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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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Baghernezhad S, Daliri MR. Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data. GeroScience 2024:10.1007/s11357-024-01128-w. [PMID: 38499956 DOI: 10.1007/s11357-024-01128-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 03/08/2024] [Indexed: 03/20/2024] Open
Abstract
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.
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Affiliation(s)
- Sepideh Baghernezhad
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience & Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
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Orlichenko A, Su KJ, Shen H, Deng HW, Wang YP. Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort. J Med Imaging (Bellingham) 2024; 11:024010. [PMID: 38618171 PMCID: PMC11009525 DOI: 10.1117/1.jmi.11.2.024010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/26/2024] [Accepted: 03/29/2024] [Indexed: 04/16/2024] Open
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
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Affiliation(s)
- Anton Orlichenko
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
| | - Kuan-Jui Su
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hui Shen
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Hong-Wen Deng
- Tulane University, School of Medicine, Center for Biomedical Informatics and Genomics, New Orleans, Louisiana, United States
| | - Yu-Ping Wang
- Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
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Orlichenko A, Su KJ, Tian Q, Shen H, Deng HW, Wang YP. Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort. ARXIV 2023:arXiv:2308.07992v2. [PMID: 37645050 PMCID: PMC10462162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Kuan-Jui Su
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Qing Tian
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hui Shen
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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Orlichenko A, Su KJ, Tian Q, Shen H, Deng HW, Wang YP. Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.15.23294133. [PMID: 37645791 PMCID: PMC10462217 DOI: 10.1101/2023.08.15.23294133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Purpose Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
| | - Kuan-Jui Su
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Qing Tian
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hui Shen
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Hong-Wen Deng
- Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA 70118
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118
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Orlichenko A, Qu G, Su KJ, Liu A, Shen H, Deng HW, Wang YP. Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results. ARXIV 2023:arXiv:2308.01451v1. [PMID: 37576121 PMCID: PMC10418521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.
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Affiliation(s)
- Anton Orlichenko
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kuan-Jui Su
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Hong-Wen Deng
- School of Medicine, Tulane University, New Orleans, LA, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
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Xiao L, Cai B, Qu G, Zhang G, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies. IEEE Trans Biomed Eng 2022; 69:3039-3050. [PMID: 35316180 PMCID: PMC9594860 DOI: 10.1109/tbme.2022.3160447] [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] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework. METHODS We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features. RESULTS AND CONCLUSION We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC. SIGNIFICANCE This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.
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Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
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Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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Cai B, Zhou Z, Zhang A, Zhang G, Xiao L, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences. J Neurosci Methods 2022; 372:109539. [PMID: 35219769 DOI: 10.1016/j.jneumeth.2022.109539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. METHODS In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. RESULTS We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. COMPARISON WITH EXISTING METHOD The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. CONCLUSIONS Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness.
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Kim E, Yu JW, Kim B, Lim SH, Lee SH, Kim K, Son G, Jeon HA, Moon C, Sakong J, Choi JW. Refined prefrontal working memory network as a neuromarker for Alzheimer's disease. BIOMEDICAL OPTICS EXPRESS 2021; 12:7199-7222. [PMID: 34858710 PMCID: PMC8606140 DOI: 10.1364/boe.438926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/02/2021] [Accepted: 10/13/2021] [Indexed: 06/13/2023]
Abstract
Detecting Alzheimer's disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named "refined network," in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.
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Affiliation(s)
- Eunho Kim
- Department of Information and Communication Engineering, DGIST, Daegu 42988, Republic of Korea
- These authors equally contributed to this work
| | - Jin-Woo Yu
- Department of Information and Communication Engineering, DGIST, Daegu 42988, Republic of Korea
- These authors equally contributed to this work
| | - Bomin Kim
- Department of Information and Communication Engineering, DGIST, Daegu 42988, Republic of Korea
| | - Sung-Ho Lim
- Department of Information and Communication Engineering, DGIST, Daegu 42988, Republic of Korea
- Brain Engineering Convergence Research Center, DGIST, Daegu 42988, Republic of Korea
| | - Sang-Ho Lee
- Convergence Research Advanced Centre for Olfaction, DGIST, Daegu 42988, Republic of Korea
| | - Kwangsu Kim
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988, Republic of Korea
| | - Gowoon Son
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988, Republic of Korea
| | - Hyeon-Ae Jeon
- Brain Engineering Convergence Research Center, DGIST, Daegu 42988, Republic of Korea
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988, Republic of Korea
| | - Cheil Moon
- Brain Engineering Convergence Research Center, DGIST, Daegu 42988, Republic of Korea
- Convergence Research Advanced Centre for Olfaction, DGIST, Daegu 42988, Republic of Korea
- Department of Brain and Cognitive Sciences, DGIST, Daegu 42988, Republic of Korea
| | - Joon Sakong
- Department of Occupational and Environmental Medicine, Yeungnam University Hospital, Daegu 42415, Republic of Korea
- Department of Preventive Medicine and Public Health, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea
| | - Ji-Woong Choi
- Department of Information and Communication Engineering, DGIST, Daegu 42988, Republic of Korea
- Brain Engineering Convergence Research Center, DGIST, Daegu 42988, Republic of Korea
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Pan G, Xiao L, Bai Y, Wilson TW, Stephen JM, Calhoun VD, Wang YP. Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis. IEEE Trans Biomed Eng 2021; 68:2529-2539. [PMID: 33382644 DOI: 10.1109/tbme.2020.3048594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets. METHODS We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data. RESULTS After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods. CONCLUSION Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance. SIGNIFICANCE To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).
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Zhang Y, Xiao L, Zhang G, Cai B, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study. IEEE J Biomed Health Inform 2021; 25:1712-1723. [PMID: 32841133 PMCID: PMC7904970 DOI: 10.1109/jbhi.2020.3019421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits. Joint learning of multiple datasets can utilize their complementary information so as to improve learning performance, but it also gives rise to the challenge for data fusion to effectively integrate brain patterns elicited by multiple fMRI data. However, most of the current data fusion methods analyze each single dataset separately and further infer the relationship among them, which fail to utilize the multidimensional structure inherent across modalities and may ignore complex but important interactions. To address this issue, we propose a novel sparse tensor decomposition method to integrate multiple task-stimulus (paradigm) fMRI data. Seeing each paradigm fMRI as one modality, our proposed method considers the relationships across subjects and modalities simultaneously. In specific, a third-order tensor is first modeled by using the functional network connectivity (FNC) of subjects in multiple fMRI paradigms. A novel sparse tensor decomposition with the regularization terms is designed to factorize the tensor into a series of rank-one components, which can extract the shared components across modalities as the embedded features. The L2,1-norm regularizer (i.e., group sparsity) is enforced to select a few common features among multiple subjects. Validation of the proposed method is performed on realistic three paradigm fMRI datasets from the Philadelphia Neurodevelopmental Cohort (PNC) study, for the study of the relationship between the FNC and human cognitive abilities. Experimental results show our method outperforms several other competing methods in the prediction of individuals with different cognitive behaviors via the wide range achievement test (WRAT). Furthermore, our method discovers the FNC related to the cognitive behaviors, such as the connectivity associated with the default mode network (DMN) for three paradigms, and the connectivity between DMN and visual (VIS) domains within the emotion task.
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13
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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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14
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Cai B, Zhang G, Hu W, Zhang A, Zille P, Zhang Y, Stephen JM, Wilson TW, Calhoun VD, Wang YP. Refined measure of functional connectomes for improved identifiability and prediction. Hum Brain Mapp 2019; 40:4843-4858. [PMID: 31355994 DOI: 10.1002/hbm.24741] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/26/2019] [Accepted: 07/13/2019] [Indexed: 11/08/2022] Open
Abstract
Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.
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Affiliation(s)
- Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Wenxing Hu
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Pascal Zille
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
| | - Yipu Zhang
- School of Electronics and Control Engineering, Chang'an University, Xi'an, Shaanxi, China
| | - Julia M Stephen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, Nebraska
| | - 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.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana
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