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Lei T, Liao X, Liang X, Sun L, Xia M, Xia Y, Zhao T, Chen X, Men W, Wang Y, Ma L, Liu N, Lu J, Zhao G, Ding Y, Deng Y, Wang J, Chen R, Zhang H, Tan S, Gao JH, Qin S, Tao S, Dong Q, He Y. Functional network modules overlap and are linked to interindividual connectome differences during human brain development. PLoS Biol 2024; 22:e3002653. [PMID: 39292711 PMCID: PMC11441662 DOI: 10.1371/journal.pbio.3002653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 09/30/2024] [Accepted: 08/29/2024] [Indexed: 09/20/2024] Open
Abstract
The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.
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Affiliation(s)
- Tianyuan Lei
- Department of Psychiatry, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaodan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ningyu Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jing Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Gai Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yuyin Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yao Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jiali Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Haibo Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shuping Tan
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilongguan Clinical College, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024; 47:608-621. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [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: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
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Ragone E, Tanner J, Jo Y, Zamani Esfahlani F, Faskowitz J, Pope M, Coletta L, Gozzi A, Betzel R. Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains. Commun Biol 2024; 7:126. [PMID: 38267534 PMCID: PMC10810083 DOI: 10.1038/s42003-024-05766-w] [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: 06/08/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.
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Affiliation(s)
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
| | - Youngheun Jo
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Farnaz Zamani Esfahlani
- Stephenson School of Biomedical Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA
| | - Maria Pope
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Richard Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Department of Psychological and Brain Sciences and Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47401, USA.
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Rutkowski TM, Komendziński T, Otake-Matsuura M. Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm. Front Aging Neurosci 2024; 15:1294139. [PMID: 38239487 PMCID: PMC10794306 DOI: 10.3389/fnagi.2023.1294139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/27/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI. Methods We use topological data analysis (TDA) on multivariate EEG data to extract features that can be used for unsupervised clustering, subsequent machine learning-based classification, and cognitive score regression. We perform EEG experiments to evaluate conscious awareness in affective reminiscent photography settings. Results We use EEG and interior photography to distinguish between healthy cognitive aging and MCI. Our clustering UMAP and random forest application accurately predict MCI stage and MoCA scores. Discussion Our team has successfully implemented TDA feature extraction, MCI classification, and an initial regression of MoCA scores. However, our study has certain limitations due to a small sample size of only 23 participants and an unbalanced class distribution. To enhance the accuracy and validity of our results, future research should focus on expanding the sample size, ensuring gender balance, and extending the study to a cross-cultural context.
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Affiliation(s)
- Tomasz M. Rutkowski
- RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
- Graduate School of Education, The University of Tokyo, Tokyo, Japan
- Department of Cognitive Science, Institute of Information and Communication Research, Nicolaus Copernicus University, Toruń, Poland
| | - Tomasz Komendziński
- Department of Cognitive Science, Institute of Information and Communication Research, Nicolaus Copernicus University, Toruń, Poland
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