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Wertman E. Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology. J Clin Med 2024; 13:4202. [PMID: 39064242 PMCID: PMC11277671 DOI: 10.3390/jcm13144202] [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: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024] Open
Abstract
Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.
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Affiliation(s)
- Eli Wertman
- Department of Neurology, Hadassah University Hospital, The Hebrew University, Jerusalem 9190500, Israel;
- Section of Neuropsychology, Department of Psychology, The Hebrew University, Jerusalem 9190500, Israel
- Or’ad: Organization for Cognitive and Behavioral Changes in the Elderly, Jerusalem 9458118, Israel
- Merhav Neuropsychogeriatric Clinics, Nehalim 4995000, Israel
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Yue WL, Ng KK, Liu S, Qian X, Chong JSX, Koh AJ, Ong MQW, Ting SKS, Ng ASL, Kandiah N, Yeo BTT, Zhou JH. Differential spatial working memory-related functional network reconfiguration in young and older adults. Netw Neurosci 2024; 8:395-417. [PMID: 38952809 PMCID: PMC11142455 DOI: 10.1162/netn_a_00358] [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: 09/21/2023] [Accepted: 01/05/2024] [Indexed: 07/03/2024] Open
Abstract
Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.
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Affiliation(s)
- Wan Lin Yue
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Siwei Liu
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Xing Qian
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Joanna Su Xian Chong
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Amelia Jialing Koh
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | - Marcus Qin Wen Ong
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
| | | | | | - Nagaendran Kandiah
- National Neuroscience Institute, Singapore
- Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore
| | - B. T. Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore
- Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore
- Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore
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3
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Jenkins LM, Heywood A, Gupta S, Kouchakidivkolaei M, Sridhar J, Rogalski E, Weintraub S, Popuri K, Rosen H, Wang L. Disinhibition in dementia related to reduced morphometric similarity of cognitive control network. Brain Commun 2024; 6:fcae124. [PMID: 38665960 PMCID: PMC11044061 DOI: 10.1093/braincomms/fcae124] [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: 07/25/2023] [Revised: 03/04/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Disinhibition is one of the most distressing and difficult to treat neuropsychiatric symptoms of dementia. It involves socially inappropriate behaviours, such as hypersexual comments, inappropriate approaching of strangers and excessive jocularity. Disinhibition occurs in multiple dementia syndromes, including behavioural variant frontotemporal dementia, and dementia of the Alzheimer's type. Morphometric similarity networks are a relatively new method for examining brain structure and can be used to calculate measures of network integrity on large scale brain networks and subnetworks such as the salience network and cognitive control network. In a cross-sectional study, we calculated morphometric similarity networks to determine whether disinhibition in behavioural variant frontotemporal dementia (n = 75) and dementia of the Alzheimer's type (n = 111) was associated with reduced integrity of these networks independent of diagnosis. We found that presence of disinhibition, measured by the Neuropsychiatric Inventory Questionnaire, was associated with reduced global efficiency of the cognitive control network in both dementia of the Alzheimer's type and behavioural variant frontotemporal dementia. Future research should replicate this transdiagnostic finding in other dementia diagnoses and imaging modalities, and investigate the potential for intervention at the level of the cognitive control network to target disinhibition.
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Affiliation(s)
- Lisanne M Jenkins
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Ashley Heywood
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Sonya Gupta
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
| | | | - Jaiashre Sridhar
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Emily Rogalski
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Sandra Weintraub
- Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL 60611, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University, Chicago, IL 60611, USA
| | - Karteek Popuri
- Computer Science, Memorial University of Newfoundland, St. Johns, NL A1C 5S7, Canada
| | - Howard Rosen
- Neurology, University of California, San Francisco, CA 94143, USA
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State University, Columbus, OH 43210, USA
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4
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Stocks J, Gibson E, Popuri K, Beg MF, Rosen H, Wang L. Spatial and Temporal Relationships Between Atrophy and Hypometabolism in Behavioral-Variant Frontotemporal Dementia. Alzheimer Dis Assoc Disord 2024; 38:112-119. [PMID: 38812447 PMCID: PMC11141524 DOI: 10.1097/wad.0000000000000611] [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: 09/18/2023] [Accepted: 02/07/2024] [Indexed: 05/31/2024]
Abstract
PURPOSE Individuals with behavioral-variant frontotemporal dementia (bvFTD) show changes in brain structure as assessed by MRI and brain function assessed by 18FDG-PET hypometabolism. However, current understanding of the spatial and temporal interplay between these measures remains limited. METHODS Here, we examined longitudinal atrophy and hypometabolism relationships in 15 bvFTD subjects with 2 to 4 follow-up MRI and PET scans (56 visits total). Subject-specific slopes of atrophy and hypometabolism over time were extracted across brain regions and correlated with baseline measures both locally, via Pearson correlations, and nonlocally, via sparse canonical correlation analyses (SCCA). RESULTS Notably, we identified a robust link between initial hypometabolism and subsequent cortical atrophy rate changes in bvFTD subjects. Network-level exploration unveiled alignment between baseline hypometabolism and ensuing atrophy rates in the dorsal attention, language, and default mode networks. SCCA identified 2 significant and highly localized components depicting the connection between baseline hypometabolism and atrophy slope over time. The first centered around bilateral orbitofrontal, frontopolar, and medial prefrontal lobes, whereas the second concentrated in the left temporal lobe and precuneus. CONCLUSIONS This study highlights 18FDG-PET as a dependable predictor of forthcoming atrophy in spatially adjacent brain regions for individuals with bvFTD.
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Affiliation(s)
- Jane Stocks
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 60611
| | - Erin Gibson
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada, M4N 3M5
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada, V5A1S6
- Memorial University of Newfoundland, Department of Computer Science, St. John’s, NL, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada, V5A1S6
| | - Howard Rosen
- School of Medicine, University of California, San Francisco, USA, 94143
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA 60611
- Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA 43210
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Brooks SJ, Jones VO, Wang H, Deng C, Golding SGH, Lim J, Gao J, Daoutidis P, Stamoulis C. Community detection in the human connectome: Method types, differences and their impact on inference. Hum Brain Mapp 2024; 45:e26669. [PMID: 38553865 PMCID: PMC10980844 DOI: 10.1002/hbm.26669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.
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Affiliation(s)
- Skylar J. Brooks
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- University of California BerkeleyHelen Wills Neuroscience InstituteBerkeleyCaliforniaUSA
| | - Victoria O. Jones
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Haotian Wang
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Chengyuan Deng
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | | | - Jethro Lim
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
| | - Jie Gao
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Prodromos Daoutidis
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Catherine Stamoulis
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- Harvard Medical SchoolDepartment of PediatricsBostonMassachusettsUSA
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Chan DC, Kim C, Kang RY, Kuhn MK, Beidler LM, Zhang N, Proctor EA. Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585383. [PMID: 38559177 PMCID: PMC10979954 DOI: 10.1101/2024.03.17.585383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.
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Affiliation(s)
- Dennis C Chan
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - ChaeMin Kim
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Rachel Y Kang
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Madison K Kuhn
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lynne M Beidler
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - Elizabeth A Proctor
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA, USA
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7
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Chong JSX, Tan YJ, Koh AJ, Ting SKS, Kandiah N, Ng ASL, Zhou JH. Plasma Neurofilament Light Relates to Divergent Default and Salience Network Connectivity in Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia. J Alzheimers Dis 2024; 99:965-980. [PMID: 38759005 PMCID: PMC11191491 DOI: 10.3233/jad-231251] [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] [Accepted: 03/27/2024] [Indexed: 05/19/2024]
Abstract
Background Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) show differential vulnerability to large-scale brain functional networks. Plasma neurofilament light (NfL), a promising biomarker of neurodegeneration, has been linked in AD patients to glucose metabolism changes in AD-related regions. However, it is unknown whether plasma NfL would be similarly associated with disease-specific functional connectivity changes in AD and bvFTD. Objective Our study examined the associations between plasma NfL and functional connectivity of the default mode and salience networks in patients with AD and bvFTD. Methods Plasma NfL and neuroimaging data from patients with bvFTD (n = 16) and AD or mild cognitive impairment (n = 38; AD + MCI) were analyzed. Seed-based functional connectivity maps of key regions within the default mode and salience networks were obtained and associated with plasma NfL in these patients. RESULTS We demonstrated divergent associations between NfL and functional connectivity in AD + MCI and bvFTD patients. Specifically, AD + MCI patients showed lower default mode network functional connectivity with higher plasma NfL, while bvFTD patients showed lower salience network functional connectivity with higher plasma NfL. Further, lower NfL-related default mode network connectivity in AD + MCI patients was associated with lower Montreal Cognitive Assessment scores and higher Clinical Dementia Rating sum-of-boxes scores, although NfL-related salience network connectivity in bvFTD patients was not associated with Neuropsychiatric Inventory Questionnaire scores. CONCLUSIONS Our findings indicate that plasma NfL is differentially associated with brain functional connectivity changes in AD and bvFTD.
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Affiliation(s)
- Joanna Su Xian Chong
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Human Potential Translational Research Programme and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yi Jayne Tan
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Amelia Jialing Koh
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Human Potential Translational Research Programme and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Simon Kang Seng Ting
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Adeline Su Lyn Ng
- Department of Neurology, National Neuroscience Institute, Tan Tock Seng Hospital, Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Human Potential Translational Research Programme and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- Neuroscience and Behavioural Disorders Programme, Duke-NUS Medical School, Singapore
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Eldaief MC, Brickhouse M, Katsumi Y, Rosen H, Carvalho N, Touroutoglou A, Dickerson BC. Atrophy in behavioural variant frontotemporal dementia spans multiple large-scale prefrontal and temporal networks. Brain 2023; 146:4476-4485. [PMID: 37201288 PMCID: PMC10629759 DOI: 10.1093/brain/awad167] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/10/2023] [Accepted: 04/16/2023] [Indexed: 05/20/2023] Open
Abstract
The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.
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Affiliation(s)
- Mark C Eldaief
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Center for Brain Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Yuta Katsumi
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Howard Rosen
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nicole Carvalho
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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9
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Luo B, Qiu C, Chang L, Lu Y, Dong W, Liu D, Xue C, Yan J, Zhang W. Altered brain network centrality in Parkinson's disease patients after deep brain stimulation: a functional MRI study using a voxel-wise degree centrality approach. J Neurosurg 2023; 138:1712-1719. [PMID: 36334296 DOI: 10.3171/2022.9.jns221640] [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: 07/13/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE After deep brain stimulation (DBS), patients with Parkinson's disease (PD) show improved motor symptoms and decreased verbal fluency, an effect that occurs before the initiation of DBS in the subthalamic nucleus. However, the underlying mechanism remains unclear. This study aimed to evaluate the effects of DBS on whole-brain degree centrality (DC) and seed-based functional connectivity (FC) in PD patients. METHODS The authors obtained resting-state functional MRI data of 28 PD patients before and after DBS surgery. All patients underwent MRI scans in the off-stimulation state. The DC method was used to evaluate the effects of DBS on whole-brain FC at the voxel level. Seed-based FC analysis was used to examine network function changes after DBS. RESULTS After DBS surgery, PD patients showed significantly weaker DC values in the left middle temporal gyrus, left supramarginal gyrus, and left middle frontal gyrus, but significantly stronger DC values in the midbrain, left precuneus, and right precentral gyrus. FC analysis revealed decreased FC values within the default mode network (DMN). CONCLUSIONS This study demonstrated that the DC of DMN-related brain regions decreased in PD patients after DBS surgery, whereas the DC of the motor cortex increased. These findings provide new evidence for the neural effects of DBS on voxel-based whole-brain networks in PD patients.
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Affiliation(s)
- Bei Luo
- Departments of1Functional Neurosurgery
| | - Chang Qiu
- Departments of1Functional Neurosurgery
| | - Lei Chang
- Departments of1Functional Neurosurgery
| | - Yue Lu
- Departments of1Functional Neurosurgery
| | | | | | | | - Jun Yan
- 4Geriatric Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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10
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Chu M, Jiang D, Liu L, Nie B, Rosa-Neto P, Chen K, Wu L. Clinical relevance of disrupted topological organization of anatomical connectivity in behavioral variant frontotemporal dementia. Neurobiol Aging 2023; 124:29-38. [PMID: 36724600 PMCID: PMC11102657 DOI: 10.1016/j.neurobiolaging.2023.01.004] [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: 07/26/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/13/2023]
Abstract
Graph theory is a novel approach used to examine the balance of brain connectomes. However, the clinical relevance of white matter (WM) connectome changes in the behavioral variant frontotemporal dementia (bvFTD) is not well understood. We aimed to investigate the clinical relevance of WM topological alterations in bvFTD. Thirty patients with probable bvFTD and 30 healthy controls underwent diffusion tensor imaging, structural MRI, and neuropsychological assessment. WM connectivity between 90 brain regions was calculated and the graph approach was applied to capture the individual characteristics of the anatomical network. Voxel-based morphometry and tract-based spatial statistics were used to present the gray matter atrophy and disrupted WM integrity. The topological organization was disrupted in patients with bvFTD both globally and locally. Compared to controls, bvFTD data showed a different pattern of hub region distributions. Notably, the nodal efficiency of the right superior orbital frontal gyrus was associated with apathy and disinhibition. Topological measures may be potential image markers for early diagnosis and disease severity monitoring of bvFTD.
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Affiliation(s)
- Min Chu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Deming Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Li Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Pedro Rosa-Neto
- McGill Centre for Studies in Aging, Alzheimer's Disease Research Unit, Montreal, Canada
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; College of Medicine-Phoenix, University of Arizona, Tucson, AZ, USA; School of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Liyong Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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11
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McKenna MC, Lope J, Bede P, Tan EL. Thalamic pathology in frontotemporal dementia: Predilection for specific nuclei, phenotype-specific signatures, clinical correlates, and practical relevance. Brain Behav 2023; 13:e2881. [PMID: 36609810 PMCID: PMC9927864 DOI: 10.1002/brb3.2881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Frontotemporal dementia (FTD) phenotypes are classically associated with distinctive cortical atrophy patterns and regional hypometabolism. However, the spectrum of cognitive and behavioral manifestations in FTD arises from multisynaptic network dysfunction. The thalamus is a key hub of several corticobasal and corticocortical circuits. The main circuits relayed via the thalamic nuclei include the dorsolateral prefrontal circuit, the anterior cingulate circuit, and the orbitofrontal circuit. METHODS In this paper, we have reviewed evidence for thalamic pathology in FTD based on radiological and postmortem studies. Original research papers were systematically reviewed for preferential involvement of specific thalamic regions, for phenotype-associated thalamic disease burden patterns, characteristic longitudinal changes, and genotype-associated thalamic signatures. Moreover, evidence for presymptomatic thalamic pathology was also reviewed. Identified papers were systematically scrutinized for imaging methods, cohort sizes, clinical profiles, clinicoradiological associations, and main anatomical findings. The findings of individual research papers were amalgamated for consensus observations and their study designs further evaluated for stereotyped shortcomings. Based on the limitations of existing studies and conflicting reports in low-incidence FTD variants, we sought to outline future research directions and pressing research priorities. RESULTS FTD is associated with focal thalamic degeneration. Phenotype-specific thalamic traits mirror established cortical vulnerability patterns. Thalamic nuclei mediating behavioral and language functions are preferentially involved. Given the compelling evidence for considerable thalamic disease burden early in the course of most FTD subtypes, we also reflect on the practical relevance, diagnostic role, prognostic significance, and monitoring potential of thalamic metrics in FTD. CONCLUSIONS Cardinal manifestations of FTD phenotypes are likely to stem from thalamocortical circuitry dysfunction and are not exclusively driven by focal cortical changes.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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12
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Ribarič S. Detecting Early Cognitive Decline in Alzheimer's Disease with Brain Synaptic Structural and Functional Evaluation. Biomedicines 2023; 11:355. [PMID: 36830892 PMCID: PMC9952956 DOI: 10.3390/biomedicines11020355] [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: 12/23/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Early cognitive decline in patients with Alzheimer's (AD) is associated with quantifiable structural and functional connectivity changes in the brain. AD dysregulation of Aβ and tau metabolism progressively disrupt normal synaptic function, leading to loss of synapses, decreased hippocampal synaptic density and early hippocampal atrophy. Advances in brain imaging techniques in living patients have enabled the transition from clinical signs and symptoms-based AD diagnosis to biomarkers-based diagnosis, with functional brain imaging techniques, quantitative EEG, and body fluids sampling. The hippocampus has a central role in semantic and episodic memory processing. This cognitive function is critically dependent on normal intrahippocampal connections and normal hippocampal functional connectivity with many cortical regions, including the perirhinal and the entorhinal cortex, parahippocampal cortex, association regions in the temporal and parietal lobes, and prefrontal cortex. Therefore, decreased hippocampal synaptic density is reflected in the altered functional connectivity of intrinsic brain networks (aka large-scale networks), including the parietal memory, default mode, and salience networks. This narrative review discusses recent critical issues related to detecting AD-associated early cognitive decline with brain synaptic structural and functional markers in high-risk or neuropsychologically diagnosed patients with subjective cognitive impairment or mild cognitive impairment.
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Affiliation(s)
- Samo Ribarič
- Faculty of Medicine, Institute of Pathophysiology, University of Ljubljana, Zaloška 4, SI-1000 Ljubljana, Slovenia
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13
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [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: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye
- Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
- Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, Italy
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14
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Penalba-Sánchez L, Oliveira-Silva P, Sumich AL, Cifre I. Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study. Front Aging Neurosci 2023; 14:1037347. [PMID: 36698861 PMCID: PMC9869068 DOI: 10.3389/fnagi.2022.1037347] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. Methods In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. Results Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. Conclusion The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain,Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal,NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom,*Correspondence: Lucía Penalba-Sánchez,
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculdade de Educação e Psicologia, Universidade Católica Portuguesa, Porto, Portugal
| | - Alexander Luke Sumich
- NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, United Kingdom
| | - Ignacio Cifre
- Facultat de Psicologia, Ciències de l’educació i de l’Esport, Blanquerna, Universitat Ramon Llull, Barcelona, Spain
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15
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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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16
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A low-dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia. Alzheimers Res Ther 2022; 14:199. [PMID: 36581943 PMCID: PMC9798659 DOI: 10.1186/s13195-022-01145-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/14/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. METHODS We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses. RESULTS Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). CONCLUSIONS A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.
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17
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Wei L, Zhang Y, Wang J, Xu L, Yang K, Lv X, Zhu Z, Gong Q, Hu W, Li X, Qian M, Shen Y, Chen W. Parietal-hippocampal rTMS improves cognitive function in Alzheimer's disease and increases dynamic functional connectivity of default mode network. Psychiatry Res 2022; 315:114721. [PMID: 35839637 DOI: 10.1016/j.psychres.2022.114721] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 11/26/2022]
Abstract
Parietal-hippocampal repetitive transcranial magnetic stimulation (rTMS) improves cognitive function in Alzheimer's disease (AD), however, the underlying therapeutic mechanism has not been elucidated. A double-blind, randomized, sham-controlled parietal-hippocampal rTMS trial (five sessions/week for a total of 10 sessions) of mild-to-moderate AD patients was conducted in the study. High-frequency rTMS was applied to a subject-specific left lateral parietal region with the highest functional connectivity with the hippocampus based on resting-state fMRI. A multimodal MRI scan and a complete neuropsychological battery of tests were conducted at baseline, immediately after the intervention and 12-week follow-up after the rTMS treatment. Compared to sham treatment (n = 27), patients undergoing active rTMS treatment (n = 29) showed higher Mini Mental State Examination (MMSE) score and dynamic functional connectivity (dFC) magnitude of the default mode network (DMN) after two weeks of rTMS treatment, but not at 12-week follow-up. A significant positive correlation was observed between changes in MMSE and changes in the dFC magnitude of DMN in patients who underwent active-rTMS treatment, but not in those who received sham-rTMS treatment. The findings of the current study indicate that fMRI-guided rTMS treatment improves cognitive function of AD patients in the short term, and DMN functional connectivity contributes to therapeutic effectiveness of rTMS.
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Affiliation(s)
- Lili Wei
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Yingchun Zhang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Jintao Wang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Luoyi Xu
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Kehua Yang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Xinghui Lv
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Zhenwei Zhu
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Qian Gong
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China
| | - Weiming Hu
- Third People's Hospital of Quzhou, Quzhou, Zhejiang 324003, China
| | - Xia Li
- Shanghai Mental Health Center, Shanghai 200030, China
| | - Mincai Qian
- Third People's Hospital of Huzhou, Huzhou, Zhejiang 313002, China.
| | - Yuedi Shen
- Hangzhou Normal University School of Medicine, Hangzhou, Zhejiang 311121, China.
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, East Qingchun Road 3#, Hangzhou, Zhejiang 310016, China; Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310007, China; Key Laboratory of Medical Neurobiology of Zhejiang Province, Hangzhou 310016, China.
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18
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Why can spontaneous intracranial hypotension cause behavioral changes? A case report and multimodality neuroimaging comparison with frontotemporal dementia. Cortex 2022; 155:322-332. [DOI: 10.1016/j.cortex.2022.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/26/2022] [Accepted: 07/30/2022] [Indexed: 11/22/2022]
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19
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Du K, Chen P, Zhao K, Qu Y, Kang X, Liu Y. Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites. BMC Bioinformatics 2022; 23:280. [PMID: 35836122 PMCID: PMC9284684 DOI: 10.1186/s12859-022-04776-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity. RESULTS In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = -0.38, P < 0.001; three classes classification: R = -0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls. CONCLUSIONS The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.
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Affiliation(s)
- Kai Du
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
- Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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20
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Nigro S, Filardi M, Tafuri B, De Blasi R, Cedola A, Gigli G, Logroscino G. The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front Neurol 2022; 13:910054. [PMID: 35837233 PMCID: PMC9275562 DOI: 10.3389/fneur.2022.910054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Salvatore Nigro
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Roberto De Blasi
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Giancarlo Logroscino
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21
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Wu Y, Wu X, Gao L, Yan Y, Geng Z, Zhou S, Zhu W, Tian Y, Yu Y, Wei L, Wang K. Abnormal Functional Connectivity of Thalamic Subdivisions in Alzheimer's Disease: A Functional Magnetic Resonance Imaging Study. Neuroscience 2022; 496:73-82. [PMID: 35690336 DOI: 10.1016/j.neuroscience.2022.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/23/2022] [Accepted: 06/02/2022] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease (AD) is characterized by global cognitive impairment in multiple cognitive domains. Thalamic dysfunction during AD progression has been reported. However, there are limited studies regarding dysfunction in the functional connectivity (FC) of thalamic subdivisions and the relationship between such dysfunction and clinical assessments. This study examined dysfunction in the FC of thalamic subdivisions and determined the relationship between such dysfunction and clinical assessments. Forty-eight patients with AD and 47 matched healthy controls were recruited and assessed with scales for multiple cognitive domains. Group-wise comparisons of FC with thalamic subdivisions as seed points were conducted to identify abnormal cerebral regions. Moreover, correlation analysis was conducted to evaluate the relationship between abnormal FC and cognitive performance. Decreased FC of the intralaminar and medial nuclei with the left precuneus was observed in patients but not in heathy controls. The abnormal FC of the medial nuclei with the left precuneus was correlated with the Mini Mental State Examination score in the patient group. Using the FC values showing between-group differences, the linear support vector machine classifier achieved quite good in accuracy, sensitivity, specificity and area under the curve. Dysfunction in the FC of the intralaminar and medial thalamus with the precuneus may comprise a potential neural substrate for cognitive impairment during AD progression, which in turn may provide new treatment targets.
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Affiliation(s)
- Yue Wu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China
| | - Xingqi Wu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China
| | - Liying Gao
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China
| | - Yibing Yan
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China
| | - Zhi Geng
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China; Department of Neurology, Second People's Hospital of Hefei City, The Hefei Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Shanshan Zhou
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province 230022, China
| | - Wanqiu Zhu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province 230022, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China.
| | - Ling Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province 230022, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui 230022, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China; The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province 230022, China.
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22
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Zhang M, Guan Z, Zhang Y, Sun W, Li W, Hu J, Li B, Ye G, Meng H, Huang X, Lin X, Wang J, Liu J, Li B, Li Y. Disrupted coupling between salience network segregation and glucose metabolism is associated with cognitive decline in Alzheimer's disease - A simultaneous resting-state FDG-PET/fMRI study. Neuroimage Clin 2022; 34:102977. [PMID: 35259618 PMCID: PMC8904621 DOI: 10.1016/j.nicl.2022.102977] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 12/21/2022]
Abstract
Hybrid PET/MRI was used to explore network segregation and glucose metabolism in AD. DMN, CEN, and SN showed reduced segregation in AD. In salience network, segregation coupled with glucose metabolism in CN group. The coupled segregation and glucose metabolism in CN disappeared in MCI and AD. Reduced segregation and hypometabolism were associated with cognitive impairments.
The aberrant organization and functioning of three core neurocognitive networks (NCNs), i.e., default-mode network (DMN), central executive network (CEN), and salience network (SN), are among the prominent features in Alzheimer’s disease (AD). The dysregulation of both intra- and inter-network functional connectivities (FCs) of the three NCNs contributed to AD-related cognitive and behavioral abnormalities. Brain functional network segregation, integrating intra- and inter-network FCs, is essential for maintaining the energetic efficiency of brain metabolism. The association of brain functional network segregation, together with glucose metabolism, with age-related cognitive decline was recently shown. Yet how these joint functional-metabolic biomarkers relate to cognitive decline along with mild cognitive impairment (MCI) and AD remains to be elucidated. In this study, under the framework of the triple-network model, we performed a hybrid FDG-PET/fMRI study to evaluate the concurrent changes of resting-state brain intrinsic FCs and glucose metabolism of the three NCNs across cognitively normal (CN) (N = 24), MCI (N = 21), and AD (N = 21) groups. Lower network segregation and glucose metabolism were observed in all three NCNs in patients with AD. More interestingly, in the SN, the coupled relationship between network segregation and glucose metabolism existed in the CN group (r = 0.523, p = 0.013) and diminished in patients with MCI (r = 0.431, p = 0.065) and AD (r = 0.079, p = 0.748). Finally, the glucose metabolism of the DMN (r = 0.380, p = 0.017) and the network segregation of the SN (r = 0.363, p = 0.023) were significantly correlated with the general cognitive status of the patients. Our findings suggest that the impaired SN segregation and its uncoupled relationship with glucose metabolism contribute to the cognitive decline in AD.
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Affiliation(s)
- Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ziyun Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wanqing Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jialin Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Binyin Li
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guanyu Ye
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jin Wang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jun Liu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai 200025, China.
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
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23
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Yang Z, Sheng X, Qin R, Chen H, Shao P, Xu H, Yao W, Zhao H, Xu Y, Bai F. Cognitive Improvement via Left Angular Gyrus-Navigated Repetitive Transcranial Magnetic Stimulation Inducing the Neuroplasticity of Thalamic System in Amnesic Mild Cognitive Impairment Patients. J Alzheimers Dis 2022; 86:537-551. [PMID: 35068464 DOI: 10.3233/jad-215390] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Stimulating superficial brain regions highly associated with the hippocampus by repetitive transcranial magnetic stimulation (rTMS) may improve memory of Alzheimer’s disease (AD) spectrum patients. Objective: We recruited 16 amnesic mild cognitive impairment (aMCI) and 6 AD patients in the study. All the patients were stimulated to the left angular gyrus, which was confirmed a strong link to the hippocampus through neuroimaging studies, by the neuro-navigated rTMS for four weeks. Methods: Automated fiber quantification using diffusion tensor imaging metrics and graph theory analysis on functional network were employed to detect the neuroplasticity of brain networks. Results: After neuro-navigated rTMS intervention, the episodic memory of aMCI patients and Montreal Cognitive Assessment score of two groups were significantly improved. Increased FA values of right anterior thalamic radiation among aMCI patients, while decreased functional network properties of thalamus subregions were observed, whereas similar changes not found in AD patients. It is worth noting that the improvement of cognition was associated with the neuroplasticity of thalamic system. Conclusion: We speculated that the rTMS intervention targeting left angular gyrus may be served as a strategy to improve cognitive impairment at the early stage of AD patients, supporting by the neuroplasticity of thalamic system.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Weina Yao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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24
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Chaudhary S, Zhornitsky S, Chao HH, van Dyck CH, Li CSR. Emotion Processing Dysfunction in Alzheimer's Disease: An Overview of Behavioral Findings, Systems Neural Correlates, and Underlying Neural Biology. Am J Alzheimers Dis Other Demen 2022; 37:15333175221082834. [PMID: 35357236 PMCID: PMC9212074 DOI: 10.1177/15333175221082834] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
We described behavioral studies to highlight emotional processing deficits in Alzheimer's disease (AD). The findings suggest prominent deficit in recognizing negative emotions, pronounced effect of positive emotion on enhancing memory, and a critical role of cognitive deficits in manifesting emotional processing dysfunction in AD. We reviewed imaging studies to highlight morphometric and functional markers of hippocampal circuit dysfunction in emotional processing deficits. Despite amygdala reactivity to emotional stimuli, hippocampal dysfunction conduces to deficits in emotional memory. Finally, the reviewed studies implicating major neurotransmitter systems in anxiety and depression in AD supported altered cholinergic and noradrenergic signaling in AD emotional disorders. Overall, the studies showed altered emotions early in the course of illness and suggest the need of multimodal imaging for further investigations. Particularly, longitudinal studies with multiple behavioral paradigms translatable between preclinical and clinical models would provide data to elucidate the time course and underlying neurobiology of emotion processing dysfunction in AD.
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Affiliation(s)
- Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Simon Zhornitsky
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Herta H. Chao
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA,VA Connecticut Healthcare System, West Haven, CT, USA
| | - Christopher H. van Dyck
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA,Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA,Wu Tsai Institute, Yale University, New Haven, CT, USA
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