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Thompson KI, Schneider CJ, Lopez-Roque JA, Wakschlag LS, Karim HT, Perlman SB. A network approach to the investigation of childhood irritability: probing frustration using social stimuli. J Child Psychol Psychiatry 2024; 65:959-972. [PMID: 38124618 PMCID: PMC11161318 DOI: 10.1111/jcpp.13937] [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] [Accepted: 10/31/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli. METHODS Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives. RESULTS ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p < .05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p < .05, FDR-corrected). CONCLUSIONS Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability.
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Affiliation(s)
- Khalil I Thompson
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Clayton J Schneider
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Justin A Lopez-Roque
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
| | - Lauren S Wakschlag
- Department of Medical Social Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Helmet T Karim
- Department of Psychiatry, University of Pittsburg School of Medicine, Pittsburgh, PA, USA
| | - Susan B Perlman
- Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA
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2
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Yang KC, Yang BH, Liu MN, Liou YJ, Chou YH. Cognitive impairment in schizophrenia is associated with prefrontal-striatal functional hypoconnectivity and striatal dopaminergic abnormalities. J Psychopharmacol 2024; 38:515-525. [PMID: 38853592 DOI: 10.1177/02698811241257877] [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] [Indexed: 06/11/2024]
Abstract
BACKGROUND A better understanding of the mechanisms underlying cognitive impairment in schizophrenia is imperative, as it causes poor functional outcomes and a lack of effective treatments. AIMS This study aimed to investigate the relationships of two proposed main pathophysiology of schizophrenia, altered prefrontal-striatal connectivity and the dopamine system, with cognitive impairment and their interactions. METHODS Thirty-three patients with schizophrenia and 27 healthy controls (HCs) who are right-handed and matched for age and sex were recruited. We evaluated their cognition, functional connectivity (FC) between the dorsolateral prefrontal cortex (DLPFC)/middle frontal gyrus (MiFG) and striatum, and the availability of striatal dopamine transporter (DAT) using a cognitive battery investigating attention, memory, and executive function, resting-state functional magnetic resonance imaging with group independent component analysis and single-photon emission computed tomography with 99mTc-TRODAT. RESULTS Patients with schizophrenia exhibited poorer cognitive performance, reduced FC between DLPFC/MiFG and the caudate nucleus (CN) or putamen, decreased DAT availability in the left CN, and decreased right-left DAT asymmetry in the CN compared to HCs. In patients with schizophrenia, altered imaging markers are associated with cognitive impairments, especially the relationship between DLPFC/MiFG-putamen FC and attention and between DAT asymmetry in the CN and executive function. CONCLUSIONS This study is the first to demonstrate how prefrontal-striatal hypoconnectivity and altered striatal DAT markers are associated with different domains of cognitive impairment in schizophrenia. More research is needed to evaluate their complex relationships and potential therapeutic implications.
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Affiliation(s)
- Kai-Chun Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Bang-Hung Yang
- Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Mu-N Liu
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ying-Jay Liou
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yuan-Hwa Chou
- Department of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taichung Veterans General Hospital, Taichung, Taiwan
- The Human Brain Research Center, Taichung Veterans General Hospital, Taichung, Taiwan
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3
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Peng C, Guo D, Liu L, Xiao D, Nie L, Liang H, Guo D, Yang H. Total sleep deprivation alters spontaneous brain activity in medical staff during routine clinical work: a resting-state functional MR imaging study. Front Neurosci 2024; 18:1377094. [PMID: 38638698 PMCID: PMC11025562 DOI: 10.3389/fnins.2024.1377094] [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: 01/26/2024] [Accepted: 03/21/2024] [Indexed: 04/20/2024] Open
Abstract
Objectives To assess the effect of total sleep deprivation (TSD) on spontaneous brain activity in medical staff during routine clinical practice. Methods A total of 36 medical staff members underwent resting-state functional MRI (rs-fMRI) scans and neuropsychological tests twice, corresponding to rested wakefulness (RW) after normal sleep and 24 h of acute TSD. The rs-fMRI features, including the mean fractional amplitude of low-frequency fluctuation (mfALFF), z-score transformed regional homogeneity (zReHo), and functional connectivity (zFC), were compared between RW and TSD. Correlation coefficients between the change in altered rs-fMRI features and the change in altered scores of neuropsychological tests after TSD were calculated. Receiver operating characteristic (ROC) and logistic regression analyses were performed to evaluate the diagnostic efficacy of significantly altered rs-fMRI features in distinguishing between RW and TSD states. Results Brain regions, including right superior temporal gyrus, bilateral postcentral gyrus, left medial superior frontal gyrus, left middle temporal gyrus, right precentral gyrus, and left precuneus, showed significantly enhanced rs-fMRI features (mfALFF, zReHo, zFC) after TSD. Moreover, the changes in altered rs-fMRI features of the right superior temporal gyrus, bilateral postcentral gyrus, left middle temporal gyrus, and left precuneus were significantly correlated with the changes in several altered scores of neuropsychological tests. The combination of mfALFF (bilateral postcentral gyrus) and zFC (left medial superior frontal gyrus and left precuneus) showed the highest area under the curve (0.870) in distinguishing RW from TSD. Conclusion Spontaneous brain activity alterations occurred after TSD in routine clinical practice, which might explain the reduced performances of these participants in neurocognitive tests after TSD. These alterations might be potential imaging biomarkers for assessing the impact of TSD and distinguishing between RW and TSD states.
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Affiliation(s)
- Cong Peng
- The Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Dingbo Guo
- The Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Liuheng Liu
- The Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Dongling Xiao
- Department of Anatomy, Key Lab for Biomechanics and Tissue Engineering of Chongqing, Army Medical University (Third Military Medical University), Chongqing, China
| | - Lisha Nie
- GE Healthcare, MR Research, Beijing, China
| | | | - Dajing Guo
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hua Yang
- The Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
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Kumar VA, Lee J, Liu HL, Allen JW, Filippi CG, Holodny AI, Hsu K, Jain R, McAndrews MP, Peck KK, Shah G, Shimony JS, Singh S, Zeineh M, Tanabe J, Vachha B, Vossough A, Welker K, Whitlow C, Wintermark M, Zaharchuk G, Sair HI. Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy. AJNR Am J Neuroradiol 2024; 45:139-148. [PMID: 38164572 DOI: 10.3174/ajnr.a8067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 01/03/2024]
Abstract
Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.
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Affiliation(s)
- V A Kumar
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J Lee
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - H-L Liu
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J W Allen
- Emory University (J.W.A.), Atlanta, Georgia
| | - C G Filippi
- Tufts University (C.G.F.), Boston, Massachusetts
| | - A I Holodny
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - K Hsu
- New York University (K.H., R.J.), New York, New York
| | - R Jain
- New York University (K.H., R.J.), New York, New York
| | - M P McAndrews
- University of Toronto (M.P.M.), Toronto, Ontario, Canada
| | - K K Peck
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - G Shah
- University of Michigan (G.S.), Ann Arbor, Michigan
| | - J S Shimony
- Washington University School of Medicine (J.S.S.), St. Louis, Missouri
| | - S Singh
- University of Texas Southwestern Medical Center (S.S.), Dallas, Texas
| | - M Zeineh
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - J Tanabe
- University of Colorado (J.T.), Aurora, Colorado
| | - B Vachha
- University of Massachusetts (B.V.), Worcester, Massachusetts
| | - A Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania (A.V.), Philadelphia, Pennsylvania
| | - K Welker
- Mayo Clinic (K.W.), Rochester, Minnesota
| | - C Whitlow
- Wake Forest University (C.W.), Winston-Salem, North Carolina
| | - M Wintermark
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - G Zaharchuk
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - H I Sair
- Johns Hopkins University (H.I.S.), Baltimore, Maryland
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Clementi L, Arnone E, Santambrogio MD, Franceschetti S, Panzica F, Sangalli LM. Anatomically compliant modes of variations: New tools for brain connectivity. PLoS One 2023; 18:e0292450. [PMID: 37934760 PMCID: PMC10629624 DOI: 10.1371/journal.pone.0292450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 11/09/2023] Open
Abstract
Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
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Affiliation(s)
- Letizia Clementi
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- CHDS, Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco D. Santambrogio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | | | | | - Laura M. Sangalli
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
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Chou BC, Lerner A, Barisano G, Phung D, Xu W, Pinto SN, Sheikh-Bahaei N. Functional MRI and Diffusion Tensor Imaging in Migraine: A Review of Migraine Functional and White Matter Microstructural Changes. J Cent Nerv Syst Dis 2023; 15:11795735231205413. [PMID: 37900908 PMCID: PMC10612465 DOI: 10.1177/11795735231205413] [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: 01/21/2023] [Accepted: 09/14/2023] [Indexed: 10/31/2023] Open
Abstract
Migraine is a complex and heterogenous disorder whose disease mechanisms remain disputed. This narrative review summarizes functional MRI (fMRI) and diffusion tensor imaging (DTI) findings and interprets their association with migraine symptoms and subtype to support and expand our current understanding of migraine pathophysiology. Our PubMed search evaluated and included fMRI and DTI studies involving comparisons between migraineurs vs healthy controls, migraineurs with vs without aura, and episodic vs chronic migraineurs. Migraineurs demonstrate changes in functional connectivity (FC) and regional activation in numerous pain-related networks depending on migraine phase, presence of aura, and chronicity. Changes to diffusion indices are observed in major cortical white matter tracts extending to the brainstem and cerebellum, more prominent in chronic migraine and associated with FC changes. Reported changes in FC and regional activation likely relate to pain processing and sensory hypersensitivities. Diffuse white matter microstructural changes in dysfunctional cortical pain and sensory pathways complement these functional differences. Interpretations of reported fMRI and DTI measure trends have not achieved a clear consensus due to inconsistencies in the migraine neuroimaging literature. Future fMRI and DTI studies should establish and implement a uniform methodology that reproduces existing results and directly compares migraineurs with different subtypes. Combined fMRI and DTI imaging may provide better pathophysiological explanations for nonspecific FC and white matter microstructural differences.
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Affiliation(s)
- Brendon C. Chou
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alexander Lerner
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Daniel Phung
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Wilson Xu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Soniya N. Pinto
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nasim Sheikh-Bahaei
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Wang L, Zhu R, Zhou X, Zhang Z, Peng D. Altered local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances. Front Aging Neurosci 2023; 15:1269582. [PMID: 37920381 PMCID: PMC10619161 DOI: 10.3389/fnagi.2023.1269582] [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: 07/30/2023] [Accepted: 10/03/2023] [Indexed: 11/04/2023] Open
Abstract
Objectives This study aimed to investigate local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD). Methods Thirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo. Results Compared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine. Conclusion The current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.
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Affiliation(s)
- Lei Wang
- Department of Neurology, Beijing Geriatric Hospital, Beijing, China
| | - Rui Zhu
- Department of Neurology, Beijing Geriatric Hospital, Beijing, China
| | - Xiao Zhou
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zhiyong Zhang
- Department of Neurology, Beijing Geriatric Hospital, Beijing, China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
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Lin C, Liu D, Liu Y, Chen Z, Wei X, Liu H, Wang K, Liu T, Xiao L, Rong L. Altered functional activity of the precuneus and superior temporal gyrus in patients with residual dizziness caused by benign paroxysmal positional vertigo. Front Neurosci 2023; 17:1221579. [PMID: 37901419 PMCID: PMC10600499 DOI: 10.3389/fnins.2023.1221579] [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: 05/12/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Benign paroxysmal positional vertigo (BPPV) is a common clinical vertigo disease, and the most effective treatment for this disease is canal repositioning procedures (CRP). Most patients return to normal after a single treatment. However, some patients still experience residual dizziness (RD) after treatment, and this disease's pathogenesis is currently unclear. The purpose of this study is to explore whether there are abnormal brain functional activities in patients with RD by using resting-state functional magnetic resonance imaging (rs-fMRI) and to provide imaging evidence for the study of the pathogenesis of RD. Materials and methods The BPPV patients in the Second Affiliated Hospital of Xuzhou Medical University had been included from December 2021 to November 2022. All patients had been received the collection of demographic and clinical characteristics (age, gender, involved semicircular canal, affected side, CRP times, BPPV course, duration of RD symptoms, and whether they had hypertension, diabetes, coronary heart disease.), scale assessment, including Dizziness Handicap Inventory (DHI), Hamilton Anxiety Inventory (HAMA), Hamilton Depression Inventory (HAMD), rs-fMRI data collection, CRP treatment, and then a one-month follow-up. According to the follow-up results, 18 patients with RD were included. At the same time, we selected 19 healthy individuals from our hospital's physical examination center who matched their age, gender as health controls (HC). First, the amplitude of low-frequency fluctuations (ALFF) analysis method was used to compare the local functional activities of the two groups of subjects. Then, the brain regions with different ALFF results were extracted as seed points. Functional connectivity (FC) analysis method based on seed points was used to explore the whole brain FC of patients with RD. Finally, a correlation analysis between clinical features and rs-fMRI data was performed. Results Compared to the HC, patients with RD showed lower ALFF value in the right precuneus and higher ALFF value in the right superior temporal gyrus (STG). When using the right STG as a seed point, it was found that the FC between the right STG, the right supramarginal gyrus (SMG), and the left precuneus was decreased in RD patients. However, no significant abnormalities in the FC were observed when using the right precuneus as a seed point. Conclusion In patients with RD, the local functional activity of the right precuneus is weakened, and the local functional activity of the right STG is enhanced. Furthermore, the FC between the right STG, the right SMG, and the left precuneus is weakened. These changes may explain the symptoms of dizziness, floating sensation, walking instability, neck tightness, and other symptoms in patients with RD to a certain extent.
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Affiliation(s)
- Cunxin Lin
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dan Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yueji Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Zhengwei Chen
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiue Wei
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Haiyan Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Kai Wang
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Tengfei Liu
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Lijie Xiao
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Liangqun Rong
- Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
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9
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da Silveira RV, Li LM, Castellano G. Texture-based brain networks for characterization of healthy subjects from MRI. Sci Rep 2023; 13:16421. [PMID: 37775531 PMCID: PMC10541866 DOI: 10.1038/s41598-023-43544-6] [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: 04/12/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.
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Affiliation(s)
- Rafael Vinícius da Silveira
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil.
| | - Li Min Li
- Department of Neurology, School of Medical Sciences, University of Campinas - UNICAMP, R. Tessália Vieira de Camargo, 126, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-887, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
| | - Gabriela Castellano
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
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10
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Sengupta A, Wang F, Mishra A, Reed JL, Chen LM, Gore JC. Detection and characterization of resting state functional networks in squirrel monkey brain. Cereb Cortex Commun 2023; 4:tgad018. [PMID: 37753115 PMCID: PMC10518810 DOI: 10.1093/texcom/tgad018] [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: 06/02/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Resting-state fMRI based on analyzing BOLD signals is widely used to derive functional networks in the brain and how they alter during disease or injury conditions. Resting-state networks can also be used to study brain functional connectomes across species, which provides insights into brain evolution. The squirrel monkey (SM) is a non-human primate (NHP) that is widely used as a preclinical model for experimental manipulations to understand the organization and functioning of the brain. We derived resting-state networks from the whole brain of anesthetized SMs using Independent Component Analysis of BOLD acquisitions. We detected 15 anatomically constrained resting-state networks localized in the cortical and subcortical regions as well as in the white-matter. Networks encompassing visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and subcortical networks including the Hippocampus-Amygdala, thalamus, basal-ganglia and brainstem region correspond well with previously detected networks in humans and NHPs. The connectivity pattern between the networks also agrees well with previously reported seed-based resting-state connectivity of SM brain. This study demonstrates that SMs share remarkable homologous network organization with humans and other NHPs, thereby providing strong support for their suitability as a translational animal model for research and additional insight into brain evolution across species.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Jamie L Reed
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Department of Psychology, Vanderbilt University, Nashville, TN, United States of America
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America
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11
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Berger D, Matharoo GS, Levman J. Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations. J Med Imaging (Bellingham) 2023; 10:036003. [PMID: 37323123 PMCID: PMC10266090 DOI: 10.1117/1.jmi.10.3.036003] [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: 11/15/2022] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Purpose Random matrix theory (RMT) is an increasingly useful tool for understanding large, complex systems. Prior studies have examined functional magnetic resonance imaging (fMRI) scans using tools from RMT, with some success. However, RMT computations are highly sensitive to a number of analytic choices, and the robustness of findings involving RMT remains in question. We systematically investigate the usefulness of RMT on a wide variety of fMRI datasets using a rigorous predictive framework. Approach We develop open-source software to efficiently compute RMT features from fMRI images and examine the cross-validated predictive potential of eigenvalue and RMT-based features ("eigenfeatures") with classic machine-learning classifiers. We systematically vary pre-processing extent, normalization procedures, RMT unfolding procedures, and feature selection and compare the impact of these analytic choices on the distributions of cross-validated prediction performance for each combination of dataset binary classification task, classifier, and feature. To deal with class imbalance, we use the area under the receiver operating characteristic curve (AUROC) as the main performance metric. Results Across all classification tasks and analytic choices, we find RMT- and eigenvalue-based "eigenfeatures" to have predictive utility more often than not (82.4% of median AUROCs > 0.5 ; median AUROC range across classification tasks 0.47 to 0.64). Simple baseline reductions on source timeseries, by contrast, were less useful (58.8% of median AUROCs > 0.5 , median AUROC range across classification tasks 0.42 to 0.62). Additionally, eigenfeature AUROC distributions were overall more right-tailed than baseline features, suggesting greater predictive potential. However, performance distributions were wide and often significantly affected by analytic choices. Conclusions Eigenfeatures clearly have potential for understanding fMRI functional connectivity in a wide variety of scenarios. The utility of these features is strongly dependent on analytic decisions, suggesting caution when interpreting past and future studies applying RMT to fMRI. However, our study demonstrates that the inclusion of RMT statistics in fMRI investigations could improve prediction performances across a wide variety of phenomena.
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Affiliation(s)
- Derek Berger
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
| | - Gurpreet S. Matharoo
- St. Francis Xavier University, ACENET, Antigonish, Nova Scotia, Canada
- St. Francis Xavier University, Department of Physics, Antigonish, Nova Scotia, Canada
| | - Jacob Levman
- St. Francis Xavier University, Department of Computer Science, Antigonish, Nova Scotia, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
- Nova Scotia Health Authority, Research Affiliate, Antigonish, Nova Scotia, Canada
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12
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Effects of exogenous oxytocin and estradiol on resting-state functional connectivity in women and men. Sci Rep 2023; 13:3113. [PMID: 36813823 PMCID: PMC9947123 DOI: 10.1038/s41598-023-29754-y] [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: 11/15/2022] [Accepted: 02/09/2023] [Indexed: 02/24/2023] Open
Abstract
Possible interactions of the neuropeptide oxytocin and the sex hormone estradiol may contribute to previously observed sex-specific effects of oxytocin on resting-state functional connectivity (rsFC) of the amygdala and hippocampus. Therefore, we used a placebo-controlled, randomized, parallel-group functional magnetic resonance imaging study design and measured amygdala and hippocampus rsFC in healthy men (n = 116) and free-cycling women (n = 111), who received estradiol gel (2 mg) or placebo before the intranasal administration of oxytocin (24 IU) or placebo. Our results reveal significant interaction effects of sex and treatments on rsFC of the amygdala and hippocampus in a seed-to-voxel analysis. In men, both oxytocin and estradiol significantly decreased rsFC between the left amygdala and the right and left lingual gyrus, the right calcarine fissure, and the right superior parietal gyrus compared to placebo, while the combined treatment produced a significant increase in rsFC. In women, the single treatments significantly increased the rsFC between the right hippocampus and the left anterior cingulate gyrus, whereas the combined treatment had the opposite effect. Collectively, our study indicates that exogenous oxytocin and estradiol have different region-specific effects on rsFC in women and men and that the combined treatment may produce antagonistic effects.
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13
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Yousefian A, Shayegh F, Maleki Z. Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals. Front Syst Neurosci 2023; 16:904770. [PMID: 36817947 PMCID: PMC9932324 DOI: 10.3389/fnsys.2022.904770] [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: 03/25/2022] [Accepted: 12/28/2022] [Indexed: 02/05/2023] Open
Abstract
Introduction Can we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain functional connectivity patterns. Attempts to unveil the common neural patterns emerged in ASD are the essence of ASD classification. We claim that graph representation learning methods can appropriately extract the connectivity patterns of the brain, in such a way that the method can be generalized to every recording condition, and phenotypical information of subjects. These methods can capture the whole structure of the brain, both local and global properties. Methods The investigation is done for the worldwide brain imaging multi-site database known as ABIDE I and II (Autism Brain Imaging Data Exchange). Among different graph representation techniques, we used AWE, Node2vec, Struct2vec, multi node2vec, and Graph2Img. The best approach was Graph2Img, in which after extracting the feature vectors representative of the brain nodes, the PCA algorithm is applied to the matrix of feature vectors. The classifier adapted to the features embedded in graphs is an LeNet deep neural network. Results and discussion Although we could not outperform the previous accuracy of 10-fold cross-validation in the identification of ASD versus control patients in this dataset, for leave-one-site-out cross-validation, we could obtain better results (our accuracy: 80%). The result is that graph embedding methods can prepare the connectivity matrix more suitable for applying to a deep network.
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Affiliation(s)
| | - Farzaneh Shayegh
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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14
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Mejia AF, Bolin D, Yue YR, Wang J, Caffo BS, Nebel MB. Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference. J Comput Graph Stat 2022; 32:413-433. [PMID: 37377728 PMCID: PMC10292763 DOI: 10.1080/10618600.2022.2104289] [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: 02/09/2021] [Accepted: 06/14/2022] [Indexed: 10/17/2022]
Abstract
Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis.
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Affiliation(s)
- Amanda F. Mejia
- Department of Statistics, Indiana University, Bloomington, IN, 47408
| | - David Bolin
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Yu Ryan Yue
- Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, New York, NY, 10010
| | - Jiongran Wang
- Department of Statistics, Indiana University, Bloomington, IN, 47408
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205
- Department of Neurology, Johns Hopkins University, Baltimore, MD, 21205
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15
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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] [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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16
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Guilbert J, Légaré A, De Koninck P, Desrosiers P, Desjardins M. Toward an integrative neurovascular framework for studying brain networks. NEUROPHOTONICS 2022; 9:032211. [PMID: 35434179 PMCID: PMC8989057 DOI: 10.1117/1.nph.9.3.032211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/11/2022] [Indexed: 05/28/2023]
Abstract
Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.
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Affiliation(s)
- Jérémie Guilbert
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| | - Antoine Légaré
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Paul De Koninck
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Patrick Desrosiers
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
| | - Michèle Desjardins
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
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17
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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18
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Brain Reactions to Opening and Closing the Eyes: Salivary Cortisol and Functional Connectivity. Brain Topogr 2022; 35:375-397. [PMID: 35666364 PMCID: PMC9334428 DOI: 10.1007/s10548-022-00897-x] [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: 01/29/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
Abstract
This study empirically assessed the strength and duration of short-term effects induced by brain reactions to closing/opening the eyes on a few well-known resting-state networks. We also examined the association between these reactions and subjects’ cortisol levels. A total of 55 young adults underwent 8-min resting-state fMRI (rs-fMRI) scans under 4-min eyes-closed and 4-min eyes-open conditions. Saliva samples were collected from 25 of the 55 subjects before and after the fMRI sessions and assayed for cortisol levels. Our empirical results indicate that when the subjects were relaxed with their eyes closed, the effect of opening the eyes on conventional resting-state networks (e.g., default-mode, frontal-parietal, and saliency networks) lasted for roughly 60-s, during which we observed a short-term increase in activity in rs-fMRI time courses. Moreover, brain reactions to opening the eyes had a pronounced effect on time courses in the temporo-parietal lobes and limbic structures, both of which presented a prolonged decrease in activity. After controlling for demographic factors, we observed a significantly positive correlation between pre-scan cortisol levels and connectivity in the limbic structures under both conditions. Under the eyes-closed condition, the temporo-parietal lobes presented significant connectivity to limbic structures and a significantly positive correlation with pre-scan cortisol levels. Future research on rs-fMRI could consider the eyes-closed condition when probing resting-state connectivity and its neuroendocrine correlates, such as cortisol levels. It also appears that abrupt instructions to open the eyes while the subject is resting quietly with eyes closed could be used to probe brain reactivity to aversive stimuli in the ventral hippocampus and other limbic structures.
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19
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Functional connectivity patterns of trait empathy are associated with age. Brain Cogn 2022; 159:105859. [PMID: 35305500 DOI: 10.1016/j.bandc.2022.105859] [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: 12/28/2020] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 12/30/2022]
Abstract
Empathy is the capacity to feel and understand others' mental states. In some individuals, there is an imbalance between the affective and cognitive components of empathy, which can lead to deficits. This study investigated the functional connectivity of the anterior insula (AI) and dorsomedial prefrontal cortex (dmPFC), which play key roles in empathy, in covariation with the affective and cognitive subscales of the Interpersonal Reactivity Index (IRI), as a function of age and sex, as an exploratory analysis. Seed-based functional connectivity analyses were performed on 33 healthy participants that were subdivided according to their age (16 adults and 17 adolescents) and sex (16 women and 17 men). Adolescents reported lower cognitive empathy than adults and men less affective empathy than women. The connectivity of the dmPFC and AI, in covariation with the cognitive and affective subscales of empathy, respectively, differed between adolescents and adults, but was similar in men and women. Adolescents had patterns of negative covariations between the regions of interest and many brain regions associated with the default-mode and salience networks. These findings support that lower self-report levels of empathy in certain individuals could be reflected in the functional connectivity patterns of the dmPFC and AI.
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20
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Altunkaya S, Huang SM, Hsu YH, Yang JJ, Lin CY, Kuo LW, Tu MC. Dissociable Functional Brain Networks Associated With Apathy in Subcortical Ischemic Vascular Disease and Alzheimer’s Disease. Front Aging Neurosci 2022; 13:717037. [PMID: 35185511 PMCID: PMC8851472 DOI: 10.3389/fnagi.2021.717037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 12/27/2021] [Indexed: 01/10/2023] Open
Abstract
Few studies have investigated differences in functional connectivity (FC) between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD), especially in relation to apathy. Therefore, the aim of this study was to compare apathy-related FC changes among patients with SIVD, AD, and cognitively normal subjects. The SIVD group had the highest level of apathy as measured using the Apathy Evaluation Scale-clinician version (AES). Dementia staging, volume of white matter hyperintensities (WMH), and the Beck Depression Inventory were the most significant clinical predictors for apathy. Group-wise comparisons revealed that the SIVD patients had the worst level of “Initiation” by factor analysis of the AES. FCs from four resting state networks (RSNs) were compared, and the connectograms at the level of intra- and inter-RSNs revealed dissociable FC changes, shared FC in the dorsal attention network, and distinct FC in the salient network across SIVD and AD. Neuronal correlates for “Initiation” deficits that underlie apathy were explored through a regional-specific approach, which showed that the right inferior frontal gyrus, left middle frontal gyrus, and left anterior insula were the critical hubs. These findings broaden the disconnection theory by considering the effect of FC interactions across multiple RSNs on apathy formation.
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Affiliation(s)
- Sabri Altunkaya
- Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Turkey
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yen-Hsuan Hsu
- Department of Psychology, National Chung Cheng University, Chiayi, Taiwan
- Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chaiyi, Taiwan
| | - Jir-Jei Yang
- Department of Radiology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
| | - Chien-Yuan Lin
- GE Healthcare, GE Medical Systems Taiwan, Ltd., Taipei, Taiwan
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Min-Chien Tu
- Department of Neurology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien, Taiwan
- *Correspondence: Min-Chien Tu,
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22
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Wu C, Ferreira F, Fox M, Harel N, Hattangadi-Gluth J, Horn A, Jbabdi S, Kahan J, Oswal A, Sheth SA, Tie Y, Vakharia V, Zrinzo L, Akram H. Clinical applications of magnetic resonance imaging based functional and structural connectivity. Neuroimage 2021; 244:118649. [PMID: 34648960 DOI: 10.1016/j.neuroimage.2021.118649] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/23/2022] Open
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
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Affiliation(s)
- Chengyuan Wu
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
| | - Francisca Ferreira
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, 2021 Sixth Street S.E., Minneapolis, MN 55455, USA.
| | - Jona Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, Center for Precision Radiation Medicine, University of California, San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037, USA.
| | - Andreas Horn
- Neurology Department, Movement Disorders and Neuromodulation Section, Charité - University Medicine Berlin, Charitéplatz 1, D-10117, Berlin, Germany.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.
| | - Joshua Kahan
- Department of Neurology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Mansfield Rd, Oxford OX1 3TH, UK.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Ninth Floor, Houston, TX 77030, USA.
| | - Yanmei Tie
- Center for Brain Circuit Therapeutics, Departments of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Vejay Vakharia
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK.
| | - Ludvic Zrinzo
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
| | - Harith Akram
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
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23
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Geller WN, Liu K, Warren SL. Specificity of anhedonic alterations in resting-state network connectivity and structure: A transdiagnostic approach. Psychiatry Res Neuroimaging 2021; 317:111349. [PMID: 34399282 DOI: 10.1016/j.pscychresns.2021.111349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/11/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
Anhedonia is a prominent characteristic of depression and related pathology that is associated with a prolonged course of mood disturbance and treatment resistance. However, the neurobiological mechanisms of anhedonia are poorly understood as few studies have disentangled the specific effects of anhedonia from other co-occurring symptoms. Here, we take a transdiagnostic, dimensional approach to distinguish anhedonia alterations from other internalizing symptoms on intrinsic functional brain circuits. 53 adults with varying degrees of anxiety and/or depression completed resting-state fMRI. Neural networks were identified through independent components analysis. Dual regression was used to characterize within-network functional connectivity alterations associated with individual differences in anhedonia. Modulation of between-network functional connectivity by anhedonia was tested using region-of-interest to region-of-interest correlational analyses. Anhedonia was associated with visual network hyperconnectivity and expansion of the visual, dorsal attention, and default networks. Additionally, anhedonia was associated with decreased between-network connectivity among default, salience, dorsal attention, somatomotor, and visual networks. Findings suggest that anhedonia is associated with aberrant connectivity and structural alterations in resting-state networks that contribute to impairments in reward learning, low motivation, and negativity bias characteristic of depression. Results reveal dissociable effects of anhedonia on resting-state network dynamics, characterizing possible neurocircuit mechanisms for intervention.
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Affiliation(s)
- Whitney N Geller
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA
| | - Kevin Liu
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA
| | - Stacie L Warren
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304, USA.
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24
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Li X, Fischer H, Manzouri A, Månsson KNT, Li TQ. A Quantitative Data-Driven Analysis Framework for Resting-State Functional Magnetic Resonance Imaging: A Study of the Impact of Adult Age. Front Neurosci 2021; 15:768418. [PMID: 34744623 PMCID: PMC8565286 DOI: 10.3389/fnins.2021.768418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/28/2021] [Indexed: 01/08/2023] Open
Abstract
The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18-76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects' age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.
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Affiliation(s)
- Xia Li
- Institute of Informatics Engineering, China Jiliang University, Hangzhou, China
| | - Håkan Fischer
- Department of Psychology, Stockholm University, Stockholm, Sweden.,Stockholm University Brain Imaging Centre, Stockholm, Sweden
| | | | - Kristoffer N T Månsson
- Department of Psychology, Stockholm University, Stockholm, Sweden.,Centre of Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Tie-Qiang Li
- Institute of Informatics Engineering, China Jiliang University, Hangzhou, China.,Department of Clinical Science, Intervention, and Technology, Karolinska Institute, Solna, Sweden.,Department of Medical Radiation and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden
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25
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Sengupta A, Mishra A, Wang F, Li M, Yang PF, Chen LM, Gore JC. Functional networks in non-human primate spinal cord and the effects of injury. Neuroimage 2021; 240:118391. [PMID: 34271158 PMCID: PMC8527400 DOI: 10.1016/j.neuroimage.2021.118391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 07/12/2021] [Indexed: 12/12/2022] Open
Abstract
Spontaneous fluctuations of Blood Oxygenation-Level Dependent (BOLD) MRI signal in a resting state have previously been detected and analyzed to describe intrinsic functional networks in the spinal cord of rodents, non-human primates and human subjects. In this study we combined high resolution imaging at high field with data-driven Independent Component Analysis (ICA) to i) delineate fine-scale functional networks within and between segments of the cervical spinal cord of monkeys, and also to ii) characterize the longitudinal effects of a unilateral dorsal column injury on these networks. Seven distinct functional hubs were revealed within each spinal segment, with new hubs detected at bilateral intermediate and gray commissure regions in addition to the bilateral dorsal and ventral horns previously reported. Pair-wise correlations revealed significantly stronger connections between hubs on the dominant hand side. Unilateral dorsal-column injuries disrupted predominantly inter-segmental rather than intra-segmental functional connectivities as revealed by correlation strengths and graph-theory based community structures. The effects of injury on inter-segmental connectivity were evident along the length of the cord both below and above the lesion region. Connectivity strengths recovered over time and there was revival of inter-segmental communities as animals recovered function. BOLD signals of frequency 0.01-0.033 Hz were found to be most affected by injury. The results in this study provide new insights into the intrinsic functional architecture of spinal cord and underscore the potential of functional connectivity measures to characterize changes in networks after an injury and during recovery.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Pai-Feng Yang
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, TN, 37232, USA; Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA; Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37232, USA
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26
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Gao Q, Huang Y, Xiang Y, Yang C, Zhang M, Guo J, Wang H, Yu J, Cui Q, Chen H. Altered dynamics of functional connectivity density associated with early and advanced stages of motor training in tennis and table tennis athletes. Brain Imaging Behav 2021; 15:1323-1334. [PMID: 32748323 DOI: 10.1007/s11682-020-00331-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Until now, knowledge about the effects of motor training on the temporal dynamics of the brain functional organization is still limited. Here we combined dynamic functional connectivity density (dFCD) mapping and k-means clustering analyses to explore how early and advanced stages of motor training affected the brain dynamic FC architecture and dynamic states in little-ball athletes using resting-state functional magnetic resonance imaging (fMRI) data of student-athletes (SA), elite athletes (EA) and non-athlete healthy controls (NC). The ANOVA analysis demonstrated the levels of dFCD variability in the EA group had the trend to regress to the NC group levels in all statistically significant regions. Specifically, the brain regions responsible for the basic motor and sensory innervations showed more stabilized dFCD variability in EA and NC compared with SA. The results supported the idea of a stronger efficiency of functional networks and an automation process of new motor skills in EA. Furthermore, EA and NC had the increased dFCD variability in brain regions responsible for top-down visual-motor control compared with SA; while EA exhibited more flexible alterations in FCD status levels and the equilibrium probability in the long run compared with SA and NC. This suggested that regions involved in higher functions of visual-motor control exhibited more flexibility in functional regulation with other brain networks in EA. Our findings suggested the diversity and specialization of fluctuating dynamic brain adaption induced by motor training in different training stages, and highlighted the effect of motor training stages on brain functional adaption.
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Affiliation(s)
- Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yue Huang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yu Xiang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chengbo Yang
- The Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China
| | - Mu Zhang
- Information Technology Center, Chengdu Sport University, 610041, Chengdu, China
| | - Jingpu Guo
- The Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China
| | - Hu Wang
- The Third Department of Physical Education and Training, Chengdu Sport University, 610041, Chengdu, China
| | - Jiali Yu
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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27
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Houston ML, Houston JR, Sakaie K, Klinge PM, Vorster S, Luciano M, Loth F, Allen PA. Functional connectivity abnormalities in Type I Chiari: associations with cognition and pain. Brain Commun 2021; 3:fcab137. [PMID: 34278303 PMCID: PMC8279071 DOI: 10.1093/braincomms/fcab137] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 04/13/2021] [Accepted: 06/11/2021] [Indexed: 12/30/2022] Open
Abstract
There is initial evidence of microstructural abnormalities in the fibre-tract pathways of the cerebellum and cerebrum of individuals diagnosed with Type I Chiari malformation. However, it is unclear whether abnormal white matter architecture and macro-level morphological deviations that have been observed in Chiari translate to differences in functional connectivity. Furthermore, common symptoms of Chiari include pain and cognitive deficits, but the relationship between these symptoms and functional connectivity has not been explored in this population. Eighteen Type I Chiari patients and 18 age-, sex- and education-matched controls underwent resting-state functional MRI to measure functional connectivity. Participants also completed a neuropsychological battery and completed self-report measures of chronic pain. Group differences in functional connectivity were identified. Subsequently, pathways of significant difference were re-analyzed after controlling for the effects of attention performance and self-reported chronic pain. Chiari patients exhibited functional hypoconnectivity between areas of the cerebellum and cerebrum. Controlling for attention eliminated all deficits with the exception of that from the posterior cerebellar pathway. Similarly, controlling for pain also eliminated deficits except for those from the posterior cerebellar pathway and vermis VII. Patterns of Chiari hyperconnectivity were also found between regions of the cerebellum and cerebrum in Chiari patients. Hyperconnectivity in all regions was eliminated after controlling for attention except between left lobule VIII and the left postcentral gyrus and between vermis IX and the precuneus. Similarly, hyperconnectivity was eliminated after controlling for pain except between the default mode network and globus pallidus, left lobule VIII and the left postcentral gyrus, and Vermis IX and the precuneus. Evidence of both hyper- and hypoconnectivity were identified in Chiari, which is posited to support the hypothesis that the effect of increased pain in Chiari draws on neural resources, requiring an upregulation in inhibitory control mechanisms and resulting in cognitive dysfunction. Areas of hypoconnectivity in Chiari patients also suggest disruption in functional pathways, and potential mechanisms are discussed.
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Affiliation(s)
- Michelle L Houston
- Department of Psychology, The University of Akron, Akron, OH 44325, USA
- Department of Psychology, Middle Tennessee State University, Murfreesboro, TN 37132, USA
| | - James R Houston
- Department of Psychology, Middle Tennessee State University, Murfreesboro, TN 37132, USA
| | - Ken Sakaie
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, OH 44195, USA
| | - Petra M Klinge
- Department of Neurosurgery, Rhode Island Hospital, and Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Sarel Vorster
- Department of Neurological Surgery, The Cleveland Clinic, Cleveland, OH 44195, USA
| | - Mark Luciano
- Department of Neurosurgery, Johns Hopkins Medical Center, Baltimore, MD 21224, USA
| | - Francis Loth
- Department of Biomedical Engineering, The University of Akron, Akron, OH 44325, USA
- Department of Mechanical Engineering, The University of Akron, Akron, OH 44325, USA
| | - Philip A Allen
- Department of Psychology, The University of Akron, Akron, OH 44325, USA
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28
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Kumar U, Keshri A, Mishra M. Alteration of brain resting-state networks and functional connectivity in prelingual deafness. J Neuroimaging 2021; 31:1135-1145. [PMID: 34189809 DOI: 10.1111/jon.12904] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Early hearing loss causes several changes in the brain structure and function at multiple levels; these changes can be observed through neuroimaging. These changes are directly associated with sensory loss (hearing) and the acquisition of alternative communication strategies. Such plasticity changes in the brain might establish a different connectivity pattern with resting-state networks (RSNs) and other brain regions. We performed resting-state functional magnetic resonance imaging (rsfMRI) to evaluate these intrinsic modifications. METHODS We used two methods to characterize the functional connectivity (FC) of RSN components in 20 prelingual deaf adults and 20 demographic-matched hearing adults. rsfMRI data were analyzed using independent component analysis (ICA) and region-of-interest seed-to-voxel correlation analysis. RESULTS In ICA, we identified altered FC of RSNs in the deaf group. RSNs with altered FC were observed in higher visual, auditory, default mode, salience, and sensorimotor networks. The findings of seed-to-voxel correlation analysis suggested increased temporal coherence with other neural networks in the deaf group compared with the hearing control group. CONCLUSION These findings suggest a highly diverse resting-state connectivity pattern in prelingual deaf adults resulting from compensatory cross-modal plasticity that includes both auditory and nonauditory regions.
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Affiliation(s)
- Uttam Kumar
- Centre of Bio-Medical Research, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India
| | - Amit Keshri
- Department of Neuro-otology, Sanjay Gandhi Postgraduate Institute of Medical Sciences Campus, Lucknow, India
| | - Mrutyunjaya Mishra
- Department of Special Education (Hearing Impairments), Dr. Shakuntala Misra National Rehabilitation University, Lucknow, India
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29
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Xu W, Rao J, Song Y, Chen S, Xue C, Hu G, Lin X, Chen J. Altered Functional Connectivity of the Basal Nucleus of Meynert in Subjective Cognitive Impairment, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:671351. [PMID: 34248603 PMCID: PMC8267913 DOI: 10.3389/fnagi.2021.671351] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 01/10/2023] Open
Abstract
Background: The spectrum of early Alzheimer's disease (AD) is thought to include subjective cognitive impairment, early mild cognitive impairment (eMCI), and late mild cognitive impairment (lMCI). Choline dysfunction affects the early progression of AD, in which the basal nucleus of Meynert (BNM) is primarily responsible for cortical cholinergic innervation. The aims of this study were to determine the abnormal patterns of BNM-functional connectivity (BNM-FC) in the preclinical AD spectrum (SCD, eMCI, and lMCI) and further explore the relationships between these alterations and neuropsychological measures. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate FC based on a seed mask (BNM mask) in 28 healthy controls (HC), 30 SCD, 24 eMCI, and 25 lMCI patients. Furthermore, the relationship between altered FC and neurocognitive performance was examined by a correlation analysis. The receiver operating characteristic (ROC) curve of abnormal BNM-FC was used to specifically determine the classification ability to differentiate the early AD disease spectrum relative to HC (SCD and HC, eMCI and HC, lMCI and HC) and pairs of groups in the AD disease spectrum (eMCI and SCD, lMCI and SCD, eMCI and lMCI). Results: Compared with HC, SCD patients showed increased FC in the bilateral SMA and decreased FC in the bilateral cerebellum and middle frontal gyrus (MFG), eMCI patients showed significantly decreased FC in the bilateral precuneus, and lMCI individuals showed decreased FC in the right lingual gyrus. Compared with the SCD group, the eMCI group showed decreased FC in the right superior frontal gyrus (SFG), while the lMCI group showed decreased FC in the left middle temporal gyrus (MTG). Compared with the eMCI group, the lMCI group showed decreased FC in the right hippocampus. Interestingly, abnormal FC was associated with certain cognitive domains and functions including episodic memory, executive function, information processing speed, and visuospatial function in the disease groups. BNM-FC of SFG in distinguishing eMCI from SCD; BNM-FC of MTG in distinguishing lMCI from SCD; BNM-FC of the MTG, hippocampus, and cerebellum in distinguishing SCD from HC; and BNM-FC of the hippocampus and MFG in distinguishing eMCI from lMCI have high sensitivity and specificity. Conclusions: The abnormal BNM-FC patterns can characterize the early disease spectrum of AD (SCD, eMCI, and lMCI) and are closely related to the cognitive domains. These new and reliable findings will provide a new perspective in identifying the early disease spectrum of AD and further strengthen the role of cholinergic theory in AD.
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Affiliation(s)
- Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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30
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Wein S, Deco G, Tomé AM, Goldhacker M, Malloni WM, Greenlee MW, Lang EW. Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5573740. [PMID: 34135951 PMCID: PMC8177997 DOI: 10.1155/2021/5573740] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/06/2021] [Indexed: 12/12/2022]
Abstract
This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Technology and Information, University Pompeu Fabra, Carrer Tanger, 122-140, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats, University Barcelona, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Ana Maria Tomé
- IEETA/DETI, University de Aveiro, Aveiro 3810-193, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Wilhelm M. Malloni
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Mark W. Greenlee
- Experimental Psychology, University of Regensburg, Regensburg 93040, Germany
| | - Elmar W. Lang
- CIML, Biophysics, University of Regensburg, Regensburg 93040, Germany
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31
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Lidstone DE, Rochowiak R, Mostofsky SH, Nebel MB. A Data Driven Approach Reveals That Anomalous Motor System Connectivity is Associated With the Severity of Core Autism Symptoms. Autism Res 2021:10.1002/aur.2476. [PMID: 33484109 PMCID: PMC8931705 DOI: 10.1002/aur.2476] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/27/2020] [Accepted: 01/07/2021] [Indexed: 11/11/2022]
Abstract
This study examined whether disruptions in connectivity involving regions critical for learning, planning, and executing movements are relevant to core autism symptoms. Spatially constrained ICA was performed using resting-state fMRI from 419 children (autism spectrum disorder (ASD) = 105; typically developing (TD) = 314) to identify functional motor subdivisions. Comparing the spatial organization of each subdivision between groups, we found voxels that contributed significantly less to the right posterior cerebellar component in children with ASD versus TD (P <0.001). Next, we examined the effect of diagnosis on right posterior cerebellar connectivity with all other motor subdivisions. The model was significant (P = 0.014) revealing that right posterior cerebellar connectivity with bilateral dorsomedial primary motor cortex was, on average, stronger in children with ASD, while right posterior cerebellar connectivity with left-inferior parietal lobule (IPL), bilateral dorsolateral premotor cortex, and supplementary motor area was stronger in TD children (all P ≤0.02). We observed a diagnosis-by-connectivity interaction such that for children with ASD, elevated social-communicative and excessive repetitive-behavior symptom severity were both associated with right posterior cerebellar-left-IPL hypoconnectivity (P ≤0.001). Right posterior cerebellar and left-IPL are strongly implicated in visuomotor processing with dysfunction in this circuit possibly leading to anomalous development of skills, such as motor imitation, that are crucial for effective social-communication. LAY SUMMARY: This study examines whether communication between various brain regions involved in the control of movement are disrupted in children with autism spectrum disorder (ASD). We show communication between the right posterior cerebellum and left IPL, a circuit important for efficient visual-motor integration, is disrupted in children with ASD and associated with the severity of ASD symptoms. These results may explain observations of visual-motor integration impairments in children with ASD that are associated with ASD symptom severity.
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Affiliation(s)
- Daniel E. Lidstone
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rebecca Rochowiak
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Phase-locking of resting-state brain networks with the gastric basal electrical rhythm. PLoS One 2021; 16:e0244756. [PMID: 33400717 PMCID: PMC7785240 DOI: 10.1371/journal.pone.0244756] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 12/15/2020] [Indexed: 11/19/2022] Open
Abstract
A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.
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Zhao Y, Caffo BS, Wang B, Li CSR, Luo X. A whole-brain modeling approach to identify individual and group variations in functional connectivity. Brain Behav 2021; 11:e01942. [PMID: 33210469 PMCID: PMC7821576 DOI: 10.1002/brb3.1942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 12/28/2022] Open
Abstract
Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Neuroscience, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Herdick M, Dyrba M, Fritz HCJ, Altenstein S, Ballarini T, Brosseron F, Buerger K, Can Cetindag A, Dechent P, Dobisch L, Duezel E, Ertl-Wagner B, Fliessbach K, Dawn Freiesleben S, Frommann I, Glanz W, Dylan Haynes J, Heneka MT, Janowitz D, Kilimann I, Laske C, Metzger CD, Munk MH, Peters O, Priller J, Roy N, Scheffler K, Schneider A, Spottke A, Jakob Spruth E, Tscheuschler M, Vukovich R, Wiltfang J, Jessen F, Teipel S, Grothe MJ. Multimodal MRI analysis of basal forebrain structure and function across the Alzheimer's disease spectrum. Neuroimage Clin 2020; 28:102495. [PMID: 33395986 PMCID: PMC7689403 DOI: 10.1016/j.nicl.2020.102495] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/19/2020] [Accepted: 11/02/2020] [Indexed: 11/25/2022]
Abstract
BACKGROUND Dysfunction of the cholinergic basal forebrain (cBF) is associated with cognitive decline in Alzheimer's disease (AD). Multimodal MRI allows for the investigation of cBF changes in-vivo. In this study we assessed alterations in cBF functional connectivity (FC), mean diffusivity (MD), and volume across the spectrum of AD. We further assessed effects of amyloid pathology on these changes. METHODS Participants included healthy controls, and subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or AD dementia (ADD) from the multicenter DELCODE study. Resting-state functional MRI (rs-fMRI) and structural MRI data was available for 477 subjects, and a subset of 243 subjects also had DTI data available. Differences between diagnostic groups were investigated using seed-based FC, volumetric, and MD analyses of functionally defined anterior (a-cBF) and posterior (p-cBF) subdivisions of a cytoarchitectonic cBF region-of-interest. In complementary analyses groups were stratified according to amyloid status based on CSF Aβ42/40 biomarker data, which was available in a subset of participants. RESULTS a-cBF and p-cBF subdivisions showed regional FC profiles that were highly consistent with previously reported patterns, but there were only minimal differences between diagnostic groups. Compared to controls, cBF volumes and MD were significantly different in MCI and ADD but not in SCD. The Aβ42/40 stratified analyses largely matched these results. CONCLUSIONS We reproduced subregion-specific FC profiles of the cBF in a clinical sample spanning the AD spectrum. At least in this multicentric cohort study, cBF-FC did not show marked changes along the AD spectrum, and multimodal MRI did not provide more sensitive measures of AD-related cBF changes compared to volumetry.
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Affiliation(s)
- Meret Herdick
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Hans-Christian J Fritz
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - Tommaso Ballarini
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE, Munich), Feodor-Lynen-Strasse 17, 81377 Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - Arda Can Cetindag
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Duezel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig-Maximilians-University, Marchioninistr. 15, 81377 Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Silka Dawn Freiesleben
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Ingo Frommann
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, Berlin, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Feodor-Lynen-Strasse 17, 81377 Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Psychiatry and Psychotherapy, Hindenburgdamm 30, 12203 Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076 Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Neurology, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité, Charitéplatz 1, 10117 Berlin, Germany
| | - Maike Tscheuschler
- Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany
| | - Ruth Vukovich
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Von-Siebold-Str. 5, 37075 Goettingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Venusberg-Campus 1, 53127 Bonn, Germany; Department of Psychiatry, University of Cologne, Medical Faculty, Kerpener Strasse 62, 50924 Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Strasse 26, 50931Köln, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany.
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain.
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Yang C, Luo N, Liang M, Zhou S, Yu Q, Zhang J, Zhang M, Guo J, Wang H, Yu J, Cui Q, Chen H, Gao Q. Altered Brain Functional Connectivity Density in Fast-Ball Sports Athletes With Early Stage of Motor Training. Front Psychol 2020; 11:530122. [PMID: 33101115 PMCID: PMC7546905 DOI: 10.3389/fpsyg.2020.530122] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 08/25/2020] [Indexed: 12/02/2022] Open
Abstract
The human brain shows neuroplastic adaptations caused by motor skill training. Of note, there is little known about the plastic architecture of the whole-brain network in resting state. The purpose of the present study was to detect how motor training affected the density distribution of whole-brain resting-state functional connectivity (FC). Resting-state functional magnetic resonance imaging data was assessed based on a comparison of fast-ball student athletes (SA) and non-athlete healthy controls (NC). The voxel-wise data-driven graph theory approach, global functional connectivity density (gFCD) mapping, was applied. Results showed that the SA group exhibited significantly decreased gFCD in brain regions centered at the left triangular part of the inferior frontal gyrus (IFG), extending to the opercular part of the left IFG and middle frontal gyrus compared to the NC group. In addition, findings suggested the idea of an increased neural efficiency of athletes’ brain regions associated with attentional–motor modulation and executive control. Furthermore, behavioral results showed that in the SA group, faster executive control reaction time relates to smaller gFCD values in the left IFG. These findings suggested that the motor training would decrease the numbers of FC in IFG to accelerate the executive control with high attentional demands and enable SA to rapidly focus the attention to detect the intriguing target.
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Affiliation(s)
- Chengbo Yang
- The Third Department of Physical Education and Training, Chengdu Sport University, Chengdu, China
| | - Ning Luo
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Minfeng Liang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Sihong Zhou
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Yu
- Exercise and Mental Health Laboratory, School of Psychology, Shenzhen University, Shenzhen, China
| | - Jiabao Zhang
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Mu Zhang
- Information Technology Center, Chengdu Sport University, Chengdu, China
| | - Jingpu Guo
- The Third Department of Physical Education and Training, Chengdu Sport University, Chengdu, China
| | - Hu Wang
- The Third Department of Physical Education and Training, Chengdu Sport University, Chengdu, China
| | - Jiali Yu
- School of Mathematical Sciences, 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
| | - Huafu Chen
- Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.,The Clinical Hospital of Chengdu Brain Science Institute, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Gao
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
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Abstract
Since its discovery in 1997, the default mode network (DMN) and its components have been extensively studied in both healthy individuals and psychiatric patients. Several studies have investigated possible DMN alterations in specific mental conditions such as bipolar disorder (BD). In this review, we describe current evidence from resting-state functional magnetic resonance imaging studies with the aim to understand possible changes in the functioning of the DMN in BD. Overall, several types of analyses including seed-based and independent component have been conducted on heterogeneous groups of patients highlighting different results. Despite the differences, findings seem to indicate that BD is associated with alterations in both frontal and posterior DMN structures, mainly in the prefrontal, posterior cingulate and inferior parietal cortices. We conclude this review by suggesting possible future research directions.
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Bellani M, Bontempi P, Zovetti N, Gloria Rossetti M, Perlini C, Dusi N, Squarcina L, Marinelli V, Zoccatelli G, Alessandrini F, Francesca Maria Ciceri E, Sbarbati A, Brambilla P. Resting state networks activity in euthymic bipolar disorder. Bipolar Disord 2020; 22:593-601. [PMID: 32212391 DOI: 10.1111/bdi.12900] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Bipolar disorder (BD) is a psychiatric condition causing shifts in mood, energy and activity levels severely altering the quality of life of the patients even in the euthymic phase. Although widely accepted, the neurobiological bases of the disorder in the euthymic phase remain elusive. This study aims at characterizing resting state functional activity of the BD euthymic phase in order to better understand the pathogenesis of the disease and build future neurobiological models. METHODS Fifteen euthymic BD patients (10 females; mean age 40.2; standard deviation 13.5; range 20-61) and 27 healthy controls (HC) (21 females; mean age 37; standard deviation 10.6; range 22-60) underwent a 3T functional MRI scan at rest. Resting state activity was extracted through independent component analysis (ICA) run with automatic dimensionality estimation. RESULTS ICA identified 22 resting state networks (RSNs). Within-network analysis revealed decreased connectivity in the visual, temporal, motor and cerebellar RSNs of BD patients vs HC. Between-network analysis showed increased connectivity between motor area and the default mode network (DMN) partially overlapping with the fronto-parietal network (FPN) in BD patients. CONCLUSION Within-network analysis confirmed existing evidence of altered cerebellar, temporal, motor and visual networks in BD. Increased connectivity between the DMN and the motor area network suggests the presence of alterations of the fronto-parietal regions, precuneus and cingulate cortex in the euthymic condition. These findings indicate that specific connectivity alterations might persist even in the euthymic state suggesting the importance of examining both within and between-network connectivity to achieve a global understanding of the BD euthymic condition.
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Affiliation(s)
- Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Pietro Bontempi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Niccolò Zovetti
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Maria Gloria Rossetti
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,Department of Neuroscience and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy
| | - Nicola Dusi
- Psychiatry Unit, Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Letizia Squarcina
- Department of Neuroscience and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Veronica Marinelli
- Department of Surgery, Dentistry, Paediatrics and Gynaecology, University of Verona, Verona, Italy
| | - Giada Zoccatelli
- Neuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Franco Alessandrini
- Neuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy
| | - Elisa Francesca Maria Ciceri
- Neuroradiology Department, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy.,Department of Neurosurgery, IRCCS Fondazione Istituto Neurologico "C.Besta", Milano, Italy
| | - Andrea Sbarbati
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona
| | - Paolo Brambilla
- Department of Neuroscience and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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Dynamic Properties of Human Default Mode Network in Eyes-Closed and Eyes-Open. Brain Topogr 2020; 33:720-732. [PMID: 32803623 DOI: 10.1007/s10548-020-00792-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/08/2020] [Indexed: 10/23/2022]
Abstract
The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.
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Vallée C, Maurel P, Corouge I, Barillot C. Acquisition Duration in Resting-State Arterial Spin Labeling. How Long Is Enough? Front Neurosci 2020; 14:598. [PMID: 32848529 PMCID: PMC7406917 DOI: 10.3389/fnins.2020.00598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 05/15/2020] [Indexed: 12/12/2022] Open
Abstract
Resting-state Arterial Spin Labeling (rs-ASL) is a rather confidential method compared to resting-state BOLD. As ASL allows to quantify the cerebral blood flow, unlike BOLD, rs-ASL can lead to significant clinical subject-scaled applications. Despite directly impacting clinical practicability and functional networks estimation, there is no standard for rs-ASL regarding the acquisition duration. Our work here focuses on assessing the feasibility of ASL as an rs-fMRI method and on studying the effect of the acquisition duration on the estimation of functional networks. To this end, we acquired a long 24 min 30 s rs-ASL sequence and investigated how estimations of six typical functional brain networks evolved with respect to the acquisition duration. Our results show that, after a certain acquisition duration, the estimations of all functional networks reach their best and are stabilized. Since, for clinical application, the acquisition duration should be the shortest possible, we suggest an acquisition duration of 14 min, i.e., 240 volumes with our sequence parameters, as it covers the functional networks estimation stabilization.
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Affiliation(s)
- Corentin Vallée
- Université de Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
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An analytical workflow for seed-based correlation and independent component analysis in interventional resting-state fMRI studies. Neurosci Res 2020; 165:26-37. [PMID: 32464181 DOI: 10.1016/j.neures.2020.05.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/08/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022]
Abstract
Resting-state functional MRI (rs-fMRI) is a task-free method of detecting spatially distinct brain regions with correlated activity, which form organised networks known as resting-state networks (RSNs). The two most widely used methods for analysing RSN connectivity are seed-based correlation analysis (SCA) and independent component analysis (ICA) but there is no established workflow of the optimal combination of analytical steps and how to execute them. Rodent rs-fMRI data from our previous longitudinal brain stimulation studies were used to investigate these two methods using FSL. Specifically, we examined: (1) RSN identification and group comparisons in ICA, (2) ICA-based denoising compared to nuisance signal regression in SCA, and (3) seed selection in SCA. In ICA, using a baseline-only template resulted in greater functional connectivity within RSNs and more sensitive detection of group differences than when an average pre/post stimulation template was used. In SCA, the use of an ICA-based denoising method in the preprocessing of rs-fMRI data and the use of seeds from individual functional connectivity maps in running group comparisons increased the sensitivity of detecting group differences by preventing the reduction in signals of interest. Accordingly, when analysing animal and human rs-fMRI data, we infer that the use of baseline-only templates in ICA and ICA-based denoising and individualised seeds in SCA will improve the reliability of results and comparability across rs-fMRI studies.
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Chemotherapy-induced brain changes in breast cancer survivors: evaluation with multimodality magnetic resonance imaging. Brain Imaging Behav 2020; 13:1799-1814. [PMID: 30937827 DOI: 10.1007/s11682-019-00074-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Chemotherapy related cognitive impairments are common in breast cancer patients undergoing chemotherapy. These cognitive dysfunctions are mainly attributable to chemotherapy related brain structural and functional alterations. Multimodality magnetic resonance imaging (MRI) can reveal brain gray matter volume loss, white matter microstructural disruption, reduced gray matter density, impaired cerebral blood flow and brain structural and functional connection networks at both local and global levels. This review outlines the potential applications of multimodality MR imaging techniques in chemotherapy induced cognitive deficit in breast cancer survivors and provides future research perspective in this field.
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Lee Masson H, Pillet I, Boets B, Op de Beeck H. Task-dependent changes in functional connectivity during the observation of social and non-social touch interaction. Cortex 2020; 125:73-89. [DOI: 10.1016/j.cortex.2019.12.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 10/18/2019] [Accepted: 12/09/2019] [Indexed: 11/17/2022]
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Piazza C, Cantiani C, Miyakoshi M, Riva V, Molteni M, Reni G, Makeig S. EEG Effective Source Projections Are More Bilaterally Symmetric in Infants Than in Adults. Front Hum Neurosci 2020; 14:82. [PMID: 32226371 PMCID: PMC7080990 DOI: 10.3389/fnhum.2020.00082] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 01/02/2023] Open
Abstract
Although anatomical brain hemispheric asymmetries have been clearly documented in the infant brain, findings concerning functional hemispheric specialization have been inconsistent. The present report aims to assess whether bilaterally symmetric synchronous activity between the two hemispheres is a characteristic of the infant brain. To asses cortical bilateral synchronicity, we used decomposition by independent component analysis (ICA) of high-density electroencephalographic (EEG) data collected in an auditory passive oddball paradigm. Decompositions of concatenated 64-channel EEG data epochs from each of 34 typically developing 6-month-old infants and from 18 healthy young adults participating in the same passive auditory oddball protocol were compared to characterize differences in functional brain organization between early life and adulthood. Our results show that infant EEG decompositions comprised a larger number of independent component (IC) effective source processes compatible with a cortical origin and having bilaterally near-symmetric scalp projections (13.8% of the infant data ICs presented a bilateral pattern vs. 4.3% of the adult data ICs). These IC projections could be modeled as the sum of potentials volume-conducted to the scalp from synchronous locally coherent field activities in corresponding left and right cortical source areas. To conclude, in this paradigm, source-resolved infant brain EEG exhibited more bilateral synchronicity than EEG produced by the adult brain, supporting the hypothesis that more strongly unilateral and likely more functionally specialized unihemispheric cortical field activities are concomitants of brain maturation.
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Affiliation(s)
- Caterina Piazza
- Scientific Institute, IRCCS Eugenio Medea, Bioengineering Lab, Lecco, Italy
| | - Chiara Cantiani
- Scientific Institute, IRCCS Eugenio Medea, Child Psychopathology Unit, Lecco, Italy
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Valentina Riva
- Scientific Institute, IRCCS Eugenio Medea, Child Psychopathology Unit, Lecco, Italy
| | - Massimo Molteni
- Scientific Institute, IRCCS Eugenio Medea, Child Psychopathology Unit, Lecco, Italy
| | - Gianluigi Reni
- Scientific Institute, IRCCS Eugenio Medea, Bioengineering Lab, Lecco, Italy
| | - Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
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Diao Q, Liu J, Zhang X. Enhanced positive functional connectivity strength in left-sided chronic subcortical stroke. Brain Res 2020; 1733:146727. [PMID: 32061738 DOI: 10.1016/j.brainres.2020.146727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 02/08/2020] [Accepted: 02/13/2020] [Indexed: 01/20/2023]
Abstract
Patients with stroke often exhibit evidence of abnormal functional connectivity (FC). However, whether and how anatomical distance affects FC at rest remains unclear in patients with chronic subcortical stroke. Eighty-six patients with chronic (more than six months post-onset) subcortical stroke (44 left-sided patients and 42 right-sided patients) with different degrees of functional recovery, and 75 matched healthy controls underwent resting-state functional magnetic resonance imaging scanning. Positive functional connectivity strength (FCS) was computed for each voxel in the brain using a data-driven whole-brain resting state FCS method, which was further divided into short- and long-range FCS. Compared with healthy controls, patients with left-sided infarctions exhibited stronger global- and long-range FCS in the left sensorimotor cortex (SMC), and no significant intergroup difference was found for short-range FCS. No significant differences were found between the patients with right-sided infarctions and healthy controls for global, long- and short-range FCS. These findings suggested that the positive FCS alteration was connection-distance dependent within patients with left-sided chronic subcortical stroke. Also, a positive correlation was found between the FCS in the left SMC and the accuracy of the Flanker test, reflecting a compensatory FCS alteration for altered attention and executive function abilities exhibited by those with left-sided stroke.
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Affiliation(s)
- Qingqing Diao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingchun Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Xuejun Zhang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China.
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Lee TH, Kim SH, Katz B, Mather M. The Decline in Intrinsic Connectivity Between the Salience Network and Locus Coeruleus in Older Adults: Implications for Distractibility. Front Aging Neurosci 2020; 12:2. [PMID: 32082136 PMCID: PMC7004957 DOI: 10.3389/fnagi.2020.00002] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 01/08/2020] [Indexed: 11/26/2022] Open
Abstract
We examined functional connectivity between the locus coeruleus (LC) and the salience network in healthy young and older adults to investigate why people become more prone to distraction with age. Recent findings suggest that the LC plays an important role in focusing processing on salient or goal-relevant information from multiple incoming sensory inputs (Mather et al., 2016). We hypothesized that the connection between LC and the salience network declines in older adults, and therefore the salience network fails to appropriately filter out irrelevant sensory signals. To examine this possibility, we used resting-state-like fMRI data, in which all task-related activities were regressed out (Fair et al., 2007; Elliott et al., 2019) and performed a functional connectivity analysis based on the time-course of LC activity. Older adults showed reduced functional connectivity between the LC and salience network compared with younger adults. Additionally, the salience network was relatively more coupled with the frontoparietal network than the default-mode network in older adults compared with younger adults, even though all task-related activities were regressed out. Together, these findings suggest that reduced interactions between LC and the salience network impairs the ability to prioritize the importance of incoming events, and in turn, the salience network fails to initiate network switching (e.g., Menon and Uddin, 2010; Uddin, 2015) that would promote further attentional processing. A chronic lack of functional connection between LC and salience network may limit older adults' attentional and executive control resources.
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Affiliation(s)
- Tae-Ho Lee
- Department of Psychology, Virginia Tech, Blacksburg, VA, United States
| | - Sun Hyung Kim
- Department of Psychiatry, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Benjamin Katz
- Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, United States
| | - Mara Mather
- Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
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Abnormal dynamics of functional connectivity density in children with benign epilepsy with centrotemporal spikes. Brain Imaging Behav 2020; 13:985-994. [PMID: 29956102 DOI: 10.1007/s11682-018-9914-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Converging evidence has shown the link between benign epilepsy with centrotemporal spikes (BECTS) and abnormal functional connectivity among distant brain regions. However, prior research in BECTS has not examined the dynamic changes in functional connectivity as networks form. We combined functional connectivity density (FCD) mapping and sliding windows correlation analyses, to fully capture the functional dynamics in patients with respect to the presence of interictal epileptic discharges (IEDs). Resting-state fMRI was performed in 43 BECTS patients and 28 healthy controls (HC). Patients were further classified into two subgroups, namely, IED (n = 20) and non-IED (n = 23) depending on the simultaneous EEG-fMRI recordings. The global dynamic FCD (dFCD) was measured using sliding window correlation. Then we quantified dFCD variability using their standard deviation. Compared with HC, patients with and without IEDs both showed invariable dFCD (decreased) among the orbital fontal cortex, anterior cingulate cortex and striatum, as well as variable dFCD (increased) in the posterior default mode network (P < 0.05, AlphaSim corrected). Correlation analysis indicated that the variable dFCD in precuneus was related to seizure onset age (P < 0.05, uncorrected). BECTS with IEDs showed variable dFCD in regions related to the typical seizure semiology. The abnormal patterns of fluctuating FCD in BECTS suggest that both active and chronic epileptic state may contribute to altered dynamics of functional connectivity associated with cognitive disturbances and developmental alterations. These findings highlight the importance of considering fluctuating dynamic neural communication among brain systems to deepen our understanding of epilepsy diseases.
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Sousa SS, Sampaio A, Marques P, López-Caneda E, Gonçalves ÓF, Crego A. Functional and structural connectivity of the executive control network in college binge drinkers. Addict Behav 2019; 99:106009. [PMID: 31487578 DOI: 10.1016/j.addbeh.2019.05.033] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 05/29/2019] [Accepted: 05/30/2019] [Indexed: 01/06/2023]
Abstract
Binge Drinking (BD) is a pattern of excessive alcohol consumption highly prevalent among college students, and has been associated with structural and functional alterations of brain networks. Recent advances in the resting-state connectivity analysis have boosted the research of the network-level connectivity disturbances associated with many psychiatric and neurological disorders, including addiction. Accordingly, atypical functional connectivity patterns in resting-state networks such as the Executive Control Network (ECN) have been found in substance users and alcohol-dependent individuals. In this study, we assessed for the first time the ECN functional and structural connectivity in a group of 34 college students, 20 (10 women) binge drinkers (BDs) in comparison with a group of 14 (8 women) alcohol abstinent controls (AACs). Overall, our findings documented increased resting-state functional connectivity (rsFC) in the BDs left middle frontal cortex of the left ECN in comparison to the AACs, while no structural connectivity differences were observed between groups. Pearson correlations revealed a positive association between the left middle frontal gyrus rsFC and the frequency of BD episodes per month, in the BD group. These findings suggest that maintaining a pattern of acute and intermittent alcohol consumption during important stages of brain development, as the transition from adolescence to adulthood, is associated with impaired ECN rsFC despite no group differences being yet noticed in the ECN structural connectivity.
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Affiliation(s)
- Sónia S Sousa
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal.
| | - Adriana Sampaio
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Eduardo López-Caneda
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
| | - Óscar F Gonçalves
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal; Spaulding Neuromodulation Center, Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Massachusetts General Hospital. Harvard Medical School, Charlestown campus: 79/96 13th Street, Charlestown, MA 02129, USA; Department of Applied Psychology, Bouvé College of Health Sciences, Northeastern University, 404 International Village, Boston, MA 02115, USA
| | - Alberto Crego
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
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Chan NK, Kim J, Shah P, Brown EE, Plitman E, Carravaggio F, Iwata Y, Gerretsen P, Graff-Guerrero A. Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review. Schizophr Res 2019; 211:10-20. [PMID: 31331784 DOI: 10.1016/j.schres.2019.07.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 06/23/2019] [Accepted: 07/11/2019] [Indexed: 01/09/2023]
Abstract
BACKGROUND Treatment-resistant schizophrenia (TRS) and treatment-responsive schizophrenia may exhibit distinct pathophysiology. Several functional magnetic resonance imaging (fMRI) studies have used resting-state functional connectivity analyses (rs-FC) in TRS patients to identify markers of treatment resistance. However, to date, existing findings have not been systematically evaluated. METHODS A systematic literature search using Embase, MEDLINE, PsycINFO, ProQuest, PUBMED, and Scopus was performed. The query sought fMRI articles investigating rs-FC in treatment response or resistance in patients with schizophrenia. Only studies that examined treatment response, operationalized as the explicit categorization of patients by their response to antipsychotic medication, were considered eligible. Pairwise comparisons between patient groups and controls were extracted from each study. RESULTS The search query identified 159 records. Ten studies met inclusion criteria. Five studies examined not TRS (NTRS), and 8 studies examined TRS. Differences in rs-FC analysis methodology precluded direct comparisons between studies. However, disruptions in areas involved in visual and auditory information processing were implicated in both patients with TRS and NTRS. Changes in connectivity with sensorimotor network areas tended to appear in the context of TRS but not NTRS. Moreover, there was some indication that this connectivity could be affected by clozapine. CONCLUSIONS Functional connectivity may provide clinically meaningful biomarkers of treatment response and resistance in schizophrenia. Studies generally identified similar areas of disruption, though methodological differences largely precluded direct comparison between disruption effects. Implementing data sharing as standard practice will allow future reviews and meta-analyses to identify rs-FC correlates of TRS.
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Affiliation(s)
- Nathan K Chan
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Julia Kim
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Parita Shah
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Eric E Brown
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Geriatric Mental Health Division, CAMH, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, University of Toronto, Toronto, Ontario, Canada
| | - Eric Plitman
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Fernando Carravaggio
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Yusuke Iwata
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Philip Gerretsen
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Geriatric Mental Health Division, CAMH, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, University of Toronto, Toronto, Ontario, Canada
| | - Ariel Graff-Guerrero
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Geriatric Mental Health Division, CAMH, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, CAMH, University of Toronto, Toronto, Ontario, Canada.
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49
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Abstract
Functional MRI (fMRI) is currently used for pre-surgical planning, but is often limited to information on the motor and language systems. Resting state fMRI can provide more information on multiple other networks to the neurosurgeon and neuroradiologist; however, currently, these networks are not well known among clinicians. The purpose of this manuscript is to provide an introduction to these networks for the clinician and to discuss how they could be used in the future for precise and individualized surgical planning. We provide a short introduction to resting state fMRI and discuss multiple currently accepted resting state networks with a review of the literature. We review the characteristics and function of multiple somatosensory, association, and other networks. We discuss the concept of critical nodes in the brain and how the neurosurgeon can use this information to individually customize patient care. Although further research is necessary, future application of pre-surgical planning will require consideration of networks other than just motor and language in order to minimize post-surgical morbidity and customize patient care.
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Affiliation(s)
- Benjamin A Seitzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO
| | | | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO
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50
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Benito-León J, Sanz-Morales E, Melero H, Louis ED, Romero JP, Rocon E, Malpica N. Graph theory analysis of resting-state functional magnetic resonance imaging in essential tremor. Hum Brain Mapp 2019; 40:4686-4702. [PMID: 31332912 DOI: 10.1002/hbm.24730] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/05/2019] [Accepted: 07/08/2019] [Indexed: 11/10/2022] Open
Abstract
Essential tremor (ET) is a neurological disease with both motor and nonmotor manifestations; however, little is known about its underlying brain basis. Furthermore, the overall organization of the brain network in ET remains largely unexplored. We investigated the topological properties of brain functional network, derived from resting-state functional magnetic resonance imaging (MRI) data, in 23 ET patients versus 23 healthy controls. Graph theory analysis was used to assess the functional network organization. At the global level, the functional network of ET patients was characterized by lower small-worldness values than healthy controls-less clustered functionality of the brain. At the regional level, compared with the healthy controls, ET patients showed significantly higher values of global efficiency, cost and degree, and a shorter average path length in the left inferior frontal gyrus (pars opercularis), right inferior temporal gyrus (posterior division and temporo-occipital part), right inferior lateral occipital cortex, left paracingulate, bilateral precuneus bilaterally, left lingual gyrus, right hippocampus, left amygdala, nucleus accumbens bilaterally, and left middle temporal gyrus (posterior part). In addition, ET patients showed significant higher local efficiency and clustering coefficient values in frontal medial cortex bilaterally, subcallosal cortex, posterior cingulate cortex, parahippocampal gyri bilaterally (posterior division), right lingual gyrus, right cerebellar flocculus, right postcentral gyrus, right inferior semilunar lobule of cerebellum and culmen of vermis. Finally, the right intracalcarine cortex and the left orbitofrontal cortex showed a shorter average path length in ET patients, while the left frontal operculum and the right planum polare showed a higher betweenness centrality in ET patients. In conclusion, the efficiency of the overall brain functional network in ET is disrupted. Further, our results support the concept that ET is a disorder that disrupts widespread brain regions, including those outside of the brain regions responsible for tremor.
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Affiliation(s)
- Julián Benito-León
- Department of Neurology, University Hospital 12 de Octubre, Madrid, Spain.,Center of Biomedical Network Research on Neurodegenerative Diseases (CIBERNED), Madrid, Spain.,Department of Medicine, Faculty of Medicine, Complutense University, Madrid, Spain
| | - Emilio Sanz-Morales
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
| | - Helena Melero
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
| | - Elan D Louis
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, Connecticut.,Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, Connecticut.,Center for Neuroepidemiology and Clinical Neurological Research, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Juan P Romero
- Faculty of Biosanitary Sciences, Francisco de Vitoria University, Madrid, Spain.,Brain Damage Unit, Hospital Beata Maria Ana, Madrid, Spain
| | - Eduardo Rocon
- Neural and Cognitive Engineering group, Center for Automation and Robotics (CAR) CSIC-UPM, Arganda del Rey, Spain
| | - Norberto Malpica
- Medical Image Analysis Laboratory (LAIMBIO), Rey Juan Carlos University, Madrid, Spain
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