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Huang T, Hua Q, Zhao X, Tian W, Cao H, Xu W, Sun J, Zhang L, Wang K, Ji GJ. Abnormal functional lateralization and cooperation in bipolar disorder are associated with neurotransmitter and cellular profiles. J Affect Disord 2024; 369:970-977. [PMID: 39447972 DOI: 10.1016/j.jad.2024.10.108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 10/18/2024] [Accepted: 10/21/2024] [Indexed: 10/26/2024]
Abstract
BACKGROUND Hemispheric lateralization and cooperation are essential for efficient brain function, and aberrations in both have been found in psychiatric disorders such as schizophrenia. This study investigated alterations in hemispheric lateralization and cooperation among patients with bipolar disorder (BD) and associations with neurotransmitter and cell-type density distributions to identify potential molecular and cellular pathomechanisms. METHODS Sixty-seven BD patients and 127 healthy controls (HCs) were examined by resting-state functional MRI (rs-fMRI). Whole-brain maps of the autonomy index (AI) and connectivity between functionally homotopic voxels (CFH) were constructed to reveal BD-specific changes in brain functional lateralization and interhemispheric cooperation, respectively. Spatial associations of regional AI and CFH abnormalities with neurotransmitter and cell-type density distributions were examined by correlation analyses. RESULTS Bipolar disorder patients exhibited higher AI values in left superior parietal gyrus, cerebellar right Crus I, and cerebellar right Crus II, and these regional abnormalities were associated with the relative densities (proportions) of oligodendrocyte precursor cells and microglia. Patients also exhibited lower CFH values in right inferior parietal gyrus, bilateral middle occipital gyrus, left postcentral gyrus, and bilateral cerebellar crus II, and these regional abnormalities were associated with the densities of serotonin 1A and dopamine D2 receptors, oligodendrocyte precursor cells, astrocytes, and neurons. CONCLUSIONS These findings indicate that abnormal functional lateralization and cooperation in BD with potential molecular and cellular basis.
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Affiliation(s)
- Tongqing Huang
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Qiang Hua
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Xiya Zhao
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Weichao Tian
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Hai Cao
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Wenqiang Xu
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Li Zhang
- Department of Psychiatry, Affiliated Psychological Hospital of Anhui Medical University, Hefei, Anhui Province, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Anhui Institute of Translational Medicine, Hefei, China.
| | - Gong-Jun Ji
- School of Mental Health and Psychological Sciences, Anhui Medical University, 81 Meishan Rd, Hefei 230032, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China; Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China; Anhui Institute of Translational Medicine, Hefei, China.
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Wu Y, Gao M, Lv L, Yan Y, Gao L, Geng Z, Zhou S, Zhu W, Yu Y, Tian Y, Ji G, Hu P, Wu X, Wang K. Brain functional specialization and cooperation in Alzheimer's disease. Brain Behav 2024; 14:e3550. [PMID: 38841739 PMCID: PMC11154812 DOI: 10.1002/brb3.3550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/10/2024] [Accepted: 04/13/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Cerebral specialization and interhemispheric cooperation are two vital features of the human brain. Their dysfunction may be associated with disease progression in patients with Alzheimer's disease (AD), which is featured as progressive cognitive degeneration and asymmetric neuropathology. OBJECTIVE This study aimed to examine and define two inherent properties of hemispheric function in patients with AD by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS Sixty-four clinically diagnosed AD patients and 52 age- and sex-matched cognitively normal subjects were recruited and underwent MRI and clinical evaluation. We calculated and compared brain specialization (autonomy index, AI) and interhemispheric cooperation (connectivity between functionally homotopic voxels, CFH). RESULTS In comparison to healthy controls, patients with AD exhibited enhanced AI in the left middle occipital gyrus. This increase in specialization can be attributed to reduced functional connectivity in the contralateral region, such as the right temporal lobe. The CFH of the bilateral precuneus and prefrontal areas was significantly decreased in AD patients compared to controls. Imaging-cognitive correlation analysis indicated that the CFH of the right prefrontal cortex was marginally positively related to the Montreal Cognitive Assessment score in patients and the Auditory Verbal Learning Test score. Moreover, taking abnormal AI and CFH values as features, support vector machine-based classification achieved good accuracy, sensitivity, specificity, and area under the curve by leave-one-out cross-validation. CONCLUSION This study suggests that individuals with AD have abnormal cerebral specialization and interhemispheric cooperation. This provides new insights for further elucidation of the pathological mechanisms of AD.
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Affiliation(s)
- Yue Wu
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Department of Psychology and Sleep Medicinethe Second Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
| | - Manman Gao
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Lingling Lv
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Yibing Yan
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Liying Gao
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Zhi Geng
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Shanshan Zhou
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental HealthHefeiAnhui ProvinceChina
| | - Wanqiu Zhu
- Department of Radiologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
| | - Yongqiang Yu
- Department of Radiologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
| | - Yanghua Tian
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental HealthHefeiAnhui ProvinceChina
- Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefeiAnhui ProvinceChina
- The School of Mental Health and Psychological SciencesAnhui Medical UniversityHefeiAnhui ProvinceChina
| | - Gong‐Jun Ji
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental HealthHefeiAnhui ProvinceChina
- The School of Mental Health and Psychological SciencesAnhui Medical UniversityHefeiAnhui ProvinceChina
| | - Panpan Hu
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental HealthHefeiAnhui ProvinceChina
| | - Xingqi Wu
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
| | - Kai Wang
- Department of Neurologythe First Affiliated Hospital of Anhui Medical UniversityHefeiAnhui ProvinceChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiAnhui ProvinceChina
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental HealthHefeiAnhui ProvinceChina
- Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefeiAnhui ProvinceChina
- The School of Mental Health and Psychological SciencesAnhui Medical UniversityHefeiAnhui ProvinceChina
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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He K, Hua Q, Li Q, Zhang Y, Yao X, Yang Y, Xu W, Sun J, Wang L, Wang A, Ji GJ, Wang K. Abnormal interhemispheric functional cooperation in schizophrenia follows the neurotransmitter profiles. J Psychiatry Neurosci 2023; 48:E452-E460. [PMID: 38123242 PMCID: PMC10743641 DOI: 10.1503/jpn.230037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Interhemispheric cooperation is one of the most prominent functional architectures of the human brain. In patients with schizophrenia, interhemispheric cooperation deficits have been reported using increasingly powerful neurobehavioural and neuroimaging measures. However, these methods rely in part on the assumption of anatomic symmetry between hemispheres. In the present study, we explored interhemispheric cooperation deficits in schizophrenia using a newly developed index, connectivity between functionally homotopic voxels (CFH), which is unbiased by hemispheric asymmetry. METHODS Patients with schizophrenia and age- and sexmatched healthy controls underwent multimodal MRI, and whole-brain CFH maps were constructed for comparison between groups. We examined the correlations of differing CFH values between the schizophrenia and control groups using various neurotransmitter receptor and transporter densities. RESULTS We included 86 patients with schizophrenia and 86 matched controls in our analysis. Patients with schizophrenia showed significantly lower CFH values in the frontal lobes, left postcentral gyrus and right inferior temporal gyrus, and significantly greater CFH values in the right caudate nucleus than healthy controls. Moreover, the differing CFH values in patients with schizophrenia were significantly correlated with positive symptom score and illness duration. Functional connectivity within frontal lobes was significantly reduced at the voxel cluster level compared with healthy controls. Finally, the abnormal CFH map of patients with schizophrenia was spatially associated with the densities of the dopamine D1 and D2 receptors, fluorodopa, dopamine transporter, serotonin transporter and acetylcholine transporter. CONCLUSION Regional abnormalities in interhemispheric cooperation may contribute to the clinical symptoms of schizophrenia. These CFH abnormalities may be associated with dysfunction in neurotransmitter systems strongly implicated in schizophrenia.
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Affiliation(s)
- Kongliang He
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Qiang Hua
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Qianqian Li
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Yan Zhang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Xiaoqing Yao
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Yinian Yang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Wenqiang Xu
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Jinmei Sun
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Lu Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Anzhen Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Gong-Jun Ji
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
| | - Kai Wang
- From the Affiliated Psychological Hospital of Anhui Medical University, Hefei, China (He, A. Wang); the Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China (He, Hua, Yao, Sun, L. Wang, Ji, K. Wang); the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China (He, Zhang, Yang, Xu, A. Wang, Ji, K. Wang); the Hefei Fourth People's Hospital, Hefei, China (He, A. Wang); the Anhui Mental Health Center, Hefei, China (He, A. Wang); the Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China (Li); the Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China (Hua, Li, Zhang, Yang, Xu, Sun, L. Wang, Ji, K. Wang); the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China (K. Wang); and the Anhui Institute of Translational Medicine, Hefei, China (K. Wang)
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5
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Conrad BN, Pollack C, Yeo DJ, Price GR. Structural and functional connectivity of the inferior temporal numeral area. Cereb Cortex 2022; 33:6152-6170. [PMID: 36587366 PMCID: PMC10183753 DOI: 10.1093/cercor/bhac492] [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: 01/31/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 01/02/2023] Open
Abstract
A growing body of evidence suggests that in adults, there is a spatially consistent "inferior temporal numeral area" (ITNA) in the occipitotemporal cortex that appears to preferentially process Arabic digits relative to non-numerical symbols and objects. However, very little is known about why the ITNA is spatially segregated from regions that process other orthographic stimuli such as letters, and why it is spatially consistent across individuals. In the present study, we used diffusion-weighted imaging and functional magnetic resonance imaging to contrast structural and functional connectivity between left and right hemisphere ITNAs and a left hemisphere letter-preferring region. We found that the left ITNA had stronger structural and functional connectivity than the letter region to inferior parietal regions involved in numerical magnitude representation and arithmetic. Between hemispheres, the left ITNA showed stronger structural connectivity with the left inferior frontal gyrus (Broca's area), while the right ITNA showed stronger structural connectivity to the ipsilateral inferior parietal cortex and stronger functional coupling with the bilateral IPS. Based on their relative connectivity, our results suggest that the left ITNA may be more readily involved in mapping digits to verbal number representations, while the right ITNA may support the mapping of digits to quantity representations.
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Affiliation(s)
- Benjamin N Conrad
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA
| | - Courtney Pollack
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA
| | - Darren J Yeo
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA.,Division of Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639818
| | - Gavin R Price
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA.,Department of Psychology, University of Exeter, Washington Singer Building Perry Road, Exeter, EX4 4QG, United Kingdom
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6
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Functional cortical associations and their intraclass correlations and heritability as revealed by the fMRI Human Connectome Project. Exp Brain Res 2022; 240:1459-1469. [PMID: 35292842 DOI: 10.1007/s00221-022-06346-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 03/04/2022] [Indexed: 11/04/2022]
Abstract
We report on the functional connectivity (FC), its intraclass correlation (ICC), and heritability among 70 areas of the human cerebral cortex. FC was estimated as the Pearson correlation between averaged prewhitened Blood Oxygenation Level-Dependent time series of cortical areas in 988 young adult participants in the Human Connectome Project. Pairs of areas were assigned to three groups, namely homotopic (same area in the two hemispheres), ipsilateral (both areas in the same hemisphere), and heterotopic (nonhomotopic areas in different hemispheres). ICC for each pair of areas was computed for six genetic groups, namely monozygotic (MZ) twins, dizygotic (DZ) twins, singleton siblings of MZ twins (MZsb), singleton siblings of DZ twins (DZsb), non-twin siblings (SB), and unrelated individuals (UNR). With respect to FC, we found the following. (a) Homotopic FC was stronger than ipsilateral and heterotopic FC; (b) average FCs of left and right cortical areas were highly and positively correlated; and (c) FC varied in a systematic fashion along the anterior-posterior and inferior-superior dimensions, such that it increased from anterior to posterior and from inferior to superior. With respect to ICC, we found the following. (a) Homotopic ICC was significantly higher than ipsilateral and heterotopic ICC, but the latter two did not differ significantly from each other; (b) ICC was highest for MZ twins; (c) ICC of DZ twins was significantly lower than that of the MZ twins and higher than that of the three sibling groups (MZsb, DZsb, SB); and (d) ICC was close to zero for UNR. Finally, with respect to heritability, it was highest for homotopic areas, followed by ipsilateral, and heterotopic; however, it did not differ statistically significantly from each other.
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7
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Gonzalez Alam TRDJ, Mckeown BLA, Gao Z, Bernhardt B, Vos de Wael R, Margulies DS, Smallwood J, Jefferies E. A tale of two gradients: differences between the left and right hemispheres predict semantic cognition. Brain Struct Funct 2021; 227:631-654. [PMID: 34510282 PMCID: PMC8844158 DOI: 10.1007/s00429-021-02374-w] [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: 08/27/2021] [Indexed: 01/21/2023]
Abstract
Decomposition of whole-brain functional connectivity patterns reveals a principal gradient that captures the separation of sensorimotor cortex from heteromodal regions in the default mode network (DMN). Functional homotopy is strongest in sensorimotor areas, and weakest in heteromodal cortices, suggesting there may be differences between the left and right hemispheres (LH/RH) in the principal gradient, especially towards its apex. This study characterised hemispheric differences in the position of large-scale cortical networks along the principal gradient, and their functional significance. We collected resting-state fMRI and semantic, working memory and non-verbal reasoning performance in 175 + healthy volunteers. We then extracted the principal gradient of connectivity for each participant, tested which networks showed significant hemispheric differences on the gradient, and regressed participants’ behavioural efficiency in tasks outside the scanner against interhemispheric gradient differences for each network. LH showed a higher overall principal gradient value, consistent with its role in heteromodal semantic cognition. One frontotemporal control subnetwork was linked to individual differences in semantic cognition: when it was nearer heteromodal DMN on the principal gradient in LH, participants showed more efficient semantic retrieval—and this network also showed a strong hemispheric difference in response to semantic demands but not working memory load in a separate study. In contrast, when a dorsal attention subnetwork was closer to the heteromodal end of the principal gradient in RH, participants showed better visual reasoning. Lateralization of function may reflect differences in connectivity between control and heteromodal regions in LH, and attention and visual regions in RH.
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Affiliation(s)
| | | | - Zhiyao Gao
- Department of Psychology, University of York, York, UK
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) and Université de Paris, INCC UMR 8002, Paris, France
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8
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Sun J, Gao X, Hua Q, Du R, Liu P, Liu T, Yang J, Qiu B, Ji GJ, Hu P, Wang K. Brain functional specialization and cooperation in Parkinson's disease. Brain Imaging Behav 2021; 16:565-573. [PMID: 34427879 DOI: 10.1007/s11682-021-00526-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 11/24/2022]
Abstract
Cerebral specialization and inter-hemispheric cooperation are two of the most prominent functional architectures of the human brain. Their dysfunctions may be related to pathophysiological changes in patients with Parkinson's disease (PD), who are characterized by unbalanced onset and progression of motor symptoms. This study aimed to characterize the two intrinsic architectures of hemispheric functions in PD using resting-state functional magnetic resonance imaging. Seventy idiopathic PD patients and 70 age-, sex-, and education-matched healthy subjects were recruited. All participants underwent magnetic resonance image scanning and clinical evaluations. The cerebral specialization (Autonomy index, AI) and inter-hemispheric cooperation (Connectivity between Functionally Homotopic voxels, CFH) were calculated and compared between groups. Compared with healthy controls, PD patients showed stronger AI in the left angular gyrus. Specifically, this difference in specialization resulted from increased functional connectivity (FC) of the ipsilateral areas (e.g., the left prefrontal area), and decreased FC in the contralateral area (e.g., the right supramarginal gyrus). Imaging-cognitive correlation analysis indicated that these connectivity were positively related to the score of Montreal Cognitive Assessment in PD patients. CFH between the bilateral sensorimotor regions was significantly decreased in PD patients compared with controls. No significant correlation between CFH and cognitive scores was found in PD patients. This study illustrated a strong leftward specialization but weak inter-hemispheric coordination in PD patients. It provided new insights to further clarify the pathological mechanism of PD.
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Affiliation(s)
- Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Xiaoran Gao
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Qiang Hua
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Rongrong Du
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Pingping Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Tingting Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China.,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Jinying Yang
- Laboratory Center for Information Science, University of Science and Technology of China, Hefei, China
| | - Bensheng Qiu
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Gong-Jun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China. .,School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China. .,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China. .,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China. .,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China. .,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China. .,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China. .,Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China. .,Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230000, China.
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9
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Jin X, Liang X, Gong G. Functional Integration Between the Two Brain Hemispheres: Evidence From the Homotopic Functional Connectivity Under Resting State. Front Neurosci 2020; 14:932. [PMID: 33122984 PMCID: PMC7566168 DOI: 10.3389/fnins.2020.00932] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 08/11/2020] [Indexed: 12/12/2022] Open
Abstract
Functional integration among neural units is one of the fundamental principles in brain organization that could be examined using resting-state functional connectivity (rs-FC). Interhemispheric functional integration plays a critical role in human cognition. Homotopic functional connectivity (HoFC) under resting state provide an avenue to investigate functional integration between the two brain hemispheres, which can improve the present understanding of how interhemispheric interactions affect cognitive processing. In this review, we summarize the progress of HoFC studies under resting state and highlight how these findings have enhanced our understanding of interhemispheric functional organization of the human brain. Future studies are encouraged to address particular methodological issues and to further ascertain behavioral correlates, brain disease's modulation, task influence, and genetic basis of HoFC.
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Affiliation(s)
- Xinhu Jin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xinyu Liang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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10
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Avelar-Pereira B, Bäckman L, Wåhlin A, Nyberg L, Salami A. Increased functional homotopy of the prefrontal cortex is associated with corpus callosum degeneration and working memory decline. Neurobiol Aging 2020; 96:68-78. [PMID: 32949903 DOI: 10.1016/j.neurobiolaging.2020.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 06/29/2020] [Accepted: 08/10/2020] [Indexed: 11/18/2022]
Abstract
Functional homotopy reflects the link between spontaneous activity in a voxel and its counterpart in the opposite hemisphere. Alterations in homotopic functional connectivity (FC) are seen in normal aging, with highest and lowest homotopy being present in sensory-motor and higher-order regions, respectively. Homotopic FC relates to underlying structural connections, but its neurobiological underpinnings remain unclear. The genu of the corpus callosum joins symmetrical parts of the prefrontal cortex (PFC) and is susceptible to age-related degeneration, suggesting that PFC homotopic connectivity is linked to changes in white-matter integrity. We investigated homotopic connectivity changes and whether these were associated with white-matter integrity in 338 individuals. In addition, we examined whether PFC homotopic FC was related to changes in the genu over 10 years and working memory over 5 years. There were increases and decreases in functional homotopy, with the former being prevalent in subcortical and frontal regions. Increased PFC homotopic FC was partially driven by structural degeneration and negatively associated with working memory, suggesting that it reflects detrimental age-related changes.
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Affiliation(s)
- Bárbara Avelar-Pereira
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.
| | - Lars Bäckman
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden; Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden; Department of Radiation Sciences, Umeå University, Umeå, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
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11
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Degrees of lateralisation in semantic cognition: Evidence from intrinsic connectivity. Neuroimage 2019; 202:116089. [DOI: 10.1016/j.neuroimage.2019.116089] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/27/2019] [Accepted: 08/08/2019] [Indexed: 11/15/2022] Open
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12
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Lu KH, Jeong JY, Wen H, Liu Z. Spontaneous activity in the visual cortex is organized by visual streams. Hum Brain Mapp 2017; 38:4613-4630. [PMID: 28608643 DOI: 10.1002/hbm.23687] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 06/05/2017] [Accepted: 06/07/2017] [Indexed: 12/12/2022] Open
Abstract
Large-scale functional networks have been extensively studied using resting state functional magnetic resonance imaging (fMRI). However, the pattern, organization, and function of fine-scale network activity remain largely unknown. Here, we characterized the spontaneously emerging visual cortical activity by applying independent component (IC) analysis to resting state fMRI signals exclusively within the visual cortex. In this subsystem scale, we observed about 50 spatially ICs that were reproducible within and across subjects, and analyzed their spatial patterns and temporal relationships to reveal the intrinsic parcellation and organization of the visual cortex. The resulting visual cortical parcels were aligned with the steepest gradient of cortical myelination, and were organized into functional modules segregated along the dorsal/ventral pathways and foveal/peripheral early visual areas. Cortical distance could partly explain intra-hemispherical functional connectivity, but not interhemispherical connectivity; after discounting the effect of anatomical affinity, the fine-scale functional connectivity still preserved a similar visual-stream-specific modular organization. Moreover, cortical retinotopy, folding, and cytoarchitecture impose limited constraints to the organization of resting state activity. Given these findings, we conclude that spontaneous activity patterns in the visual cortex are primarily organized by visual streams, likely reflecting feedback network interactions. Hum Brain Mapp 38:4613-4630, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Kun-Han Lu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Jun Young Jeong
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Haiguang Wen
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana
| | - Zhongming Liu
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana
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13
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Tobyne SM, Boratyn D, Johnson JA, Greve DN, Mainero C, Klawiter EC. A surface-based technique for mapping homotopic interhemispheric connectivity: Development, characterization, and clinical application. Hum Brain Mapp 2016; 37:2849-68. [PMID: 27219660 DOI: 10.1002/hbm.23214] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 03/29/2016] [Accepted: 04/01/2016] [Indexed: 02/01/2023] Open
Abstract
The functional organization of the human brain consists of a high degree of connectivity between interhemispheric homologous regions. The degree of homotopic organization is known to vary across the cortex and homotopic connectivity is high in regions that share cross-hemisphere structural connections or are activated by common input streams (e.g., the visual system). Damage to one or both regions, as well as damage to the connections between homotopic regions, could disrupt this functional organization. Here were introduce and test a computationally efficient technique, surface-based homotopic interhermispheric connectivity (sHIC), that leverages surface-based registration and processing techniques in an attempt to improve the spatial specificity and accuracy of cortical interhemispheric connectivity estimated with resting state functional connectivity. This technique is shown to be reliable both within and across subjects. sHIC is also characterized in a dataset of nearly 1000 subjects. We confirm previous results showing increased interhemispheric connectivity in primary sensory regions, and reveal a novel rostro-caudal functionally defined network level pattern of sHIC across the brain. In addition, we demonstrate a structural-functional relationship between sHIC and atrophy of the corpus callosum in multiple sclerosis (r = 0.2979, p = 0.0461). sHIC presents as a sensitive and reliable measure of cortical homotopy that may prove useful as a biomarker in neurologic disease. Hum Brain Mapp 37:2849-2868, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sean M Tobyne
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Daria Boratyn
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Douglas N Greve
- Athinoula a. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Caterina Mainero
- Athinoula a. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
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14
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Abstract
Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. Significance statement: Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience.
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15
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Joliot M, Jobard G, Naveau M, Delcroix N, Petit L, Zago L, Crivello F, Mellet E, Mazoyer B, Tzourio-Mazoyer N. AICHA: An atlas of intrinsic connectivity of homotopic areas. J Neurosci Methods 2015. [DOI: 10.1016/j.jneumeth.2015.07.013] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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16
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Tungaraza RL, Mehta SH, Haynor DR, Grabowski TJ. Anatomically informed metrics for connectivity-based cortical parcellation from diffusion MRI. IEEE J Biomed Health Inform 2015; 19:1375-83. [PMID: 26080389 DOI: 10.1109/jbhi.2015.2444917] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Connectivity information derived from diffusion MRI can be used to parcellate the cerebral cortex into anatomically and functionally meaningful subdivisions. Acquisition and processing parameters can significantly affect parcellation results, and there is no consensus on best practice protocols. We propose a novel approach for evaluating parcellation based on measuring the degree to which parcellation conforms to known principles of brain organization, specifically cortical field homogeneity and interhemispheric homology. The proposed metrics are well behaved on morphologically generated whole-brain parcels, where they correctly identify contralateral homologies and give higher scores to anatomically versus arbitrarily generated parcellations. The measures show that individual cortical fields have characteristic connectivity profiles that are compact and separable, and that the topological arrangement of such fields is strongly conserved between hemispheres and individuals. The proposed metrics can be used to evaluate the quality of parcellations at the subject and group levels and to improve acquisition and data processing for connectivity-based cortical parcellation.
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17
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Stable long-range interhemispheric coordination is supported by direct anatomical projections. Proc Natl Acad Sci U S A 2015; 112:6473-8. [PMID: 25941372 DOI: 10.1073/pnas.1503436112] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.
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18
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Butt OH, Benson NC, Datta R, Aguirre GK. Hierarchical and homotopic correlations of spontaneous neural activity within the visual cortex of the sighted and blind. Front Hum Neurosci 2015; 9:25. [PMID: 25713519 PMCID: PMC4322716 DOI: 10.3389/fnhum.2015.00025] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 01/12/2015] [Indexed: 10/31/2022] Open
Abstract
Spontaneous neural activity within visual cortex is synchronized by both monosynaptic, hierarchical connections between visual areas and indirect, network-level activity. We examined the interplay of hierarchical and network connectivity in human visual cortex by measuring the organization of spontaneous neural signals within the visual cortex in total darkness using functional magnetic resonance imaging (fMRI). Twenty-five blind (14 congenital and 11 postnatal) participants with equally severe vision loss and 22 sighted subjects were studied. An anatomical template based on cortical surface topology was used for all subjects to identify the quarter-field components of visual areas V1-V3, and assign retinotopic organization. Cortical visual areas that represent the same quadrant of the visual field were considered to have a hierarchical relationship, while the spatially separated quarters of the same visual area were considered homotopic. Blindness was found to enhance correlations between hierarchical cortical areas as compared to indirect, homotopic areas at both the level of visual areas (p = 0.000031) and fine, retinotopic scale (p = 0.0024). A specific effect of congenital, but not postnatal, blindness was to further broaden the cortico-cortico connections between hierarchical visual areas (p = 0.0029). This finding is consistent with animal studies that observe a broadening of axonal terminal arborization when the visual cortex is deprived of early input. We therefore find separable roles for vision in developing and maintaining the intrinsic neural activity of visual cortex.
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Affiliation(s)
- Omar H Butt
- Department of Neurology, University of Pennsylvania Philadelphia, PA, USA
| | - Noah C Benson
- Department of Neurology, University of Pennsylvania Philadelphia, PA, USA ; Department of Psychology, University of Pennsylvania Philadelphia, PA, USA
| | - Ritobrato Datta
- Department of Neurology, University of Pennsylvania Philadelphia, PA, USA
| | - Geoffrey K Aguirre
- Department of Neurology, University of Pennsylvania Philadelphia, PA, USA
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Xu Q, Zhang Z, Liao W, Xiang L, Yang F, Wang Z, Chen G, Tan Q, Jiao Q, Lu G. Time-shift homotopic connectivity in mesial temporal lobe epilepsy. AJNR Am J Neuroradiol 2014; 35:1746-52. [PMID: 24742802 DOI: 10.3174/ajnr.a3934] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Voxel-mirrored intrinsic functional connectivity allows the depiction of interhemispheric homotopic connections in the human brain, whereas time-shift intrinsic functional connectivity allows the detection of the extent of brain injury by measuring hemodynamic properties. We combined time-shift voxel-mirrored homotopic connectivity analyses to investigate the alterations in homotopic connectivity in mesial temporal lobe epilepsy and assessed the value of applying this approach to epilepsy lateralization and the prediction of surgical outcomes in mesial temporal lobe epilepsy. MATERIALS AND METHODS Resting-state functional MR imaging data were acquired from patients with unilateral mesial temporal lobe epilepsy (n=62) (31 left- and 31 right-side) and healthy controls (n=33). Dynamic interhemispheric homotopic architecture seeding from each hemisphere was individually calculated by 0, 1, 2, and 3 repetition time time-shift voxel-mirrored homotopic connectivity. Voxel-mirrored homotopic connectivity maps were compared between the patient and control groups by using 1-way ANOVA for each time-shift condition, separately. Group comparisons were further performed on the laterality of voxel-mirrored homotopic connectivity in each time-shift condition. Finally, we correlated the interhemispheric homotopic connection to the surgical outcomes in a portion of the patients (n=20). RESULTS The patients with mesial temporal lobe epilepsy showed decreased homotopic connectivity in the mesial temporal structures, temporal pole, and striatum. Alterations of the bihemispheric homotopic connectivity were lateralized along with delays in the time-shift in mesial temporal lobe epilepsy. The patients with unsuccessful surgical outcomes presented larger interhemispheric voxel-mirrored homotopic connectivity differences. CONCLUSIONS This study showed whole patterns of dynamic alterations of interhemispheric homotopic connectivity in mesial temporal lobe epilepsy, extending the knowledge of abnormalities in interhemispheric connectivity in this condition. Time-shift voxel-mirrored homotopic connectivity has the potential for lateralization of unilateral mesial temporal lobe epilepsy and may have the capability of predicting surgical outcomes in this condition.
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Affiliation(s)
- Q Xu
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.)
| | - Z Zhang
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.)
| | - W Liao
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.) Center for Cognition and Brain Disorders and the Affiliated Hospital (W.L.), Hangzhou Normal University, Hangzhou, China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments (W.L.), Hangzhou, China
| | - L Xiang
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.)
| | | | - Z Wang
- Department of Medical Imaging (Z.W.), Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | | | - Q Tan
- Neurosurgery (Q.T.), Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Q Jiao
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.) Department of Medical Imaging (Q.J.), Taishan Medical College, TaiAn, China
| | - G Lu
- From the Departments of Medical Imaging (Q.X., Z.Z., W.L., L.X., Q.J., G.L.)
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Long X, Goltz D, Margulies DS, Nierhaus T, Villringer A. Functional connectivity-based parcellation of the human sensorimotor cortex. Eur J Neurosci 2014; 39:1332-42. [DOI: 10.1111/ejn.12473] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 12/05/2013] [Indexed: 11/28/2022]
Affiliation(s)
- Xiangyu Long
- Department of Neurology; Max Planck Institute for Human Cognitive and Brain Sciences; Leipzig Germany
| | - Dominique Goltz
- Department of Neurology; Max Planck Institute for Human Cognitive and Brain Sciences; Leipzig Germany
- Institute of Psychology; University of Leipzig; Leipzig Germany
| | - Daniel S. Margulies
- Max Planck Research Group: Neuroanatomy & Connectivity; Max Planck Institute for Human Cognitive and Brain Sciences; Leipzig Germany
- Mind-Brain Institute at Berlin School of Mind and Brain; Charité-Universitätsmedizin Berlin and Humboldt University; Berlin Germany
| | - Till Nierhaus
- Department of Neurology; Max Planck Institute for Human Cognitive and Brain Sciences; Leipzig Germany
- Mind-Brain Institute at Berlin School of Mind and Brain; Charité-Universitätsmedizin Berlin and Humboldt University; Berlin Germany
| | - Arno Villringer
- Department of Neurology; Max Planck Institute for Human Cognitive and Brain Sciences; Leipzig Germany
- Mind-Brain Institute at Berlin School of Mind and Brain; Charité-Universitätsmedizin Berlin and Humboldt University; Berlin Germany
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Distinct parietal and temporal connectivity profiles of ventrolateral frontal areas involved in language production. J Neurosci 2013; 33:16846-52. [PMID: 24133284 DOI: 10.1523/jneurosci.2259-13.2013] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Broca's region, which in the language-dominant hemisphere of the human brain plays a major role in language production, includes two distinct cytoarchitectonic areas: 44 and 45. The unique connectivity patterns of these two areas have not been well established. In a resting-state functional connectivity study, we tested predictions about these areas from invasive tract-tracing studies of the connectivity of their homologs in the macaque monkey. We demonstrated their distinct connectivity profiles as well as their differences from the caudally adjacent ventral parts of the premotor cortex and the primary motor cortical region that represent the orofacial musculature. Area 45 is strongly connected with the superior temporal sulcus and the cortex on the adjacent superior and middle temporal gyri. In the parietal region, area 45 is connected with the angular gyrus, whereas area 44 is connected with the supramarginal gyrus. The primary motor cortical region in the caudal precentral gyrus is not connected with the posterior parietal region, which lies outside the confines of the postcentral gyrus, whereas the ventrorostral premotor cortical area 6VR, in the most anterior part of the precentral gyrus, has strong connections with the rostral supramarginal gyrus. Thus, area 44, which has stronger connections to the posterior supramarginal gyrus, can be distinguished from both the adjacent area 6VR and area 45. These findings provide a major improvement in understanding the connectivity of the areas in the ventrolateral frontal region that are involved in language production.
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Abstract
To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Broca's area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation.
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Raemaekers M, Schellekens W, van Wezel RJA, Petridou N, Kristo G, Ramsey NF. Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study. Neuroimage 2013; 84:911-21. [PMID: 24099850 DOI: 10.1016/j.neuroimage.2013.09.060] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 09/23/2013] [Accepted: 09/24/2013] [Indexed: 12/20/2022] Open
Abstract
The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity.
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Affiliation(s)
- Mathijs Raemaekers
- Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
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Nielsen JA, Zielinski BA, Fletcher PT, Alexander AL, Lange N, Bigler ED, Lainhart JE, Anderson JS. Multisite functional connectivity MRI classification of autism: ABIDE results. Front Hum Neurosci 2013; 7:599. [PMID: 24093016 PMCID: PMC3782703 DOI: 10.3389/fnhum.2013.00599] [Citation(s) in RCA: 190] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 09/04/2013] [Indexed: 12/02/2022] Open
Abstract
Background: Systematic differences in functional connectivity MRI metrics have been consistently observed in autism, with predominantly decreased cortico-cortical connectivity. Previous attempts at single subject classification in high-functioning autism using whole brain point-to-point functional connectivity have yielded about 80% accurate classification of autism vs. control subjects across a wide age range. We attempted to replicate the method and results using the Autism Brain Imaging Data Exchange (ABIDE) including resting state fMRI data obtained from 964 subjects and 16 separate international sites. Methods: For each of 964 subjects, we obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the gray matter (26.4 million “connections”) after preprocessing that included motion and slice timing correction, coregistration to an anatomic image, normalization to standard space, and voxelwise removal by regression of motion parameters, soft tissue, CSF, and white matter signals. Connections were grouped into multiple bins, and a leave-one-out classifier was evaluated on connections comprising each set of bins. Age, age-squared, gender, handedness, and site were included as covariates for the classifier. Results: Classification accuracy significantly outperformed chance but was much lower for multisite prediction than for previous single site results. As high as 60% accuracy was obtained for whole brain classification, with the best accuracy from connections involving regions of the default mode network, parahippocampaland fusiform gyri, insula, Wernicke Area, and intraparietal sulcus. The classifier score was related to symptom severity, social function, daily living skills, and verbal IQ. Classification accuracy was significantly higher for sites with longer BOLD imaging times. Conclusions: Multisite functional connectivity classification of autism outperformed chance using a simple leave-one-out classifier, but exhibited poorer accuracy than for single site results. Attempts to use multisite classifiers will likely require improved classification algorithms, longer BOLD imaging times, and standardized acquisition parameters for possible future clinical utility.
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Affiliation(s)
- Jared A Nielsen
- Interdepartmental Program in Neuroscience, University of Utah Salt Lake City, UT, USA ; Department of Psychiatry, University of Utah Salt Lake City, UT, USA
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Abstract
The hemispheric lateralization of certain faculties in the human brain has long been held to be beneficial for functioning. However, quantitative relationships between the degree of lateralization in particular brain regions and the level of functioning have yet to be established. Here we demonstrate that two distinct forms of functional lateralization are present in the left vs. the right cerebral hemisphere, with the left hemisphere showing a preference to interact more exclusively with itself, particularly for cortical regions involved in language and fine motor coordination. In contrast, right-hemisphere cortical regions involved in visuospatial and attentional processing interact in a more integrative fashion with both hemispheres. The degree of lateralization present in these distinct systems selectively predicted behavioral measures of verbal and visuospatial ability, providing direct evidence that lateralization is associated with enhanced cognitive ability.
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