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Zhang K, Fang H, Li Z, Ren T, Li BM, Wang C. Sex differences in large-scale brain network connectivity for mental rotation performance. Neuroimage 2024; 298:120807. [PMID: 39179012 DOI: 10.1016/j.neuroimage.2024.120807] [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: 05/24/2024] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 08/26/2024] Open
Abstract
Mental rotation has emerged as an important predictor for success in science, technology, engineering, and math fields. Previous studies have shown that males and females perform mental rotation tasks differently. However, how the brain functions to support this difference remains poorly understood. Recent advancements in neuroimaging techniques have enabled the identification of sex differences in large-scale brain network connectivity. Using a classic mental rotation task with functional magnetic resonance imaging, the present study investigated whether there are any sex differences in large-scale brain network connectivity for mental rotation performance. Our results revealed that, relative to females, males exhibited less cross-network interaction (i.e. lower inter-network connectivity and participation coefficient) of the visual network but more intra-network integration (i.e. higher intra-network connectivity and local efficiency) and cross-network interaction (i.e. higher inter-network connectivity and participation coefficient) of the salience network. Across all participants, mental rotation performance was negatively correlated with cross-network interaction (i.e. participation coefficient) of the visual network, was positively correlated with cross-network interaction (i.e. inter-network connectivity) of the salience network, and was positively correlated with intra-network integration (i.e. local efficiency) of the somato-motor network. Interestingly, the cross-network integration indexes of both the visual and salience networks significantly mediated sex difference in mental rotation performance. The present findings suggest that large-scale brain network connectivity may constitute an essential neural basis for sex difference in mental rotation, and highlight the importance of considering sex as a research variable in investigating the complex network underpinnings of spatial cognition.
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Affiliation(s)
- Kaijie Zhang
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Haifeng Fang
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Zheng Li
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Tian Ren
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China
| | - Bao-Ming Li
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China; School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou 311121, China.
| | - Chunjie Wang
- Institute of Brain Science and Department of Psychology, Jing Hengyi School of Education, Hangzhou 311121, China; Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Hangzhou 311121, China.
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Kang DW, Wang SM, Um YH, Kim S, Kim T, Kim D, Lee CU, Lim HK. Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD. Front Psychiatry 2024; 15:1428535. [PMID: 39224475 PMCID: PMC11366601 DOI: 10.3389/fpsyt.2024.1428535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 07/11/2024] [Indexed: 09/04/2024] Open
Abstract
Background Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored. Objective To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex. Methods In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD. Results tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition. Conclusions The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - TaeYeong Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon DH, Pearlson GD, Macciardi F, Preda A, van Erp TGM, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun VD. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk. Biol Psychiatry 2024; 96:188-197. [PMID: 38070846 PMCID: PMC11156799 DOI: 10.1016/j.biopsych.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/15/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification. METHODS We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data. RESULTS Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks. CONCLUSIONS Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computer Science, Georgia State University, Atlanta, Georgia.
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland
| | - Daniel H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco Veteran Affairs Medical Center, San Francisco, California
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, New Mexico
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Mukesh Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia; Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia.
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Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari BM, Mathalon D, Pearlson G, Macciardi F, Preda A, van Erp T, Bustillo JR, Díaz-Caneja CM, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V. Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.18.548880. [PMID: 37503085 PMCID: PMC10370141 DOI: 10.1101/2023.07.18.548880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.
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Affiliation(s)
- A. Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - J. Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - N. Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
| | - A. Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Z. Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - O. Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - P. Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - B. M. Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, USA
| | - D.H. Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
- San Francisco VA Medical Center, San Francisco, CA, USA
| | - G.D. Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - F. Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - A. Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - T.G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - J. R. Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - C. M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - P. Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - M. Dhamala
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - T. Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, Maryland
| | - V.D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of CSE, Georgia Institute of Technology, Atlanta, Georgia
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Lewis N, Miller R, Gazula H, Calhoun V. Fine temporal brain network structure modularizes and localizes differently in men and women: insights from a novel explainability framework. Cereb Cortex 2023; 33:5817-5828. [PMID: 36843049 PMCID: PMC10183744 DOI: 10.1093/cercor/bhac462] [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: 05/27/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 02/28/2023] Open
Abstract
Deep learning has become an effective tool for classifying biological sex based on functional magnetic resonance imaging (fMRI). However, research on what features within the brain are most relevant to this classification is still lacking. Model interpretability has become a powerful way to understand "black box" deep-learning models, and select features within the input data that are most relevant to the correct classification. However, very little work has been done employing these methods to understand the relationship between the temporal dimension of functional imaging signals and the classification of biological sex. Consequently, less attention has been paid to rectifying problems and limitations associated with feature explanation models, e.g. underspecification and instability. In this work, we first provide a methodology to limit the impact of underspecification on the stability of the measured feature importance. Then, using intrinsic connectivity networks from fMRI data, we provide a deep exploration of sex differences among functional brain networks. We report numerous conclusions, including activity differences in the visual and cognitive domains and major connectivity differences.
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Affiliation(s)
- Noah Lewis
- Computational Science and Engineering, Georgia Institute of Technology, North Ave, 30332, GA, United States
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), 55 Park Pl NE, 30303, GA, United States
- Georgia State University, 33 Gilmer St SE, 30303, GA, United States
| | - Harshvardhan Gazula
- Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, 02129, MA, United States
- Harvard Medical School, 25 Shattuck St, 02115, MA, United States
| | - Vince Calhoun
- Computational Science and Engineering, Georgia Institute of Technology, North Ave, 30332, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), 55 Park Pl NE, 30303, GA, United States
- Georgia State University, 33 Gilmer St SE, 30303, GA, United States
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Odorfer TM, Yabe M, Hiew S, Volkmann J, Zeller D. Topological differences and confounders of mental rotation in cervical dystonia and blepharospasm. Sci Rep 2023; 13:6026. [PMID: 37055560 PMCID: PMC10102235 DOI: 10.1038/s41598-023-33262-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/11/2023] [Indexed: 04/15/2023] Open
Abstract
Mental rotation (mR) bases on imagination of actual movements. It remains unclear whether there is a specific pattern of mR impairment in focal dystonia. We aimed to investigate mR in patients with cervical dystonia (CD) and blepharospasm (BS) and to assess potential confounders. 23 CD patients and 23 healthy controls (HC) as well as 21 BS and 19 hemifacial spasm (HS) patients were matched for sex, age, and education level. Handedness, finger dexterity, general reaction time, and cognitive status were assessed. Disease severity was evaluated by clinical scales. During mR, photographs of body parts (head, hand, or foot) and a non-corporal object (car) were displayed at different angles rotated within their plane. Subjects were asked to judge laterality of the presented image by keystroke. Both speed and correctness were evaluated. Compared to HC, CD and HS patients performed worse in mR of hands, whereas BS group showed comparable performance. There was a significant association of prolonged mR reaction time (RT) with reduced MoCA scores and with increased RT in an unspecific reaction speed task. After exclusion of cognitively impaired patients, increased RT in the mR of hands was confined to CD group, but not HS. While the question of whether specific patterns of mR impairment reliably define a dystonic endophenotype remains elusive, our findings point to mR as a useful tool, when used carefully with control measures and tasks, which may be capable of identifying specific deficits that distinguish between subtypes of dystonia.
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Affiliation(s)
- Thorsten M Odorfer
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany.
| | - Marie Yabe
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany
| | - Shawn Hiew
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany
| | - Daniel Zeller
- Department of Neurology, University of Würzburg, 97080, Würzburg, Germany
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