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Fırat Z, Er F, Noyan H, Ekinci G, Üçok A, Uluğ AM, Aktekin B. Discriminant analysis using MRI asymmetry indices and cognitive scores of women with temporal lobe epilepsy or schizophrenia. Neuroradiology 2024; 66:1083-1092. [PMID: 38416211 DOI: 10.1007/s00234-024-03317-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/20/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE This study aims to assess the diagnostic power of brain asymmetry indices and neuropsychological tests for differentiating mesial temporal lobe epilepsy (MTLE) and schizophrenia (SCZ). METHODS We studied a total of 39 women including 13 MTLE, 13 SCZ, and 13 healthy individuals (HC). A neuropsychological test battery (NPT) was administered and scored by an experienced neuropsychologist, and NeuroQuant (CorTechs Labs Inc., San Diego, California) software was used to calculate brain asymmetry indices (ASI) for 71 different anatomical regions of all participants based on their 3D T1 MR imaging scans. RESULTS Asymmetry indices measured from 10 regions showed statistically significant differences between the three groups. In this study, a multi-class linear discriminant analysis (LDA) model was built based on a total of fifteen variables composed of the most five significantly informative NPT scores and ten significant asymmetry indices, and the model achieved an accuracy of 87.2%. In pairwise classification, the accuracy for distinguishing MTLE from either SCZ or HC was 94.8%, while the accuracy for distinguishing SCZ from either MTLE or HC was 92.3%. CONCLUSION The ability to differentiate MTLE from SCZ using neuroradiological and neuropsychological biomarkers, even within a limited patient cohort, could make a substantial contribution to research in larger patient groups using different machine learning techniques.
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Affiliation(s)
- Zeynep Fırat
- Department of Radiology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey.
| | - Füsun Er
- Department of Information Systems Engineering, Piri Reis University, Istanbul, Turkey
| | - Handan Noyan
- Faculty of Social Sciences, Department of Psychology, Beykoz University, 34810, Istanbul, Turkey
| | - Gazanfer Ekinci
- Department of Radiology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey
| | - Alp Üçok
- Istanbul Faculty of Medicine, Department of Psychiatry, Istanbul University, 34134, Istanbul, Turkey
| | - Aziz M Uluğ
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
- CorTechs Labs Inc, San Diego, CA, USA
| | - Berrin Aktekin
- Department of Neurology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey
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Yang Y, Zhen Y, Wang X, Liu L, Zheng Y, Zheng Z, Zheng H, Tang S. Altered asymmetry of functional connectome gradients in major depressive disorder. Front Neurosci 2024; 18:1385920. [PMID: 38745933 PMCID: PMC11092381 DOI: 10.3389/fnins.2024.1385920] [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: 02/14/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction Major depressive disorder (MDD) is a debilitating disease involving sensory and higher-order cognitive dysfunction. Previous work has shown altered asymmetry in MDD, including abnormal lateralized activation and disrupted hemispheric connectivity. However, it remains unclear whether and how MDD affects functional asymmetries in the context of intrinsic hierarchical organization. Methods Here, we evaluate intra- and inter-hemispheric asymmetries of the first three functional gradients, characterizing unimodal-transmodal, visual-somatosensory, and somatomotor/default mode-multiple demand hierarchies, to study MDD-related alterations in overarching system-level architecture. Results We find that, relative to the healthy controls, MDD patients exhibit alterations in both primary sensory regions (e.g., visual areas) and transmodal association regions (e.g., default mode areas). We further find these abnormalities are woven in heterogeneous alterations along multiple functional gradients, associated with cognitive terms involving mind, memory, and visual processing. Moreover, through an elastic net model, we observe that both intra- and inter-asymmetric features are predictive of depressive traits measured by BDI-II scores. Discussion Altogether, these findings highlight a broad and mixed effect of MDD on functional gradient asymmetry, contributing to a richer understanding of the neurobiological underpinnings in MDD.
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Affiliation(s)
- Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
| | - Xin Wang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Longzhao Liu
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
| | - Zhiming Zheng
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- State Key Lab of Software Development Environment, Beihang University, Beijing, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing, China
| | - Shaoting Tang
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- State Key Lab of Software Development Environment, Beihang University, Beijing, China
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Shi Y, Cui D, Sun F, OuYang Z, Dou R, Jiao Q, Cao W, Yu G. Exploring sexual dimorphism in basal forebrain volume changes during aging and neurodegenerative diseases. iScience 2024; 27:109041. [PMID: 38361626 PMCID: PMC10867643 DOI: 10.1016/j.isci.2024.109041] [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: 08/11/2023] [Revised: 11/15/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
Patients with neurodegenerative diseases exhibit diminished basal forebrain (BF) volume compared to healthy individuals. However, it's uncertain whether this difference is consistent between sexes. It has been reported that BF volume moderately atrophies during aging, but the effect of sex on BF volume changes during the normal aging process remains unclear. In the cross-sectional study, we observed a significant reduction in BF volume in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to Healthy Controls (HCs), especially in the Ch4 subregion. Notably, significant differences in BF volume between MCI and HCs were observed solely in the female group. Additionally, we identified asymmetrical atrophy in the left and right Ch4 subregions in female patients with AD. In the longitudinal analysis, we found that aging seemed to have a minimal impact on BF volume in males. Our study highlights the importance of considering sex as a research variable in brain science.
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Affiliation(s)
- Yajun Shi
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Dong Cui
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Fengzhu Sun
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Zhen OuYang
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
- Department of Radiology, Taian Municipal Hospital, Tai’ an, Shandong 271000, China
| | - Ruhai Dou
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Qing Jiao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Weifang Cao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
| | - Guanghui Yu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’ an, Shandong 271000, China
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’ an, Shandong 271016, China
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Jiang Y, Gong G. Common and distinct patterns underlying different linguistic tasks: multivariate disconnectome symptom mapping in poststroke patients. Cereb Cortex 2024; 34:bhae008. [PMID: 38265297 DOI: 10.1093/cercor/bhae008] [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: 10/14/2023] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/25/2024] Open
Abstract
Numerous studies have been devoted to neural mechanisms of a variety of linguistic tasks (e.g. speech comprehension and production). To date, however, whether and how the neural patterns underlying different linguistic tasks are similar or differ remains elusive. In this study, we compared the neural patterns underlying 3 linguistic tasks mainly concerning speech comprehension and production. To address this, multivariate regression approaches with lesion/disconnection symptom mapping were applied to data from 216 stroke patients with damage to the left hemisphere. The results showed that lesion/disconnection patterns could predict both poststroke scores of speech comprehension and production tasks; these patterns exhibited shared regions on the temporal pole of the left hemisphere as well as unique regions contributing to the prediction for each domain. Lower scores in speech comprehension tasks were associated with lesions/abnormalities in the superior temporal gyrus and middle temporal gyrus, while lower scores in speech production tasks were associated with lesions/abnormalities in the left inferior parietal lobe and frontal lobe. These results suggested an important role of the ventral and dorsal stream pathways in speech comprehension and production (i.e. supporting the dual stream model) and highlighted the applicability of the novel multivariate disconnectome-based symptom mapping in cognitive neuroscience research.
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Affiliation(s)
- Yaya Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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Wang X, Huang CC, Tsai SJ, Lin CP, Cai Q. The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan. Front Aging Neurosci 2024; 16:1331574. [PMID: 38313436 PMCID: PMC10837851 DOI: 10.3389/fnagi.2024.1331574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive. Methods This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan. Results The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect. Discussion Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.
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Affiliation(s)
- Xiao Wang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
| | - Shih-Jen Tsai
- Brain Research Center, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ching-Po Lin
- Brain Research Center, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Institute of Neuroscience, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Qing Cai
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
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Roger E, Labache L, Hamlin N, Kruse J, Baciu M, Doucet GE. When Age Tips the Balance: a Dual Mechanism Affecting Hemispheric Specialization for Language. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.04.569978. [PMID: 38106059 PMCID: PMC10723284 DOI: 10.1101/2023.12.04.569978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Aging engenders neuroadaptations, generally reducing specificity and selectivity in functional brain responses. Our investigation delves into the functional specialization of brain hemispheres within language-related networks across adulthood. In a cohort of 728 healthy adults spanning ages 18 to 88, we modeled the trajectories of inter-hemispheric asymmetry concerning the principal functional gradient across 37 homotopic regions of interest (hROIs) of an extensive language network, known as the Language-and-Memory Network. Our findings reveal that over two-thirds of Language-and-Memory Network hROIs undergo asymmetry changes with age, falling into two main clusters. The first cluster evolves from left-sided specialization to right-sided tendencies, while the second cluster transitions from right-sided asymmetry to left-hemisphere dominance. These reversed asymmetry shifts manifest around midlife, occurring after age 50, and are associated with poorer language production performance. Our results provide valuable insights into the influence of functional brain asymmetries on language proficiency and present a dynamic perspective on brain plasticity during the typical aging process.
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Affiliation(s)
- Elise Roger
- Institut Universitaire de Gériatrie de Montréal, Communication and Aging Lab, Montreal, Quebec, Canada
- Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France
| | - Loïc Labache
- Department of Psychology, Yale University, New Haven, CT, 06520, US
| | - Noah Hamlin
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
| | - Jordanna Kruse
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
| | - Monica Baciu
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France
| | - Gaelle E. Doucet
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 68010, US
- Department of Pharmacology and Neuroscience, Creighton University School of Medicine, Omaha, NE, 68178, US
- Center for Pediatric Brain Health, Boys Town National Research Hospital, Omaha, NE, 68178, US
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Wang H, Yu M, Ren J, Zhong X, Xu D, Gao L, Xu H. Neuroanatomical correlates of cognitive impairment following basal ganglia-thalamic post-hemorrhagic stroke: Uncovering network-wide alterations in hemispheric gray matter asymmetry. Brain Res 2023; 1820:148559. [PMID: 37652090 DOI: 10.1016/j.brainres.2023.148559] [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: 04/12/2023] [Revised: 08/16/2023] [Accepted: 08/26/2023] [Indexed: 09/02/2023]
Abstract
Cognitive impairment and recovery are central issues in hemorrhagic stroke. This study aimed to investigate whether post-hemorrhagic stroke cognitive impairment (PhSCI) is associated with cortical gray matter (GM) loss and hemispheric asymmetry changes and whether these changes could predict improvements in cognitive function during the recovery. Nineteen patients with PhSCI, comprising 10 with basal ganglia hemorrhage and 9 with thalamic hemorrhage, were recruited. Among them, 9 completed a course of repetitive transcranial magnetic stimulation (rTMS). Additionally, 19 demographically and comorbidity-matched healthy controls were also included. Structural brain MRI and cognitive assessments were performed. Voxel-wise GM volume and hemispheric asymmetry were analyzed. The PhSCI patients exhibited bilateral, yet asymmetric, GM losses in the hippocampus, fusiform, lateral temporal, prefrontal, somatomotor, and inferior parietal regions. The analysis of GM asymmetry revealed that patients showed rightward GM in the lateral temporal, somatomotor, and inferior parietal regions. Among the 9 PhSCI patients who completed rTMS, there was a marginal trend of regional GM increase and leftward GM, and these changes were in parallel with the improvements in cognitive tests. Further lesion connectivity and metanalytic mapping identified two interconnected systems linked to the lesions, which were anchored in the default mode, somatomotor, and salience/cognitive control networks and in the cognitive domains of memory, language, decision-making, and executive function. In conclusion, PhSCI patients exhibited network-wide cortical GM losses, distal to subcortical hemorrhagic lesions, and hemisphere asymmetry changes. These changes appear to predict rTMS-related cognitive improvements, suggesting that even subcortical focal lesions can lead to alterations in distal cortical neuroanatomical architecture. Our preliminary findings provide new insights into the neuroanatomical basis of PhSCI.
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Affiliation(s)
- Huan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Jinxia Ren
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Xiaoli Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Dan Xu
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City 430071, Hubei Province, China.
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Man C, Gilissen E, Michaud M. Sexual dimorphism in the cranium and endocast of the eastern lowland gorillas (Gorilla beringei graueri). J Hum Evol 2023; 184:103439. [PMID: 37804559 DOI: 10.1016/j.jhevol.2023.103439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 10/09/2023]
Abstract
Sexual dimorphism of the nervous system has been reported for a wide range of vertebrates. However, understanding of sexual dimorphism in primate cranial structures and soft tissues, and more particularly the brain, remains limited. In this study, we aimed to investigate the external and internal (i.e., endocast) cranial differences between male and female eastern lowland gorillas (Gorilla beringei graueri). We examined the differences in the size, shape, and disparity with the aim to compare how sexual dimorphism can impact these two structures distinctively, with a particular focus on the endocranium. To do so, we reconstructed gorilla external crania and endocasts from CT scans and used 3D geometric morphometric techniques combined with multivariate analyses to assess the cranial and endocranial differences between the sexes. Our results highlighted sexual dimorphism for the external cranium and endocast with regard to both size and shape. In particular, males display an elongated face accompanied by a pronounced sagittal crest and an elongated endocast along the rostroposterior axis, in contrast to females who are identified by a more rounded brain case and endocast. Males also show a significantly larger external cranium and endocast size than females. In addition, we described important differences for the posterior cranial fossae (i.e., the position of the cerebellum within the brain case) and olfactory bulb between the two sexes. Particularly, our results highlighted that, relatively to males, females have larger posterior cranial fossae, whereas males have been characterized by a larger and rostrally oriented olfactory bulb.
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Affiliation(s)
- Caitlin Man
- Laboratory of Ecology, Evolution and Biodiversity Conservation, Katholieke Universiteit Leuven, Charles Deberiotstraat 32 Bus 2439, 3000 Leuven, Belgium; Department of African Zoology, Royal Museum for Central Africa, Leuvensesteenweg 13, 3080 Tervuren, Belgium.
| | - Emmanuel Gilissen
- Department of African Zoology, Royal Museum for Central Africa, Leuvensesteenweg 13, 3080 Tervuren, Belgium; Laboratory of Histology and Neuropathology, Université Libre de Bruxelles, CP620 - Route de Lennik 808, 1070 Brussels, Belgium
| | - Margot Michaud
- Department of African Zoology, Royal Museum for Central Africa, Leuvensesteenweg 13, 3080 Tervuren, Belgium
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Wan B, Hong SJ, Bethlehem RAI, Floris DL, Bernhardt BC, Valk SL. Diverging asymmetry of intrinsic functional organization in autism. Mol Psychiatry 2023; 28:4331-4341. [PMID: 37587246 PMCID: PMC10827663 DOI: 10.1038/s41380-023-02220-x] [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: 04/06/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023]
Abstract
Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we examined inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra- but not inter-hemispheric features were predictive of the severity of autistic traits. Our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in atypical developmental trajectories of functional organization asymmetry in autism.
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Affiliation(s)
- Bin Wan
- Otto Hahn Research Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity (IMPRS NeuroCom), Leipzig, Germany.
- Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
| | - Seok-Jun Hong
- Centre for Neuroscience Imaging Research, Institute for Basic Science, Department of Global Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | | | - Dorothea L Floris
- Department of Psychology, University of Zürich, Zürich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Sofie L Valk
- Otto Hahn Research Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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10
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Zhang Q, Li J, He Y, Yang F, Xu Q, Larivière S, Bernhardt BC, Liao W, Lu G, Zhang Z. Atypical functional connectivity hierarchy in Rolandic epilepsy. Commun Biol 2023; 6:704. [PMID: 37429897 DOI: 10.1038/s42003-023-05075-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
Functional connectivity hierarchy is an important principle in the process of brain functional organization and an important feature reflecting brain development. However, atypical brain network hierarchy organization in Rolandic epilepsy have not been systematically investigated. We examined connectivity alteration with age and its relation to epileptic incidence, cognition, or underlying genetic factors in 162 cases of Rolandic epilepsy and 117 typically developing children, by measuring fMRI multi-axis functional connectivity gradients. Rolandic epilepsy is characterized by contracting and slowing expansion of the functional connectivity gradients, highlighting the atypical age-related change of the connectivity hierarchy in segregation properties. The gradient alterations are relevant to seizure incidence, cognition, and connectivity deficit, and development-associated genetic basis. Collectively, our approach provides converging evidence for atypical connectivity hierarchy as a system-level substrate of Rolandic epilepsy, suggesting this is a disorder of information processing across multiple functional domains, and established a framework for large-scale brain hierarchical research.
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Affiliation(s)
- Qirui Zhang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yan He
- Department of Neurology, Children's Hospital of Nanjing Medical University, Nanjing, 210002, China
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Qiang Xu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210002, China
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Guangming Lu
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China.
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China.
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China.
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China.
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11
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Shen Y, Cai H, Mo F, Yao S, Yu Y, Zhu J. Functional connectivity gradients of the cingulate cortex. Commun Biol 2023; 6:650. [PMID: 37337086 DOI: 10.1038/s42003-023-05029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/08/2023] [Indexed: 06/21/2023] Open
Abstract
Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior-posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience.
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Affiliation(s)
- Yuhao Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Fan Mo
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Shanwen Yao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China.
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China.
- Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
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12
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Labache L, Ge T, Yeo BTT, Holmes AJ. Language network lateralization is reflected throughout the macroscale functional organization of cortex. Nat Commun 2023; 14:3405. [PMID: 37296118 PMCID: PMC10256741 DOI: 10.1038/s41467-023-39131-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.
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Affiliation(s)
- Loïc Labache
- Department of Psychology, Yale University, New Haven, CT, 06520, US.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, US
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, US
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, 02142, US
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, National University of Singapore, Singapore, SG, 119077, Singapore
- Department of Electrical and Computer Engineering, Centre for Translational Magnetic Resonance Research, National University of Singapore, Singapore, SG, 119077, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore, SG, 119077, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, US
- National University of Singapore Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, SG, 119077, Singapore
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, 06520, US.
- Department of Psychiatry, Yale University, New Haven, CT, 06520, US.
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, US.
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, 08854, US.
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13
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Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability. Neuroimage 2022; 263:119672. [PMID: 36209795 DOI: 10.1016/j.neuroimage.2022.119672] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/23/2022] Open
Abstract
Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.
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14
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Li W, Fan L, Shi W, Lu Y, Li J, Luo N, Wang H, Chu C, Ma L, Song M, Li K, Cheng L, Cao L, Jiang T. Brainnetome atlas of preadolescent children based on anatomical connectivity profiles. Cereb Cortex 2022; 33:5264-5275. [PMID: 36255322 PMCID: PMC10151881 DOI: 10.1093/cercor/bhac415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/17/2022] [Accepted: 09/18/2022] [Indexed: 11/13/2022] Open
Abstract
During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.
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Affiliation(s)
- Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, No.1 Jinji Road, Qixing District, Guilin 541004, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
| | - Long Cao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
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15
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Zhang H, Zhao R, Hu X, Guan S, Margulies DS, Meng C, Biswal BB. Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads. Brain Struct Funct 2022; 227:2701-2712. [PMID: 36098843 DOI: 10.1007/s00429-022-02564-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
Abstract
Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Xin Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Sihai Guan
- Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission. College of Electronic and Information, Southwest Minzu University, Chengdu, 610225, China
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China.
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, 607 Fenster Hall, Newark, NJ, 07102, USA.
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16
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Wan B, Bayrak Ş, Ting Xu T, Schaare HL, Bethlehem RAI, Bernhardt BC, Valk SL. Heritability and cross-species comparisons of human cortical functional organization asymmetry. eLife 2022; 11:77215. [PMID: 35904242 PMCID: PMC9381036 DOI: 10.7554/elife.77215] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/28/2022] [Indexed: 11/17/2022] Open
Abstract
The human cerebral cortex is symmetrically organized along large-scale axes but also presents inter-hemispheric differences in structure and function. The quantified contralateral homologous difference, that is asymmetry, is a key feature of the human brain left-right axis supporting functional processes, such as language. Here, we assessed whether the asymmetry of cortical functional organization is heritable and phylogenetically conserved between humans and macaques. Our findings indicate asymmetric organization along an axis describing a functional trajectory from perceptual/action to abstract cognition. Whereas language network showed leftward asymmetric organization, frontoparietal network showed rightward asymmetric organization in humans. These asymmetries were heritable in humans and showed a similar spatial distribution with macaques, in the case of intra-hemispheric asymmetry of functional hierarchy. This suggests (phylo)genetic conservation. However, both language and frontoparietal networks showed a qualitatively larger asymmetry in humans relative to macaques. Overall, our findings suggest a genetic basis for asymmetry in intrinsic functional organization, linked to higher order cognitive functions uniquely developed in humans.
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Affiliation(s)
- Bin Wan
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ting Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | | | - Sofie Louise Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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17
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Xia Y, Xia M, Liu J, Liao X, Lei T, Liang X, Zhao T, Shi Z, Sun L, Chen X, Men W, Wang Y, Pan Z, Luo J, Peng S, Chen M, Hao L, Tan S, Gao JH, Qin S, Gong G, Tao S, Dong Q, He Y. Development of functional connectome gradients during childhood and adolescence. Sci Bull (Beijing) 2022; 67:1049-1061. [PMID: 36546249 DOI: 10.1016/j.scib.2022.01.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/29/2021] [Accepted: 12/23/2021] [Indexed: 01/07/2023]
Abstract
Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6-14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.
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Affiliation(s)
- Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xinyu Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Ziyi Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Zhiying Pan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Jie Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Siya Peng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Menglu Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lei Hao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China.
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18
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Chaari N, Gharsallaoui MA, Akdağ HC, Rekik I. Multigraph classification using learnable integration network with application to gender fingerprinting. Neural Netw 2022; 151:250-263. [PMID: 35447482 DOI: 10.1016/j.neunet.2022.03.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/10/2022] [Accepted: 03/28/2022] [Indexed: 01/27/2023]
Abstract
Multigraphs with heterogeneous views present one of the most challenging obstacles to classification tasks due to their complexity. Several works based on feature selection have been recently proposed to disentangle the problem of multigraph heterogeneity. However, such techniques have major drawbacks. First, the bulk of such works lies in the vectorization and the flattening operations, failing to preserve and exploit the rich topological properties of the multigraph. Second, they learn the classification process in a dichotomized manner where the cascaded learning steps are pieced in together independently. Hence, such architectures are inherently agnostic to the cumulative estimation error from step to step. To overcome these drawbacks, we introduce MICNet (multigraph integration and classifier network), the first end-to-end graph neural network based model for multigraph classification. First, we learn a single-view graph representation of a heterogeneous multigraph using a GNN based integration model. The integration process in our model helps tease apart the heterogeneity across the different views of the multigraph by generating a subject-specific graph template while preserving its geometrical and topological properties conserving the node-wise information while reducing the size of the graph (i.e., number of views). Second, we classify each integrated template using a geometric deep learning block which enables us to grasp the salient graph features. We train, in end-to-end fashion, these two blocks using a single objective function to optimize the classification performance. We evaluate our MICNet in gender classification using brain multigraphs derived from different cortical measures. We demonstrate that our MICNet significantly outperformed its variants thereby showing its great potential in multigraph classification.
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Affiliation(s)
- Nada Chaari
- BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Faculty of Management, Istanbul Technical University, Istanbul, Turkey
| | - Mohammed Amine Gharsallaoui
- BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Ecole Polytechnique de Tunisie (EPT), Tunis, Tunisia
| | | | - Islem Rekik
- BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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19
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Friedrich P, Patil KR, Mochalski LN, Li X, Camilleri JA, Kröll JP, Wiersch L, Eickhoff SB, Weis S. Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality. Brain Struct Funct 2021; 227:425-440. [PMID: 34882263 PMCID: PMC8844166 DOI: 10.1007/s00429-021-02418-1] [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: 02/14/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022]
Abstract
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.
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Affiliation(s)
- Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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20
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Deng X, Wang J, Zang Y, Li Y, Fu W, Su Y, Chen X, Du B, Dong Q, Chen C, Li J. Intermittent theta burst stimulation over the parietal cortex has a significant neural effect on working memory. Hum Brain Mapp 2021; 43:1076-1086. [PMID: 34730863 PMCID: PMC8764471 DOI: 10.1002/hbm.25708] [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: 07/17/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/17/2022] Open
Abstract
The crucial role of the parietal cortex in working memory (WM) storage has been identified by fMRI studies. However, it remains unknown whether repeated parietal intermittent theta‐burst stimulation (iTBS) can improve WM. In this within‐subject randomized controlled study, under the guidance of fMRI‐identified parietal activation in the left hemisphere, 22 healthy adults received real and sham iTBS sessions (five consecutive days, 600 pulses per day for each session) with an interval of 9 months between the two sessions. Electroencephalography signals of each subject before and after both iTBS sessions were collected during a change detection task. Changes in contralateral delay activity (CDA) and K‐score were then calculated to reflect neural and behavioral WM improvement. Repeated‐measures ANOVA suggested that real iTBS increased CDA more than the sham one (p = .011 for iTBS effect). Further analysis showed that this effect was more significant in the left hemisphere than in the right hemisphere (p = .029 for the hemisphere‐by‐iTBS interaction effect). Pearson correlation analyses showed significant correlations for two conditions between CDA changes in the left hemisphere and K score changes (ps <.05). In terms of the behavioral results, significant K score changes after real iTBS were observed for two conditions, but a repeated‐measures ANOVA showed a nonsignificant main effect of iTBS (p = .826). These results indicate that the current iTBS protocol is a promising way to improve WM capability based on the neural indicator (CDA) but further optimization is needed to produce a behavioral effect.
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Affiliation(s)
- Xinping Deng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jue Wang
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Yang Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Wenjin Fu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanyan Su
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & the Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, School of Mental Health, Capital Medical University, Beijing, China
| | - Boqi Du
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, California, USA
| | - Jun Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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21
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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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22
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Lin J, Zhang Y, Li W, Yan J, Ke Y. Flatness of the Meckel cave may cause primary trigeminal neuralgia: a radiomics-based study. J Headache Pain 2021; 22:104. [PMID: 34479476 PMCID: PMC8414677 DOI: 10.1186/s10194-021-01317-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/12/2021] [Indexed: 12/20/2022] Open
Abstract
Background Neurovascular contact (NVC) is the main cause of primary trigeminal neuralgia (PTN); however, cases of PTN without NVC are still observed. In this study, the Meckel cave (MC) morphology in PTN were analyzed by radiomics and compared to healthy controls (HCs) to explore the cause of PTN. Methods We studied the 3.0T MRI data of 115 patients with PTN and 46 HCs. Bilateral MC was modeled using the 3D Slicer software, and the morphological characteristics of MC were analyzed using the radiomics method. Results The right side incidence rate in the PTN group was higher than the left side incidence. By analyzing the flatness feature of MC, we observed that the affected side of the PTN was lower than that of the unaffected side, the right MC of the PTN and HC was lower than that of the left MC, the MC of the affected side of the left and right PTN without bilateral NVC was lower than that of the unaffected side. Conclusions By providing a method to analyze the morphology of the MC, we found that there is an asymmetry in the morphology of bilateral MC in the PTN and HC groups. It can be inferred that the flatness of the MC may be a cause of PTN.
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Affiliation(s)
- Jinzhi Lin
- The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China.,Department of Neurosurgery, Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | - Yong Zhang
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | - Wuming Li
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | - Jianhao Yan
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, 510317, Guangzhou, China
| | - Yiquan Ke
- The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China.
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23
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Poydasheva AG, Sinitsyn DO, Bakulin IS, Suponeva NA, Piradov MA. Structural and functional biomarkers of efficacy of navigated repetitive transcranial magnetic stimulation in therapy for trigeminal neuralgia. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2021. [DOI: 10.24075/brsmu.2021.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an alternative treatment option for patients with drug-resistant trigeminal neuralgia (TN). However, the effect of rTMS is variable. The aim of this study was to find neuroimaging biomarkers of clinical efficacy of navigated rTMS. Seventeen patients with TN (14 women and 3 men, median age 56 years) received 10 sessions of high-frequency rTMS of the motor cortex contralateral to pain side. The data were analyzed for correlations between functional connectivity (FC), the grey matter (GM) volume and the reduction in pain intensity. Positive correlations were established between the reduction in average pain intensity and GM volume in caudate nuclei in both hemispheres (p(unc) = 0.03), both cerebellar hemispheres (p(unc) = 0.002) and the postcentral gyrus contralateral to pain side (p(unc) = 0.005); between the reduction in peak pain intensity and GM volume in the caudate nucleus contralateral to pain side (p(unc) = 0.04) and the cerebellar hemisphere ipsilateral to pain (p(unc) = 0.03). Significant positive correlations were discovered between the reduction in average pain intensity and FC between the thalamus contralateral to pain side, the postcentral gyrus and the insular operculum (both ipsilateral to pain side; (p(FWE) = 0.018), as well as between the cingulate cortex and the anterior cingulate cortex ipsilateral to pain (p(FWE) = 0.017), between the contralateral subcallosal gyrus and the cerebellar hemisphere ipsilateral to pain (p(FWE) = 0.018). A negative correlation was established for FC between the contralateral putamen and the occipital lobes in both hemispheres (p(FWE) = 0.001). Our findings may spur the development of individual predictors of rTMS efficacy in patients with chronic pain.
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Affiliation(s)
| | - DO Sinitsyn
- Research Center of Neurology, Moscow, Russia
| | - IS Bakulin
- Research Center of Neurology, Moscow, Russia
| | - NA Suponeva
- Research Center of Neurology, Moscow, Russia
| | - MA Piradov
- Research Center of Neurology, Moscow, Russia
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