clinical manifestations of alpha-synucleinopathies in men, leading to a suggestion of neuroprotective properties of female sex hormones (especially estrogen). The potential pathomechanisms of any such effect on alpha-synucleinopathies, however, are far from understood. With that aim, we undertook to systematically review, and to critically assess, contemporary evidence on sex and gender differences in alpha-synucleinopathies using a bench-to-bedside approach. Methods In this systematic review, studies investigating sex and gender differences in alpha-synucleinopathies (Rapid Eye Movement (REM) Behavior Disorder (RBD), Parkinson's Disease (PD), Dementia with Lewy Bodies (DLB), Multiple System Atrophy (MSA)) from 2012 to 2022 were identified using electronic database searches of PubMed, Embase and Ovid. Results One hundred sixty-two studies were included; 5 RBD, 6 MSA, 20 DLB and 131 PD studies. Overall, there is conclusive evidence to suggest sex-and gender-specific manifestation in demographics, biomarkers, genetics, clinical features, interventions, and quality of life in alpha-synucleinopathies. Only limited data exists on the effects of distinct sex hormones, with majority of studies concentrating on estrogen and its speculated neuroprotective effects. Conclusion Future studies disentangling the underlying sex-specific mechanisms of alpha-synucleinopathies are urgently needed in order to enable novel sex-specific therapeutics.
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Affiliation(s)
- Kausar Raheel
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Gemma Deegan
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
| | - Irene Di Giulio
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
| | - Diana Cash
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Katarina Ilic
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Valentina Gnoni
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Lecce, Italy
| | - K. Ray Chaudhuri
- Movement Disorders Unit, King’s College Hospital and Department of Clinical and Basic Neurosciences, Institute of Psychiatry, Psychology and Neuroscience and Parkinson Foundation Centre of Excellence, King’s College London, London, United Kingdom
| | - Panagis Drakatos
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
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23
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Joo Y, Lee S, Hwang J, Kim J, Cheon YH, Lee H, Kim S, Yurgelun-Todd DA, Renshaw PF, Yoon S, Lyoo IK. Differential alterations in brain structural network organization during addiction between adolescents and adults. Psychol Med 2023; 53:3805-3816. [PMID: 35440353 PMCID: PMC10317813 DOI: 10.1017/s0033291722000423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 01/06/2022] [Accepted: 02/04/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The adolescent brain may be susceptible to the influences of illicit drug use. While compensatory network reorganization is a unique developmental characteristic that may restore several brain disorders, its association with methamphetamine (MA) use-induced damage during adolescence is unclear. METHODS Using independent component (IC) analysis on structural magnetic resonance imaging data, spatially ICs described as morphometric networks were extracted to examine the effects of MA use on gray matter (GM) volumes and network module connectivity in adolescents (51 MA users v. 60 controls) and adults (54 MA users v. 60 controls). RESULTS MA use was related to significant GM volume reductions in the default mode, cognitive control, salience, limbic, sensory and visual network modules in adolescents. GM volumes were also reduced in the limbic and visual network modules of the adult MA group as compared to the adult control group. Differential patterns of structural connectivity between the basal ganglia (BG) and network modules were found between the adolescent and adult MA groups. Specifically, adult MA users exhibited significantly reduced connectivity of the BG with the default network modules compared to control adults, while adolescent MA users, despite the greater extent of network GM volume reductions, did not show alterations in network connectivity relative to control adolescents. CONCLUSIONS Our findings suggest the potential of compensatory network reorganization in adolescent brains in response to MA use. The developmental characteristic to compensate for MA-induced brain damage can be considered as an age-specific therapeutic target for adolescent MA users.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Suji Lee
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Jungyoon Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - Young-Hoon Cheon
- Department of Psychiatry, Incheon Chamsarang Hospital, Incheon, South Korea
| | - Hyangwon Lee
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - Shinhye Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - Deborah A. Yurgelun-Todd
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
- Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, VA VISN 19 Mental Illness Research, Education and Clinical Center (MIRECC), Salt Lake City, UT, USA
| | - Perry F. Renshaw
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
- Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, VA VISN 19 Mental Illness Research, Education and Clinical Center (MIRECC), Salt Lake City, UT, USA
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
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Sun C, Xu H, Zhang Y, Zhang Y. A reliable subtyping of de novo Parkinson disease: biomarkers, medication effects and longitudinal progression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083250 DOI: 10.1109/embc40787.2023.10340372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Divergent clinical symptoms and pathological progression suggest multiple subtypes of Parkinson disease (PD). Here, we proposed a reliable PD subtyping approach that quantifies the disturbance of an individual patient to the reference structural covariance networks derived from healthy controls. We revealed two subtypes of de novo PD patients by using longitudinal data from the PPMI dataset. Compared to the conventional clinical TD/PIGD phenotypes, our subtyping was highly stable in 5 years' visits. The two subtypes of PD showed significant differences in motor symptoms, medication effects, CSF biomarkers, and longitudinal progression. Moreover, patients of subtype 2 showed widespread lower cortical-to-dorsal raphe nucleus (DRN) connections and higher medication effects on motor symptoms which was regulated by 5-HT neurons in DRN. Our results suggest distinct neuropathological pathways underlying the two subtypes, such that, in contrast to the typical PD subtype, patients of subtype 2 may be affected by serotonergic modulation on dopaminergic neurons in striatum. Our study opens new avenue to precision medicine and personalized treatments in PD and may be applicable to other neurodegenerative diseases.
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25
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Knolle F, Arumugham SS, Barker RA, Chee MWL, Justicia A, Kamble N, Lee J, Liu S, Lenka A, Lewis SJG, Murray GK, Pal PK, Saini J, Szeto J, Yadav R, Zhou JH, Koch K. A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis. NPJ Parkinsons Dis 2023; 9:87. [PMID: 37291143 PMCID: PMC10250419 DOI: 10.1038/s41531-023-00522-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 05/15/2023] [Indexed: 06/10/2023] Open
Abstract
Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.
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Affiliation(s)
- Franziska Knolle
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Shyam S Arumugham
- Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Roger A Barker
- Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Azucena Justicia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- IMIM (Hospital del Mar Medical Research Institute), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Nitish Kamble
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jimmy Lee
- Research Division, Institute of Mental Health, Singapore, Singapore
- Department of Psychosis, Institute of Mental Health, Singapore, Singapore
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Siwei Liu
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Abhishek Lenka
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
- Department of Neurology, Medstar Georgetown University School of Medicine, Washington, DC, USA
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jitender Saini
- Department of Neurology, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Jennifer Szeto
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Ravi Yadav
- Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bengaluru, India
| | - Juan H Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kathrin Koch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.
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A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD. Mol Psychiatry 2023; 28:1210-1218. [PMID: 36575304 PMCID: PMC10005951 DOI: 10.1038/s41380-022-01916-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/21/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.
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27
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Chen P, Zhao K, Zhang H, Wei Y, Wang P, Wang D, Song C, Yang H, Zhang Z, Yao H, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhou B, Jiang T, Zhou Y, Lu J, Han Y, Zhang X, Liu B, Liu Y. Altered global signal topography in Alzheimer's disease. EBioMedicine 2023; 89:104455. [PMID: 36758481 PMCID: PMC9941064 DOI: 10.1016/j.ebiom.2023.104455] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/31/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI). METHODS fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics. FINDINGS Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (Padj < 0.05). Notably, topographical GS changes in these regions correlated with cognitive ability (P < 0.05). The changes in GS topography also correlated with the changes in functional network segregation (ρ = 0.5). Moreover, the genes identified based on GS topographical changes were enriched in pathways associated with AD and neurodegenerative diseases. INTERPRETATION Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD. FUNDING Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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28
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Makdissi S, Parsons BD, Di Cara F. Towards early detection of neurodegenerative diseases: A gut feeling. Front Cell Dev Biol 2023; 11:1087091. [PMID: 36824371 PMCID: PMC9941184 DOI: 10.3389/fcell.2023.1087091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/20/2023] [Indexed: 02/10/2023] Open
Abstract
The gastrointestinal tract communicates with the nervous system through a bidirectional network of signaling pathways called the gut-brain axis, which consists of multiple connections, including the enteric nervous system, the vagus nerve, the immune system, endocrine signals, the microbiota, and its metabolites. Alteration of communications in the gut-brain axis is emerging as an overlooked cause of neuroinflammation. Neuroinflammation is a common feature of the pathogenic mechanisms involved in various neurodegenerative diseases (NDs) that are incurable and debilitating conditions resulting in progressive degeneration and death of neurons, such as in Alzheimer and Parkinson diseases. NDs are a leading cause of global death and disability, and the incidences are expected to increase in the following decades if prevention strategies and successful treatment remain elusive. To date, the etiology of NDs is unclear due to the complexity of the mechanisms of diseases involving genetic and environmental factors, including diet and microbiota. Emerging evidence suggests that changes in diet, alteration of the microbiota, and deregulation of metabolism in the intestinal epithelium influence the inflammatory status of the neurons linked to disease insurgence and progression. This review will describe the leading players of the so-called diet-microbiota-gut-brain (DMGB) axis in the context of NDs. We will report recent findings from studies in model organisms such as rodents and fruit flies that support the role of diets, commensals, and intestinal epithelial functions as an overlooked primary regulator of brain health. We will finish discussing the pivotal role of metabolisms of cellular organelles such as mitochondria and peroxisomes in maintaining the DMGB axis and how alteration of the latter can be used as early disease makers and novel therapeutic targets.
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Affiliation(s)
- Stephanie Makdissi
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
- IWK Health Centre, Department of Pediatrics, Halifax, Canada
| | - Brendon D. Parsons
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
| | - Francesca Di Cara
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
- IWK Health Centre, Department of Pediatrics, Halifax, Canada
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29
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Habich A, Oltra J, Schwarz CG, Przybelski SA, Oppedal K, Inguanzo A, Blanc F, Lemstra AW, Hort J, Westman E, Lowe VJ, Boeve BF, Dierks T, Aarsland D, Kantarci K, Ferreira D. Sex differences in grey matter networks in dementia with Lewy bodies. RESEARCH SQUARE 2023:rs.3.rs-2519935. [PMID: 36778448 PMCID: PMC9915801 DOI: 10.21203/rs.3.rs-2519935/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Objectives Sex differences permeate many aspects of dementia with Lewy bodies (DLB), including epidemiology, pathogenesis, disease progression, and symptom manifestation. However, less is known about potential sex differences in patterns of neurodegeneration in DLB. Here, we test whether grey matter networks also differ between female and male DLB patients. To assess the specificity of these sex differences to DLB, we additionally investigate sex differences in healthy controls (HCs). Methods A total of 119 (68.7 ± 8.4 years) male and 45 female (69.9 ± 9.1 years) DLB patients from three European centres and the Mayo Clinic were included in this study. Additionally, we included 119 male and 45 female age-matched HCs from the Mayo Clinic. Grey matter volumes of 58 cortical, subcortical, cerebellar, and pontine brain regions derived from structural magnetic resonance images were corrected for age, intracranial volume, and centre. Sex-specific grey matter networks for DLB patients and HCs were constructed by correlating each pair of brain regions. Network properties of the correlation matrices were compared between sexes and groups. Additional analyses were conducted on W-scored data to identify DLB-specific findings. Results Networks of male HCs and male DLB patients were characterised by a lower nodal strength compared to their respective female counterparts. In comparison to female HCs, the grey matter networks of male HCs showed a higher global efficiency, modularity, and a lower number of modules. None of the global and nodal network measures showed significant sex differences in DLB. Conclusions The disappearance of sex differences in the structural grey matter networks of DLB patients compared to HCs may indicate a sex-dependent network vulnerability to the alpha-synuclein pathology in DLB. Future studies might investigate whether the differences in structural network measures are associated with differences in cognitive scores and clinical symptoms between the sexes.
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Affiliation(s)
- Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Javier Oltra
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | | | | | - Ketil Oppedal
- Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Anna Inguanzo
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Frédéric Blanc
- Day Hospital of Geriatrics, Memory Resource and Research Centre (CM2R) of Strasbourg, Department of Geriatrics, Hopitaux Universitaires de Strasbourg, Strasbourg, France
| | - Afina W Lemstra
- Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| | - Jakub Hort
- Motol University Hospital, Prague, Czech Republic
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, USA
| | | | - Thomas Dierks
- University Hospital of Psychiatry and Psychotherapy Bern, University of Bern, Bern, Switzerland
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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30
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Pletcher C, Dabbs K, Barzgari A, Pozorski V, Haebig M, Wey S, Krislov S, Theisen F, Okonkwo O, Cary P, Oh J, Illingworth C, Wakely M, Law L, Gallagher CL. Cerebral cortical thickness and cognitive decline in Parkinson's disease. Cereb Cortex Commun 2023; 4:tgac044. [PMID: 36660417 PMCID: PMC9840947 DOI: 10.1093/texcom/tgac044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/28/2022] [Accepted: 10/05/2022] [Indexed: 01/21/2023] Open
Abstract
In Parkinson's disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.
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Affiliation(s)
- Colleen Pletcher
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Amy Barzgari
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Vincent Pozorski
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Maureen Haebig
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Sasha Wey
- Medical College of Wisconsin, Milwaukee, WI, United States
| | - Stephanie Krislov
- Institute for Clinical and Translational Research, Madison, WI, United States
| | - Frances Theisen
- Cox Medical Centers, Department of Surgery, Springfield, MO, United States
| | - Ozioma Okonkwo
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Paul Cary
- Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Jennifer Oh
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Chuck Illingworth
- William S. Middleton Memorial Veterans Hospital, Madison, WI, United States,Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Michael Wakely
- Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Lena Law
- Wisconsin Alzheimer’s Disease Research Center, Madison, WI, United States
| | - Catherine L Gallagher
- Corresponding author: University of Wisconsin School of Medicine and Public Health, Chief of Neurology, William S. Middleton V.A. 2500 Overlook Terrace Dr. Madison, WI 53705-2281, United States, ;
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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32
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Liu F, Yang J, Feng M, Cui Z, He X, Zhou L, Feng J, Shen D. Does perfect filtering really guarantee perfect phase correction for diffusion MRI data? Comput Med Imaging Graph 2023; 103:102160. [PMID: 36528017 DOI: 10.1016/j.compmedimag.2022.102160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022]
Abstract
Owing to its merit of avoiding noise-floor, phase correction is recently used to reconstruct real-valued diffusion MRI data by employing an image filter to estimate the noise-free background phase. However, several studies report an unexpected signal-loss issue for their reconstruction results, with its causing reason still remaining unclear. Although phase correction has achieved promising results in mitigating the signal-loss issue via improving the employed image filter, we have observed counterintuitive results that an advanced filter generates severe artifacts in our previous work. Considering the potential issues with phase correction procedures, in this paper, we argue that even a perfect image filter is insufficient to produce perfect phase correction. To point out the reason why phase correction introduces signal-loss and address this issue, we first propose a complex polar coordinate system (CPCS) to analyze its procedures in detail; second, based on CPCS, we find that phase correction has not sufficiently utilized the background phase, and thus propose a quantitative criterion to fully exploit the background phase; eventually, we propose a phase calibration procedure to remedy current phase correction. Extensive experimental results, including those on synthetic and real diffusion MRI data, demonstrate that our proposed method significantly reduces signal-loss and also eliminates artifacts in FA maps, particularly with improved accuracy on FA.
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Affiliation(s)
- Feihong Liu
- School of Information Science and Technology, Northwest University, Xi'an, China; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Junwei Yang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Mingyue Feng
- Department of Informatics, Technische Universität München, Garching, Germany
| | - Zhiming Cui
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Xiaowei He
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China
| | - Luping Zhou
- School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.
| | - Jun Feng
- School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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Mandal AS, Brem S, Suckling J. Brain network mapping and glioma pathophysiology. Brain Commun 2023; 5:fcad040. [PMID: 36895956 PMCID: PMC9989143 DOI: 10.1093/braincomms/fcad040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/23/2022] [Accepted: 02/18/2023] [Indexed: 02/25/2023] Open
Abstract
Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.
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Affiliation(s)
- Ayan S Mandal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Steven Brem
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104, USA.,Glioblastoma Translational Center of Excellence, Abramson Cancer Center, Philadelphia, PA 19104, USA
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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34
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Filippi M, Spinelli EG, Cividini C, Ghirelli A, Basaia S, Agosta F. The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression. Expert Rev Neurother 2023; 23:59-73. [PMID: 36710600 DOI: 10.1080/14737175.2023.2174016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome. AREAS COVERED The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years. EXPERT OPINION Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.
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Affiliation(s)
- Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alma Ghirelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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35
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Li Y, Huang X, Ruan X, Duan D, Zhang Y, Yu S, Chen A, Wang Z, Zou Y, Xia M, Wei X. Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson's disease. NPJ Parkinsons Dis 2022; 8:176. [PMID: 36581626 PMCID: PMC9800563 DOI: 10.1038/s41531-022-00442-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022] Open
Abstract
Freezing of gait (FOG) greatly impacts the daily life of patients with Parkinson's disease (PD). However, predictors of FOG in early PD are limited. Moreover, recent neuroimaging evidence of cerebral morphological alterations in PD is heterogeneous. We aimed to develop a model that could predict the occurrence of FOG using machine learning, collaborating with clinical, laboratory, and cerebral structural imaging information of early drug-naïve PD and investigate alterations in cerebral morphology in early PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson's Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a 5-year follow-up period. We found that models trained with structural morphological features showed fair to good performance (accuracy range, 0.67-0.73). Performance improved when clinical and laboratory data was added (accuracy range, 0.71-0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69-0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the structural morphological features that were mainly distributed in the left cerebrum. Moreover, the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Overall, we found that T1WI morphometric markers helped predict future FOG occurrence in patients with early drug-naïve PD at the individual level. The OLF exhibits predominantly cortical expansion in early PD.
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Affiliation(s)
- Yuting Li
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China ,grid.284723.80000 0000 8877 7471Affiliated Dongguan Hospital, Southern Medical University (Dongguan People’s Hospital), Guangdong, China
| | - Xiaofei Huang
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China
| | - Xiuhang Ruan
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China
| | - Dingna Duan
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihe Zhang
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shaode Yu
- grid.443274.20000 0001 2237 1871School of Information and Communication Engineering, Communication University of China, Beijing, China
| | - Amei Chen
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China
| | - Zhaoxiu Wang
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China
| | - Yujian Zou
- grid.284723.80000 0000 8877 7471Affiliated Dongguan Hospital, Southern Medical University (Dongguan People’s Hospital), Guangdong, China
| | - Mingrui Xia
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xinhua Wei
- grid.79703.3a0000 0004 1764 3838Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangdong, China
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36
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Guan X, Guo T, Zhou C, Wu J, Zeng Q, Li K, Luo X, Bai X, Wu H, Gao T, Gu L, Liu X, Cao Z, Wen J, Chen J, Wei H, Zhang Y, Liu C, Song Z, Yan Y, Pu J, Zhang B, Xu X, Zhang M. Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study. Neuroimage 2022; 264:119683. [PMID: 36243270 DOI: 10.1016/j.neuroimage.2022.119683] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Qingze Zeng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Kaicheng Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xiao Luo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Zhengye Cao
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaping Yan
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Walton E, Bernardoni F, Batury VL, Bahnsen K, Larivière S, Abbate-Daga G, Andres-Perpiña S, Bang L, Bischoff-Grethe A, Brooks SJ, Campbell IC, Cascino G, Castro-Fornieles J, Collantoni E, D'Agata F, Dahmen B, Danner UN, Favaro A, Feusner JD, Frank GKW, Friederich HC, Graner JL, Herpertz-Dahlmann B, Hess A, Horndasch S, Kaplan AS, Kaufmann LK, Kaye WH, Khalsa SS, LaBar KS, Lavagnino L, Lazaro L, Manara R, Miles AE, Milos GF, Monteleone AM, Monteleone P, Mwangi B, O'Daly O, Pariente J, Roesch J, Schmidt UH, Seitz J, Shott ME, Simon JJ, Smeets PAM, Tamnes CK, Tenconi E, Thomopoulos SI, van Elburg AA, Voineskos AN, von Polier GG, Wierenga CE, Zucker NL, Jahanshad N, King JA, Thompson PM, Berner LA, Ehrlich S. Brain Structure in Acutely Underweight and Partially Weight-Restored Individuals With Anorexia Nervosa: A Coordinated Analysis by the ENIGMA Eating Disorders Working Group. Biol Psychiatry 2022; 92:730-738. [PMID: 36031441 DOI: 10.1016/j.biopsych.2022.04.022] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/01/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in gray matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy control subjects. Recent weight regain before scanning may explain some of this heterogeneity. To clarify the extent, magnitude, and dependencies of gray matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. METHODS We analyzed T1-weighted structural magnetic resonance imaging scans assessed with standardized methods from 685 female patients with AN and 963 female healthy control subjects across 22 sites worldwide. In addition to a case-control comparison, we conducted a 3-group analysis comparing healthy control subjects with acutely underweight AN patients (n = 466) and partially weight-restored patients in treatment (n = 251). RESULTS In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area were sizable (Cohen's d up to 0.95), widespread, and colocalized with hub regions. Highlighting the effects of undernutrition, these deficits were associated with lower body mass index in the AN sample and were less pronounced in partially weight-restored patients. CONCLUSIONS The effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.
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Affiliation(s)
- Esther Walton
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Fabio Bernardoni
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Victoria-Luise Batury
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Klaas Bahnsen
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec
| | - Giovanni Abbate-Daga
- Eating Disorders Center for Treatment and Research, University of Turin, Turin, Italy
| | - Susana Andres-Perpiña
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Lasse Bang
- Norwegian Institute of Public Health, Oslo; Regional Department for Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Amanda Bischoff-Grethe
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Samantha J Brooks
- School of Psychology, Faculty of Health Sciences, Liverpool John Moores University, Liverpool, United Kingdom; Department of Neuroscience, Uppsala University, Sweden
| | - Iain C Campbell
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Eating Disorders Unit, Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Giammarco Cascino
- Section of Neurosciences, Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Josefina Castro-Fornieles
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | | | | | - Brigitte Dahmen
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Unna N Danner
- Altrecht Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands; Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
| | - Angela Favaro
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Jamie D Feusner
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, California
| | - Guido K W Frank
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Hans-Christoph Friederich
- Centre for Psychosocial Medicine, Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
| | - John L Graner
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina
| | - Beate Herpertz-Dahlmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Andreas Hess
- Institute for Pharmacology and Toxicology, University Erlangen-Nuremberg, Erlangen, Germany
| | - Stefanie Horndasch
- Department of Child and Adolescent Psychiatry, University Clinic Erlangen, Erlangen, Germany
| | - Allan S Kaplan
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Lisa-Katrin Kaufmann
- Department of Consultation-Liaison Psychiatry and Psychosomatics, University Hospital Zurich, University of Zurich; Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Walter H Kaye
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, University of Tulsa, Tulsa, Oklahoma; Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma
| | - Kevin S LaBar
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina
| | - Luca Lavagnino
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston Texas
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Renzo Manara
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Amy E Miles
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Gabriella F Milos
- Department of Consultation-Liaison Psychiatry and Psychosomatics, University Hospital Zurich, University of Zurich
| | | | - Palmiero Monteleone
- Section of Neurosciences, Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston Texas
| | - Owen O'Daly
- Centre for Neuroimaging Studies, King's College London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jose Pariente
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Julie Roesch
- Department of Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - Ulrike H Schmidt
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Eating Disorders Unit, Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Jochen Seitz
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Megan E Shott
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Joe J Simon
- Centre for Psychosocial Medicine, Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
| | - Paul A M Smeets
- UMC Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Elena Tenconi
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Annemarie A van Elburg
- Altrecht Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands; Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Georg G von Polier
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; Institute for Neuroscience and Medicine: Brain and Behaviour, Forschungszentrum Jülich, Jülich, Germany; Department of Child and Adolescent Psychiatry, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Christina E Wierenga
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Nancy L Zucker
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Joseph A King
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Laura A Berner
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
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Du T, Yuan T, Zhu G, Ma R, Zhang X, Chen Y, Zhang J. The effect of age and disease duration on the efficacy of subthalamic nuclei deep brain stimulation in Parkinson's disease patients. CNS Neurosci Ther 2022; 28:2163-2171. [PMID: 36069345 PMCID: PMC9627397 DOI: 10.1111/cns.13958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Previous studies have reported the effects of age and disease duration on the efficacy of subthalamic nuclei deep brain stimulation (STN-DBS) of Parkinson's disease (PD) patients. However, available data involving these issues are not consistent. In particular, the effect of age and disease duration on the initial efficacy of STN-DBS has not been established. METHODS A total of 51 patients with PD treated with bilateral STN-DBS were involved in the present study. They received clinical symptom evaluation during the preoperative, initial, and chronic stages of surgery. The correlations between age when undergoing surgery/age at disease onset/disease duration and outcomes of STN-DBS were measured. RESULTS The preoperative levodopa response was negatively associated with age. During the initial stage, the age when undergoing surgery and age at disease onset were negatively correlated with the effect on bradykinesia, with better symptom control of general symptoms in long-term disease patients. Similarly, patients with an early time of surgery and disease onset and long-term disease duration showed better control of bradykinesia and axial symptoms at the chronic stage. Furthermore, a long-term disease duration and early disease onset benefited from an increase of therapeutic efficacy in general, rigid, and axial symptoms with STN-DBS after a long period. Nevertheless, patients with late disease onset achieved a better relief of stigma. CONCLUSION Age and disease durations played a unique role in controlling the symptoms of PD patients treated with STN-DBS. These results may contribute to patient selection and adjustments of expectations of surgery, based on the age, disease duration, and different symptoms.
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Affiliation(s)
- Tingting Du
- Department of Functional NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
| | - Tianshuo Yuan
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Ruoyu Ma
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Xin Zhang
- Department of Functional NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina
| | - Yingchuan Chen
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Jianguo Zhang
- Department of Functional NeurosurgeryBeijing Neurosurgical Institute, Capital Medical UniversityBeijingChina,Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina,Beijing Key Laboratory of NeurostimulationBeijingChina
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Wang L, Wu P, Brown P, Zhang W, Liu F, Han Y, Zuo CT, Cheng W, Feng J. Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease. Neurology 2022; 99:e977-e988. [PMID: 35667838 PMCID: PMC7613818 DOI: 10.1212/wnl.0000000000200814] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores. METHODS This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline. RESULTS A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA). DISCUSSION Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.
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Affiliation(s)
- Linbo Wang
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Ping Wu
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Peter Brown
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Wei Zhang
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Fengtao Liu
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Yan Han
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Chuan-Tao Zuo
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Wei Cheng
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China.
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Zheng JH, Sun WH, Ma JJ, Wang ZD, Chang QQ, Dong LR, Shi XX, Li MJ, Gu Q, Chen SY. Structural and functional abnormalities in Parkinson's disease based on voxel-based morphometry and resting-state functional magnetic resonance imaging. Neurosci Lett 2022; 788:136835. [PMID: 35963477 DOI: 10.1016/j.neulet.2022.136835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 07/25/2022] [Accepted: 08/07/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To explore differences in gray matter volume (GMV) and white matter volume (WMV) between patients with Parkinson's disease (PD) and healthy controls, and to examine whether the structural abnormalities correlate with functional abnormalities. METHODS T1-weighted magnetic resonance imaging and resting-state functional magnetic resonance imaging (fMRI) were performed on 180 patients with PD and 58 age- and sex-matched healthy controls. We used voxel-based morphometry (VBM) to compare GMV and WMV between groups, and resting-state fMRI to compare amplitudes of low-frequency fluctuations (ALFF) in the structurally abnormal brain regions. RESULTS Structural neuroimaging showed smaller whole-brain GMV, but not WMV, in patients. Furthermore, VBM revealed smaller GMV in the right superior temporal gyrus (STG) and left frontotemporal space in patients, after correction for multiple comparisons. Patients also showed significantly higher ALFF in the right STG. GMV in the right STG and left frontotemporal space in patients correlated negatively with age and scores on Part III of the Movement Disorder Society Unified Parkinson's Disease Rating Scale, but not with PD duration. CONCLUSIONS Structural atrophy in the frontotemporal lobe may be a useful imaging biomarker in PD, such as for detecting disease progression. Furthermore, this structural atrophy appears to correlate with enhanced spontaneous brain activity. This study associates particular structural and functional abnormalities with PD neuropathology.
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Affiliation(s)
- Jin Hua Zheng
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province, China
| | - Wen Hua Sun
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Jian Jun Ma
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province, China.
| | - Zhi Dong Wang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Qing Qing Chang
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Lin Rui Dong
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Xiao Xue Shi
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Ming Jian Li
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province, China
| | - Qi Gu
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province, China
| | - Si Yuan Chen
- Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Zhengzhou University, Zhengzhou, Henan Province, China; Department of Neurology, People's Hospital of Henan University, Zhengzhou, Henan Province, China
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41
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Hansen JY, Shafiei G, Vogel JW, Smart K, Bearden CE, Hoogman M, Franke B, van Rooij D, Buitelaar J, McDonald CR, Sisodiya SM, Schmaal L, Veltman DJ, van den Heuvel OA, Stein DJ, van Erp TGM, Ching CRK, Andreassen OA, Hajek T, Opel N, Modinos G, Aleman A, van der Werf Y, Jahanshad N, Thomopoulos SI, Thompson PM, Carson RE, Dagher A, Misic B. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. Nat Commun 2022; 13:4682. [PMID: 35948562 PMCID: PMC9365855 DOI: 10.1038/s41467-022-32420-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
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Affiliation(s)
- Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jacob W Vogel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Smart
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Martine Hoogman
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Barbara Franke
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Daan van Rooij
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, & Center for the Neurobiology of Leaning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, USA
| | - Christopher R K Ching
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Nils Opel
- Institute of Translational Psychiatry, University of Münster, Münster, Germany & Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Gemma Modinos
- Department of Psychosis Studies & MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, Groningen, The Netherlands
| | - Ysbrand van der Werf
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Neda Jahanshad
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Sophia I Thomopoulos
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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42
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Banwinkler M, Dzialas V, Hoenig MC, van Eimeren T. Gray Matter Volume Loss in Proposed Brain-First and Body-First Parkinson's Disease Subtypes. Mov Disord 2022; 37:2066-2074. [PMID: 35943058 DOI: 10.1002/mds.29172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/24/2022] [Accepted: 07/10/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND α-Synuclein pathology is associated with neuronal degeneration in Parkinson's disease (PD) and considered to sequentially spread across the brain (Braak stages). According to a new hypothesis of distinct α-synuclein spreading directions based on the initial site of pathology, the "brain-first" spreading subtype would be associated with a more asymmetric cerebral and nigrostriatal pathology than the "body-first" subtype. OBJECTIVE Here, we tested if proposed markers of brain-first PD (ie, higher dopamine transporter [DaT] asymmetry; absence of rapid eye movement sleep behavior disorder [RBD]) are associated with a greater or more asymmetric reduction in gray matter volume (GMV) in comparison to body-first PD. METHODS Data of 255 de novo PD patients and 110 healthy controls (HCs) were retrieved from the Parkinson's Progression Markers Initiative. Structural magnetic resonance images were preprocessed, and GMVs and their hemispherical asymmetry were obtained for each of the neuropathologically defined Braak stages. Group and correlation comparisons were performed to assess differences in GMV and GMV asymmetry between PD subtypes. RESULTS PD patients demonstrated significantly smaller bilateral GMVs compared to HCs, in a pattern denoting stage-dependent disease-related brain atrophy. However, the degree of putaminal DaT asymmetry was not associated with reduced GMV or higher GMV asymmetry. Furthermore, RBD-negative and RBD-positive patients did not demonstrate a significant difference in GMV or GMV asymmetry. CONCLUSIONS Our findings suggest that putative brain-first and body-first patients do not present diverging brain atrophy patterns. Although certainly not disproving the brain-first/body-first spreading hypothesis, this study fails to provide evidence in support of it. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Magdalena Banwinkler
- Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, University of Cologne, Cologne, Germany
| | - Verena Dzialas
- Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, University of Cologne, Cologne, Germany.,Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | | | - Merle C Hoenig
- Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, University of Cologne, Cologne, Germany.,Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Research Center Juelich, Juelich, Germany
| | - Thilo van Eimeren
- Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, University of Cologne, Cologne, Germany.,Faculty of Medicine and University Hospital Cologne, Department of Neurology, University of Cologne, Cologne, Germany
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43
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Steidel K, Ruppert MC, Greuel A, Tahmasian M, Maier F, Hammes J, van Eimeren T, Timmermann L, Tittgemeyer M, Drzezga A, Pedrosa DJ, Eggers C. Longitudinal trimodal imaging of midbrain-associated network degeneration in Parkinson's disease. NPJ Parkinsons Dis 2022; 8:79. [PMID: 35732679 PMCID: PMC9218128 DOI: 10.1038/s41531-022-00341-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 05/24/2022] [Indexed: 11/20/2022] Open
Abstract
The prevailing network perspective of Parkinson’s disease (PD) emerges not least from the ascending neuropathology traceable in histological studies. However, whether longitudinal in vivo correlates of network degeneration in PD can be observed remains unresolved. Here, we applied a trimodal imaging protocol combining 18F-fluorodeoxyglucose (FDG)- and 18F-fluoro-L-Dopa- (FDOPA)-PET with resting-state functional MRI to assess longitudinal changes in midbrain metabolism, striatal dopamine depletion and striatocortical dysconnectivity in 17 well-characterized PD patients. Whole-brain (un)paired-t-tests with focus on midbrain or striatum were performed between visits and in relation to 14 healthy controls (HC) in PET modalities. Resulting clusters of FDOPA-PET comparisons provided volumes for seed-based functional connectivity (FC) analyses between visits and in relation to HC. FDG metabolism in the left midbrain decreased compared to baseline along with caudatal FDOPA-uptake. This caudate cluster exhibited a longitudinal FC decrease to sensorimotor and frontal areas. Compared to healthy subjects, dopamine-depleted putamina indicated stronger decline in striatocortical FC at follow-up with respect to baseline. Increasing nigrostriatal deficits and striatocortical decoupling were associated with deterioration in motor scores between visits in repeated-measures correlations. In summary, our results demonstrate the feasibility of in-vivo tracking of progressive network degeneration using a multimodal imaging approach. Specifically, our data suggest advancing striatal and widespread striatocortical dysfunction via an anterior-posterior gradient originating from a hypometabolic midbrain cluster within a well-characterized and only mild to moderately affected PD cohort during a relatively short period.
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Affiliation(s)
- Kenan Steidel
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.
| | - Marina C Ruppert
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Andrea Greuel
- Department of Neurology, University Hospital of Marburg, Marburg, Germany
| | - Masoud Tahmasian
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Franziska Maier
- Department of Psychiatry, University Hospital Cologne, Medical Faculty, Cologne, Germany
| | - Jochen Hammes
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany
| | - Thilo van Eimeren
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany.,Department of Neurology, Medical Faculty and University Hospital Cologne, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany.,Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Alexander Drzezga
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty University Hospital Cologne, Cologne, Germany.,Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Jülich, Germany
| | - David J Pedrosa
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior-CMBB, Universities Marburg and Gießen, Marburg, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Marburg, Marburg, Germany.,Department of Neurology, Knappschaftskrankenhaus Bottrop, Bottrop, Germany
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44
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Cerebral metabolic pattern associated with progressive parkinsonism in non-human primates reveals early cortical hypometabolism. Neurobiol Dis 2022; 167:105669. [DOI: 10.1016/j.nbd.2022.105669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/07/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022] Open
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45
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Rahayel S, Tremblay C, Vo A, Zheng YQ, Lehéricy S, Arnulf I, Vidailhet M, Corvol JC, Gagnon JF, Postuma RB, Montplaisir J, Lewis S, Matar E, Ehgoetz Martens K, Borghammer P, Knudsen K, Hansen A, Monchi O, Misic B, Dagher A. Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression. Brain 2022; 145:3162-3178. [PMID: 35594873 DOI: 10.1093/brain/awac187] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
Isolated REM sleep behaviour disorder (iRBD) is a synucleinopathy characterized by abnormal behaviours and vocalizations during REM sleep. Most iRBD patients develop dementia with Lewy bodies, Parkinson's disease, or multiple system atrophy over time. Patients with iRBD exhibit brain atrophy patterns that are reminiscent of those observed in overt synucleinopathies. However, the mechanisms linking brain atrophy to the underlying alpha-synuclein pathophysiology are poorly understood. Our objective was to investigate how the prion-like and regional vulnerability hypotheses of alpha-synuclein might explain brain atrophy in iRBD. Using a multicentric cohort of 182 polysomnography-confirmed iRBD patients who underwent T1-weighted MRI, we performed vertex-based cortical surface and deformation-based morphometry analyses to quantify brain atrophy in patients (67.8 years, 84% men) and 261 healthy controls (66.2 years, 75%) and investigated the morphological correlates of motor and cognitive functioning in iRBD. Next, we applied the agent-based Susceptible-Infected-Removed model (i.e., a computational model that simulates in silico the spread of pathologic alpha-synuclein based on structural connectivity and gene expression) and tested if it recreated atrophy in iRBD by statistically comparing simulated regional brain atrophy to the atrophy observed in patients. The impact of SNCA and GBA gene expression and brain connectivity was then evaluated by comparing the model fit to the one obtained in null models where either gene expression or connectivity was randomized. The results showed that iRBD patients present with cortical thinning and tissue deformation, which correlated with motor and cognitive functioning. Next, we found that the computational model recreated cortical thinning (r = 0.51, p = 0.0007) and tissue deformation (r = 0.52, p = 0.0005) in patients, and that the connectome's architecture along with SNCA and GBA gene expression contributed to shaping atrophy in iRBD. We further demonstrated that the full agent-based model performed better than network measures or gene expression alone in recreating the atrophy pattern in iRBD. In summary, atrophy in iRBD is extensive, correlates with motor and cognitive function, and can be recreated using the dynamics of agent-based modelling, structural connectivity, and gene expression. These findings support the concepts that both prion-like spread and regional susceptibility account for the atrophy observed in prodromal synucleinopathies. Therefore, the agent-based Susceptible-Infected-Removed model may be a useful tool for testing hypotheses underlying neurodegenerative diseases and new therapies aimed at slowing or stopping the spread of alpha-synuclein pathology.
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Affiliation(s)
- Shady Rahayel
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal H3A 2B4, Canada.,Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal H4J 1C5, Montreal, Canada
| | - Christina Tremblay
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal H3A 2B4, Canada
| | - Andrew Vo
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal H3A 2B4, Canada
| | - Ying-Qiu Zheng
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom
| | - Stéphane Lehéricy
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris 75013, France
| | - Isabelle Arnulf
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris 75013, France
| | - Marie Vidailhet
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris 75013, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris 75013, France
| | | | - Jean-François Gagnon
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal H4J 1C5, Montreal, Canada.,Department of Psychology, Université du Québec à Montréal, Montreal H2X 3P2, Canada.,Research Centre, Institut universitaire de gériatrie de Montréal, Montreal H3W 1W5, Canada
| | - Ronald B Postuma
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal H4J 1C5, Montreal, Canada.,Department of Neurology, Montreal General Hospital, Montreal H3G 1A4, Canada
| | - Jacques Montplaisir
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal H4J 1C5, Montreal, Canada.,Department of Psychiatry, Université de Montréal, Montreal H3 T 1J4, Canada
| | - Simon Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Camperdown NSW 2050, Australia
| | - Elie Matar
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Camperdown NSW 2050, Australia
| | - Kaylena Ehgoetz Martens
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Camperdown NSW 2050, Australia.,Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo N2L 3G1, Canada
| | - Per Borghammer
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus DK-8200, Denmark
| | - Karoline Knudsen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus DK-8200, Denmark
| | - Allan Hansen
- Department of Nuclear Medicine and PET, Aarhus University Hospital, Aarhus DK-8200, Denmark
| | - Oury Monchi
- Research Centre, Institut universitaire de gériatrie de Montréal, Montreal H3W 1W5, Canada.,Departments of Clinical Neurosciences, Radiology, and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 4N1, Canada
| | - Bratislav Misic
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal H3A 2B4, Canada
| | - Alain Dagher
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal H3A 2B4, Canada
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Cavanagh JF, Ryman S, Richardson SP. Cognitive control in Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2022; 269:137-152. [PMID: 35248192 DOI: 10.1016/bs.pbr.2022.01.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Cognitive control is the ability to act according to plan. Problems with cognitive control are a primary symptom and a major decrement of quality of life in Parkinson's disease (PD). Individuals with PD have problems with seemingly different controlled processes (e.g., task switching, impulsivity, gait disturbance, apathetic motivation). We review how these varied processes all rely upon disease-related alteration of common neural substrates, particularly due to dopaminergic imbalance. A comprehensive understanding of the neural systems underlying cognitive control will hopefully lead to more concise and reliable explanations of distributed deficits. However, high levels of clinical heterogeneity and medication-invariant control deficiencies suggest the need for increasingly detailed elaboration of the neural systems underlying control in PD.
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Affiliation(s)
- James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States.
| | - Sephira Ryman
- Mind Research Network, Albuquerque, NM, United States
| | - Sarah Pirio Richardson
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States; Neurology Service, New Mexico Veterans Affairs Healthcare System, Albuquerque, NM, United States
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47
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Manera AL, Dadar M, Collins DL, Ducharme S. Ventricular features as reliable differentiators between bvFTD and other dementias. Neuroimage Clin 2022; 33:102947. [PMID: 35134704 PMCID: PMC8856914 DOI: 10.1016/j.nicl.2022.102947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 11/24/2021] [Accepted: 01/19/2022] [Indexed: 11/28/2022]
Abstract
Our results showed a consistent pattern of ventricle enlargement in the bvFTD patients, particularly in the anterior parts of the frontal and temporal horns of the lateral ventricles. The estimation of the proposed ventricular anteroposterior ratio (APR) resulted in statistically significant difference compared to all other groups. Our study proposes an easy to obtain and generalizable ventricle-based feature (APR) from T1-weighted structural MRI (routinely acquired and available in the clinic) that can be used not only to differentiate bvFTD from normal subjects, but also from other FTD variants (SV and PNFA), MCI, and AD patients. We have made our ventricle feature estimation and bvFTD diagnosis tool (VentRa) publicly available, allowing application of our model in other studies. If validated in a prospective study, VentRa has the potential to aid bvFTD diagnosis, particularly in settings where access to specialized FTD care is limited.
Introduction Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. Methods Using 678 subjects −69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer’s Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. Results Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left–right lateral ventricle ratios), and 76% accuracy using only the single APR feature. Discussion Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.
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Affiliation(s)
- Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada.
| | - Mahsa Dadar
- Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada; Douglas Mental Health University Institute, Verdun, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec (QC), Canada; Department of Psychiatry, Douglas Mental Health University Health Centre, McGill University, Montreal, Quebec (QC), Canada
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Pieperhoff P, Südmeyer M, Dinkelbach L, Hartmann CJ, Ferrea S, Moldovan AS, Minnerop M, Diaz-Pier S, Schnitzler A, Amunts K. Regional changes of brain structure during progression of idiopathic Parkinson’s disease – a longitudinal study using deformation based morphometry. Cortex 2022; 151:188-210. [DOI: 10.1016/j.cortex.2022.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 02/04/2022] [Accepted: 03/12/2022] [Indexed: 12/14/2022]
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Shafiei G, Bazinet V, Dadar M, Manera AL, Collins DL, Dagher A, Borroni B, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Santana I, Butler C, Gerhard A, Danek A, Levin J, Otto M, Sorbi S, Jiskoot LC, Seelaar H, van Swieten JC, Rohrer JD, Misic B, Ducharme S. Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia. Brain 2022; 146:321-336. [PMID: 35188955 PMCID: PMC9825569 DOI: 10.1093/brain/awac069] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/14/2021] [Accepted: 01/30/2022] [Indexed: 01/13/2023] Open
Abstract
Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.
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Affiliation(s)
| | | | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Radiology and Nuclear Medicine, Laval University, Quebec City, QC, Canada
| | - Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain,Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Quebec, QC, Canada
| | - Caroline Graff
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden,Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany,Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Daniela Galimberti
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy,Department of Biomedical, Surgical and Dental Sciences, University of Milan, Dino Ferrari Center, Milan, Italy
| | - James B Rowe
- University of Cambridge, Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium,Neurology Service, University Hospitals Leuven, Leuven, Belgium,Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
| | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Chris Butler
- Department of Clinical Neurology, University of Oxford, Oxford, UK,Department of Brain Sciences, Imperial College London, London, UK
| | - Alex Gerhard
- Division of Neuroscience and Experimental Psychology, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK,Department of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Duisburg and Essen, Germany
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Clinical Research Unit, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy,IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Lize C Jiskoot
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK
| | - Bratislav Misic
- Correspondence to: Bratislav Misic 3801 Rue University Webster 211, Montreal QC H3A 2B4, Canada E-mail:
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Coundouris SP, Henry JD, Lehn AC. Moving beyond emotions in Parkinson's disease. BRITISH JOURNAL OF CLINICAL PSYCHOLOGY 2022; 61:647-665. [PMID: 35048398 DOI: 10.1111/bjc.12354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/09/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Emotion recognition is a fundamental neurocognitive capacity that is a critical predictor of interpersonal function and, in turn, mental health. Although people with Parkinson's disease (PD) often exhibit difficulties recognizing emotions, almost all studies to date have focused on basic emotions (happiness, sadness, anger, surprise, fear, and disgust), with little consideration of how more cognitively complex self-conscious emotions such as contempt, embarrassment, and pride might also be affected. Further, the few studies that have considered self-conscious emotions have relied on high intensity, static stimuli. The aim of the present study was to therefore provide the first examination of how self-conscious emotion recognition is affected by PD using a dynamic, dual-intensity measure that more closely captures how emotion recognition judgements are made in daily life. METHOD People with PD (n = 42) and neurotypical controls (n = 42) completed a validated measure of self-conscious facial emotion recognition. For comparative purposes, in addition to a broader clinical test battery, both groups also completed a traditional static emotion recognition measure and a measure of self-conscious emotional experience. RESULTS Relative to controls, the PD group did not differ in their capacity to recognize basic emotions but were impaired in their recognition of self-conscious emotions. These difficulties were associated with elevated negative affect and poorer subjective well-being. CONCLUSIONS Difficulties recognizing self-conscious emotions may be more problematic for people with PD than difficulties recognizing basic ones, with implications for interventions focused on helping people with this disorder develop and maintain strong social networks. PRACTITIONER POINTS This is the first direct investigation into how the recognition of self-conscious emotion is affected in Parkinson's disease using dynamic, dual-intensity stimuli, thus providing an important extension to prior literature that has focused solely on basic emotion recognition and/or relied on static, high-intensity stimuli. Results revealed preserved basic facial emotional recognition coexisting with impairment in all three self-conscious emotions assessed, therefore suggesting that the latter stimuli type may function as a more sensitive indicator of Parkinson's disease-related social cognitive impairment. Problems with self-conscious emotion recognition in people with Parkinson's disease were associated with poorer broader subjective well-being and increased negative affect. This aligns with the broader literature linking interpersonal difficulties with poorer clinical outcomes in this cohort.
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Affiliation(s)
- Sarah P Coundouris
- School of Psychology, The University of Queensland, Brisbane, Queensland, Australia
| | - Julie D Henry
- School of Psychology, The University of Queensland, Brisbane, Queensland, Australia
| | - Alexander C Lehn
- Department of Neurology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.,The University of Queensland Princess Alexandra Hospital Clinical School, Woolloongabba, Queensland, Australia
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