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Lu J, Yan T, Yang L, Zhang X, Li J, Li D, Xiang J, Wang B. Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability. Neuroimage 2024; 295:120651. [PMID: 38788914 DOI: 10.1016/j.neuroimage.2024.120651] [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: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024] Open
Abstract
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
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Affiliation(s)
- Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 100081, China
| | - Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xi Zhang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaxin Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
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2
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Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [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: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
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Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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3
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Sun L, Zhang A, Liang F. Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing. Stat Med 2024; 43:2713-2733. [PMID: 38690642 PMCID: PMC11195441 DOI: 10.1002/sim.10096] [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: 07/08/2023] [Revised: 04/01/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.
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Affiliation(s)
- Lizhe Sun
- Beijing International Center for Mathematical Research, Peking University and Department of Statistics, Purdue University
| | | | - Faming Liang
- Department of Statistics, Purdue University, West Lafayette, IN 47907
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4
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Zheng J, Cheng Y, Wu X, Li X, Fu Y, Yang Z. Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks. Front Neurosci 2024; 18:1405734. [PMID: 38855440 PMCID: PMC11157044 DOI: 10.3389/fnins.2024.1405734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/07/2024] [Indexed: 06/11/2024] Open
Abstract
Objective In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics. Methods Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis. Results The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients. Conclusion The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.
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Affiliation(s)
- Jianhui Zheng
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Yuhao Cheng
- Huaxi Molecular Imaging Research Laboratory, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Xiaojie Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Ying Fu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
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5
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Spencer APC, Goodfellow M, Chakkarapani E, Brooks JCW. Resting-state functional connectivity in children cooled for neonatal encephalopathy. Brain Commun 2024; 6:fcae154. [PMID: 38741661 PMCID: PMC11089421 DOI: 10.1093/braincomms/fcae154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/21/2024] [Accepted: 04/28/2024] [Indexed: 05/16/2024] Open
Abstract
Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol BS2 8DX, UK
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- Department of Radiology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK
- Department of Mathematics and Statistics, University of Exeter, Exeter EX4 4QF, UK
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1TH, UK
- Neonatal Intensive Care Unit, St Michaels Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol BS2 8EG, UK
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol BS2 8DX, UK
- University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), University of East Anglia, Norwich NR4 7TJ, UK
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6
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Wang G, Jiang N, Ma Y, Suo D, Liu T, Funahashi S, Yan T. Using a deep generation network reveals neuroanatomical specificity in hemispheres. PATTERNS (NEW YORK, N.Y.) 2024; 5:100930. [PMID: 38645770 PMCID: PMC11026975 DOI: 10.1016/j.patter.2024.100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 04/23/2024]
Abstract
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
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7
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Okuno T, Hata J, Haga Y, Muta K, Tsukada H, Nakae K, Okano H, Woodward A. Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study. Neuroimage 2023; 279:120329. [PMID: 37591477 DOI: 10.1016/j.neuroimage.2023.120329] [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: 12/26/2022] [Revised: 07/31/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023] Open
Abstract
Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.
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Affiliation(s)
- Takuto Okuno
- Connectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashiogu, Arakawa-ku, Tokyo 116-8551, Japan
| | - Yawara Haga
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, 7-2-10 Higashiogu, Arakawa-ku, Tokyo 116-8551, Japan
| | - Hiromichi Tsukada
- Center for Mathematical Science and Artificial Intelligence, Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501, Japan
| | - Ken Nakae
- Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Aichi, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
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8
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Guo Y, Xia Y, Chen K. The body mass index is associated with increased temporal variability of functional connectivity in brain reward system. Front Nutr 2023; 10:1210726. [PMID: 37388634 PMCID: PMC10300418 DOI: 10.3389/fnut.2023.1210726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023] Open
Abstract
The reward system has been proven to be contributed to the vulnerability of obesity. Previous fMRI studies have shown abnormal functional connectivity of the reward system in obesity. However, most studies were based on static index such as resting-state functional connectivity (FC), ignoring the dynamic changes over time. To investigate the dynamic neural correlates of obesity susceptibility, we used a large, demographically well-characterized sample from the Human Connectome Project (HCP) to determine the relationship of body mass index (BMI) with the temporal variability of FC from integrated multilevel perspectives, i.e., regional and within- and between-network levels. Linear regression analysis was used to investigate the association between BMI and temporal variability of FC, adjusting for covariates of no interest. We found that BMI was positively associated with regional FC variability in reward regions, such as the ventral orbitofrontal cortex and visual regions. At the intra-network level, BMI was positively related to the variability of FC within the limbic network (LN) and default mode network (DMN). At the inter-network level, variability of connectivity of LN with DMN, frontoparietal, sensorimotor, and ventral attention networks showed positive correlations with BMI. These findings provided novel evidence for abnormal dynamic functional interaction between the reward network and the rest of the brain in obesity, suggesting a more unstable state and over-frequent interaction of the reward network and other attention and cognitive networks. These findings, thus, provide novel insight into obesity interventions that need to decrease the dynamic interaction between reward networks and other brain networks through behavioral treatment and neural modulation.
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Affiliation(s)
- Yiqun Guo
- School of Innovation and Entrepreneurship Education, Chongqing University of Posts and Telecommunications, Chongqing, China
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yuxiao Xia
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ke Chen
- School of Innovation and Entrepreneurship Education, Chongqing University of Posts and Telecommunications, Chongqing, China
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9
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Guo Y, Dong D, Wu H, Xue Z, Zhou F, Zhao L, Li Z, Feng T. The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping. Cereb Cortex 2023; 33:7163-7174. [PMID: 36748995 PMCID: PMC10422924 DOI: 10.1093/cercor/bhad028] [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: 09/27/2022] [Revised: 01/18/2023] [Indexed: 02/08/2023] Open
Abstract
Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.
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Affiliation(s)
- Yiqun Guo
- School of Innovation and Entrepreneurship education, Chongqing University of Posts and Telecommunications, Chongqing, China
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany
| | - Huimin Wu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Zhiyuan Xue
- School of Humanities and Management, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Le Zhao
- Faculty of Psychology, Beijing Normal University, Zhuhai, China
| | - Zhangyong Li
- Research Center of Biomedical Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
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10
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Li YL, Zheng MX, Hua XY, Gao X, Wu JJ, Shan CL, Zhang JP, Wei D, Xu JG. Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study. Brain Struct Funct 2023; 228:761-773. [PMID: 36749387 DOI: 10.1007/s00429-023-02616-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/25/2023] [Indexed: 02/08/2023]
Abstract
The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The 18F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China.,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Jun-Peng Zhang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China. .,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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11
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Zhang J, Zhuang L, Jiang J, Yang M, Li S, Tang X, Ma Y, Liu L, Ding G. Brain fingerprints along the language hierarchy. Front Hum Neurosci 2022; 16:982905. [PMID: 36188171 PMCID: PMC9521489 DOI: 10.3389/fnhum.2022.982905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Recent studies have shown that the brain functional connectome constitutes a unique fingerprint that allows the identification of individuals from a group. However, what information encoded in the brain that makes us unique remains elusive. Here, we addressed this issue by examining how individual identifiability changed along the language hierarchy. Subjects underwent fMRI scanning during rest and when listening to short stories played backward, scrambled at the sentence level, and played forward. Identification for individuals was performed between two scan sessions for each task as well as between the rest and task sessions. We found that individual identifiability tends to increase along the language hierarchy: the more complex the task is, the better subjects can be distinguished from each other based on their whole-brain functional connectivity profiles. A similar principle is found at the functional network level: compared to the low-order network (the auditory network), the high-order network is more individualized (the frontoparietal network). Moreover, in both cases, the increase in individual identifiability is accompanied by the increase in inter-subject variability of functional connectivities. These findings advance the understanding of the source of brain individualization and have potential implications for developing robust connectivity-based biomarkers.
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Affiliation(s)
- Juan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liping Zhuang
- Department of Psychology, School of Arts and Sciences, Beijing Normal University, Zhuhai, China
| | - Jiahao Jiang
- School of Psychology, Beijing Normal University, Beijing, China
| | - Menghan Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Shijie Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiangrong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yingbo Ma
- School of Psychology, Beijing Normal University, Beijing, China
| | - Lanfang Liu
- Department of Psychology, School of Arts and Sciences, Beijing Normal University, Zhuhai, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Center for Cognition and Neuroergonomics, Beijing Normal University, Zhuhai, China
- Lanfang Liu,
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- *Correspondence: Guosheng Ding,
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12
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Spencer APC, Goodfellow M. Using deep clustering to improve fMRI dynamic functional connectivity analysis. Neuroimage 2022; 257:119288. [PMID: 35551991 PMCID: PMC10751537 DOI: 10.1016/j.neuroimage.2022.119288] [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: 12/14/2021] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 10/18/2022] Open
Abstract
Dynamic functional connectivity (dFC) analysis of resting-state fMRI data is commonly performed by calculating sliding-window correlations (SWC), followed by k-means clustering in order to assign each window to a given state. Studies using synthetic data have shown that k-means performance is highly dependent on sliding window parameters and signal-to-noise ratio. Additionally, sources of heterogeneity between subjects may affect the accuracy of group-level clustering, thus affecting measurements of dFC state temporal properties such as dwell time and fractional occupancy. This may result in spurious conclusions regarding differences between groups (e.g. when comparing a clinical population to healthy controls). Therefore, is it important to quantify the ability of k-means to estimate dFC state temporal properties when applied to cohorts of multiple subjects, and to explore ways in which clustering performance can be maximised. Here, we explore the use of dimensionality reduction methods prior to clustering in order to map high-dimensional data to a lower dimensional space, providing salient features to the subsequent clustering step. We assess the use of deep autoencoders for dimensionality reduction prior to applying k-means clustering to the encoded data. We compare this deep clustering method to dimensionality reduction using principle component analysis (PCA), uniform manifold approximation and projection (UMAP), as well as applying k-means to the original feature space using either L1 or L2 distance. We provide extensive quantitative evaluation of clustering performance using synthetic datasets, representing data from multiple heterogeneous subjects. In synthetic data we find that deep clustering gives the best performance, while other approaches are often insufficient to capture temporal properties of dFC states. We then demonstrate the application of each method to real-world data from human subjects and show that the choice of dimensionality reduction method has a significant effect on group-level measurements of state temporal properties.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol, UK; Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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13
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14
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Vieira BH, Pamplona GSP, Fachinello K, Silva AK, Foss MP, Salmon CEG. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Li J, Bzdok D, Chen J, Tam A, Ooi LQR, Holmes AJ, Ge T, Patil KR, Jabbi M, Eickhoff SB, Yeo BTT, Genon S. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. SCIENCE ADVANCES 2022; 8:eabj1812. [PMID: 35294251 PMCID: PMC8926333 DOI: 10.1126/sciadv.abj1812] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models of behavioral phenotypes from brain functional magnetic resonance imaging. We examined the prediction bias using two independent datasets (preadolescent versus adult) of mixed ethnic/racial composition. When predictive models were trained on data dominated by white Americans (WA), out-of-sample prediction errors were generally higher for African Americans (AA) than for WA. This bias toward WA corresponds to more WA-like brain-behavior association patterns learned by the models. When models were trained on AA only, compared to training only on WA or an equal number of AA and WA participants, AA prediction accuracy improved but stayed below that for WA. Overall, the results point to the need for caution and further research regarding the application of current brain-behavior prediction models in minority populations.
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Affiliation(s)
- Jingwei Li
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal
Neurological Institute (MNI), McConnell Brain Imaging Institute (BIC), McGill
University, Montreal, QC, Canada
- Mila-Quebec Artificial Intelligence Institute,
Montreal, QC, Canada
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
| | - Avram J. Holmes
- Departments of Psychology and Psychiatry, Yale
University, New Haven, CT, USA
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit,
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA,
USA
- Stanley Center for Psychiatric Research, Broad
Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA
| | - Kaustubh R. Patil
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mbemba Jabbi
- Department of Psychiatry, Dell Medical School,
University of Texas at Austin, Austin, TX, USA
- The Mulva Clinic for Neurosciences, Dell Medical
School, University of Texas at Austin, Austin, TX, USA
- Institute of Neuroscience, University of Texas at
Austin, Austin, TX, USA
- Department of Psychology, University of Texas at
Austin, Austin, TX, USA
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering,
Centre for Sleep and Cognition, Centre for Translational Magnetic Resonance
Research, and N.1 Institute for Health and Institute for Digital Medicine,
National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme
(ISEP), National University of Singapore, Singapore, Singapore
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and
Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty,
Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Corresponding author. (J.L.); (S.G.);
(B.T.T.Y.)
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16
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Prediction of Neuropsychological Scores from Functional Connectivity Matrices Using Deep Autoencoders. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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17
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Zhao Q, Li J, Xiao Y, Cao H, Wang X, Zhang W, Li S, Liao W, Gong Q, Lui S. Distinct neuroanatomic subtypes in antipsychotic-treated patients with schizophrenia classified by the predefined classification in a never-treated sample. PSYCHORADIOLOGY 2021; 1:212-224. [PMID: 38666223 PMCID: PMC11025559 DOI: 10.1093/psyrad/kkab018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/17/2021] [Accepted: 12/01/2021] [Indexed: 02/05/2023]
Abstract
Background Distinct neuroanatomic subtypes have been identified in never-treated patients with schizophrenia based on cerebral structural abnormalities, but whether antipsychotic-treated patients would be stratified under the guidance of such previously formed classification remains unclear. Objective The present study aimed to investigate alterations of brain structures in antipsychotic-treated patients with schizophrenia based on a predefined morphological classification and their relationships with cognitive performance. Methods Cortical thickness, surface area, and subcortical volume were extracted from 147 antipsychotic-treated patients with schizophrenia using structural magnetic resonance imaging for classification. The Brief Assessment of Cognition in Schizophrenia (BACS) and Positive and Negative Syndrome Scale (PANSS) were used to assess cognition and symptoms. Results Antipsychotic-treated patients were categorized into three subtypes with distinct patterns of brain morphological alterations. Subtypes 1 and 2 were characterized by widespread deficits in cortical thickness but relatively limited deficits in surface area. In contrast, subtype 3 demonstrated cortical thickening mainly in parietal-occipital regions and widespread deficits in surface area. All three subgroups demonstrated cognitive deficits compared with healthy controls. Significant associations between neuroanatomic and cognitive abnormalities were only observed in subtype 1, where cortical thinning in the left lingual gyrus was conversely related to symbol coding performance. Conclusions Similar to drug-naïve patients, neuroanatomic heterogeneity exists in antipsychotic-treated patients, with disparate associations with cognition. These findings promote our understanding of relationships between neuroanatomic abnormalities and cognitive performance in the context of heterogeneity. Moreover, these results suggest that neurobiological heterogeneity needs to be considered in cognitive research in schizophrenia.
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Affiliation(s)
- Qiannan Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, 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
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY 11030, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY 11004, United States
| | - Xiao Wang
- 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
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - 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
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu 610041, China
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18
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Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome. Transl Psychiatry 2021; 11:526. [PMID: 34645783 PMCID: PMC8513388 DOI: 10.1038/s41398-021-01646-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 02/06/2023] Open
Abstract
Brain function relies on efficient communications between distinct brain systems. The pathology of major depressive disorder (MDD) damages functional brain networks, resulting in cognitive impairment. Here, we reviewed the associations between brain functional connectome changes and MDD pathogenesis. We also highlighted the utility of brain functional connectome for differentiating MDD from other similar psychiatric disorders, predicting recurrence and suicide attempts in MDD, and evaluating treatment responses. Converging evidence has now linked aberrant brain functional network organization in MDD to the dysregulation of neurotransmitter signaling and neuroplasticity, providing insights into the neurobiological mechanisms of the disease and antidepressant efficacy. Widespread connectome dysfunctions in MDD patients include multiple, large-scale brain networks as well as local disturbances in brain circuits associated with negative and positive valence systems and cognitive functions. Although the clinical utility of the brain functional connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments, and clinical outcomes of MDD.
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19
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Krupnik R, Yovel Y, Assaf Y. Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain. J Neurosci 2021; 41:8351-8361. [PMID: 34465598 PMCID: PMC8496194 DOI: 10.1523/jneurosci.1074-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/25/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Abstract
The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.
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Affiliation(s)
- Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- School of Neurobiology, Biochemistry and Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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20
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Cai B, Zhang G, Zhang A, Xiao L, Hu W, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder. Hum Brain Mapp 2021; 42:2691-2705. [PMID: 33835637 PMCID: PMC8127140 DOI: 10.1002/hbm.25394] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 02/11/2021] [Accepted: 02/17/2021] [Indexed: 12/29/2022] Open
Abstract
Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.
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Affiliation(s)
- Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
| | - Li Xiao
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
| | - Wenxing Hu
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
| | | | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana, USA
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