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Cui W, Chen B, He J, Fan G, Wang S. Dynamic functional network connectivity in children with profound bilateral congenital sensorineural hearing loss. Pediatr Radiol 2024; 54:1738-1747. [PMID: 39134864 DOI: 10.1007/s00247-024-06022-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Functional magnetic resonance imaging (fMRI) studies have revealed extensive functional reorganization in patients with sensorineural hearing loss (SNHL). However, almost no study focuses on the dynamic functional connectivity after hearing loss. OBJECTIVE This study aimed to investigate dynamic functional connectivity changes in children with profound bilateral congenital SNHL under the age of 3 years. MATERIALS AND METHODS Thirty-two children with profound bilateral congenital SNHL and 24 children with normal hearing were recruited for the present study. Independent component analysis identified 18 independent components composing five resting-state networks. A sliding window approach was used to acquire dynamic functional matrices. Three states were identified using the k-means algorithm. Then, the differences in temporal properties and the variance of network efficiency between groups were compared. RESULTS The children with SNHL showed longer mean dwell time and decreased functional connectivity between the auditory network and sensorimotor network in state 3 (P < 0.05), which was characterized by relatively stronger functional connectivity between high-order resting-state networks and motion and perception networks. There was no difference in the variance of network efficiency. CONCLUSIONS These results indicated the functional reorganization due to hearing loss. This study also provided new perspectives for understanding the state-dependent connectivity patterns in children with SNHL.
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Affiliation(s)
- Wenzhuo Cui
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Boyu Chen
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Jiachuan He
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China
| | - Shanshan Wang
- Department of Radiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, PR China.
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Soleimani N, Iraji A, Pearlson G, Preda A, Calhoun VD. Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.606076. [PMID: 39131299 PMCID: PMC11312551 DOI: 10.1101/2024.08.01.606076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.
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Affiliation(s)
- Najme Soleimani
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, California, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
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3
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He X, Calhoun VD, Du Y. SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks. Neurosci Bull 2024; 40:905-920. [PMID: 38491231 DOI: 10.1007/s12264-024-01184-4] [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: 06/30/2023] [Accepted: 12/08/2023] [Indexed: 03/18/2024] Open
Abstract
Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.
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Affiliation(s)
- Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China.
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, USA.
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Soleimani N, Iraji A, van Erp TGM, Belger A, Calhoun VD. A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583731. [PMID: 38559041 PMCID: PMC10979844 DOI: 10.1101/2024.03.06.583731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.
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Affiliation(s)
- Najme Soleimani
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, UC Irvine, Irvine, California, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
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5
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Wiafe SL, Asante NO, Calhoun VD, Faghiri A. Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598720. [PMID: 38915498 PMCID: PMC11195172 DOI: 10.1101/2024.06.12.598720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.
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Affiliation(s)
- Sir-Lord Wiafe
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Nana O. Asante
- ETH Zürich, Zürich, Rämistrasse 101, Switzerland
- Ashesi University, 1 University Avenue Berekuso, Ghana
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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6
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Zürcher NR, Chen JE, Wey HY. PET-MRI Applications and Future Prospects in Psychiatry. J Magn Reson Imaging 2024. [PMID: 38838352 DOI: 10.1002/jmri.29471] [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: 03/01/2024] [Revised: 05/19/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
This article reviews the synergistic application of positron emission tomography-magnetic resonance imaging (PET-MRI) in neuroscience with relevance for psychiatry, particularly examining neurotransmission, epigenetics, and dynamic imaging methodologies. We begin by discussing the complementary insights that PET and MRI modalities provide into neuroreceptor systems, with a focus on dopamine, opioids, and serotonin receptors, and their implications for understanding and treating psychiatric disorders. We further highlight recent PET-MRI studies using a radioligand that enables the quantification of epigenetic enzymes, specifically histone deacetylases, in the brain in vivo. Imaging epigenetics is used to exemplify the impact the quantification of novel molecular targets may have, including new treatment approaches for psychiatric disorders. Finally, we discuss innovative designs involving functional PET using [18F]FDG (fPET-FDG), which provides detailed information regarding dynamic changes in glucose metabolism. Concurrent acquisitions of fPET-FDG and functional MRI provide a time-resolved approach to studying brain function, yielding simultaneous metabolic and hemodynamic information and thereby opening new avenues for psychiatric research. Collectively, the review underscores the potential of a multimodal PET-MRI approach to advance our understanding of brain structure and function in health and disease, which could improve clinical care based on objective neurobiological features and treatment response monitoring. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Nicole R Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Lurie Center for Autism, Massachusetts General Hospital, Lexington, Massachusetts, USA
| | - Jingyuan E Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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7
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [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: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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Bravo Balsa L, Abu-Akel A, Mevorach C. Dynamic functional connectivity in the right temporoparietal junction captures variations in male autistic trait expression. Autism Res 2024; 17:702-715. [PMID: 38456581 DOI: 10.1002/aur.3117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/21/2024] [Indexed: 03/09/2024]
Abstract
Autistic individuals can experience difficulties with attention reorienting and Theory of Mind (ToM), which are closely associated with anterior and posterior subdivisions of the right temporoparietal junction. While the link between these processes remains unclear, it is likely subserved by a dynamic crosstalk between these two subdivisions. We, therefore, examined the dynamic functional connectivity (dFC) between the anterior and posterior temporoparietal junction, as a biological marker of attention and ToM, to test its contribution to the manifestation of autistic trait expression in Autism Spectrum Condition (ASC). Two studies were conducted, exploratory (14 ASC, 15 TD) and replication (29 ASC, 29 TD), using resting-state fMRI data and the Social Responsiveness Scale (SRS) from the Autism Brain Imaging Data Exchange repository. Dynamic Independent Component Analysis was performed in both datasets using the CONN toolbox. An additional sliding-window analysis was performed in the replication study to explore different connectivity states (from highly negatively to highly positively correlated). Dynamic FC was reduced in ASC compared to TD adults in both the exploratory and replication datasets and was associated with increased SRS scores (especially in ASC). Regression analyses revealed that decreased SRS autistic expression was predicted by engagement of highly negatively correlated states, while engagement of highly positively correlated states predicted increased expression. These findings provided consistent evidence that the difficulties observed in ASC are associated with altered patterns of dFC between brain regions subserving attention reorienting and ToM processes and may serve as a biomarker of autistic trait expression.
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Affiliation(s)
- Laura Bravo Balsa
- Centre for Human Brain Health, University of Birmingham, Edgbaston, UK
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Ahmad Abu-Akel
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- Haifa Brain and Behavior Hub, University of Haifa, Haifa, Israel
| | - Carmel Mevorach
- Centre for Human Brain Health, University of Birmingham, Edgbaston, UK
- School of Psychology, University of Birmingham, Edgbaston, UK
- Centre for Developmental Science, University of Birmingham, Edgbaston, UK
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Sendi M, Fu Z, Harnett N, van Rooij S, Vergara V, Pizzagalli D, Daskalakis N, House S, Beaudoin F, An X, Neylan T, Clifford G, Jovanovic T, Linnstaedt S, Germine L, Bollen K, Rauch S, Haran J, Storrow A, Lewandowski C, Musey P, Hendry P, Sheikh S, Jones C, Punches B, Swor R, Gentile N, Murty V, Hudak L, Pascual J, Seamon M, Harris E, Chang A, Pearson C, Peak D, Merchant R, Domeier R, Rathlev N, O'Neil B, Sergot P, Sanchez L, Bruce S, Sheridan J, Harte S, Kessler R, Koenen K, McLean S, Stevens J, Calhoun V, Ressler K. Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma. RESEARCH SQUARE 2024:rs.3.rs-4004473. [PMID: 38496567 PMCID: PMC10942549 DOI: 10.21203/rs.3.rs-4004473/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
This study examines the association between brain dynamic functional network connectivity (dFNC) and current/future posttraumatic stress (PTS) symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.179, pcorrected= 0.021) and future (r=-0.166, pcorrected= 0.029) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.192, pcorrected = 0.021). We additionally observed that the association between the network dynamics of the inter-network brain state with symptom severity is more pronounced in females (r=-0.244, pcorrected = 0.014). Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger protective effect of inter-network brain states against symptom severity in females, underscoring the importance of sex differences.
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Affiliation(s)
| | - Zening Fu
- d Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University
| | | | | | | | | | | | | | - Francesca Beaudoin
- The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital
| | - Xinming An
- University of North Carolina at Chapel Hill
| | - Thomas Neylan
- San Francisco VA Healthcare System; University of California San Francisco
| | - Gari Clifford
- Emory University School of Medicine; Georgia Institute of Technology
| | | | | | | | | | | | - John Haran
- University of Massachusetts Medical School
| | | | | | | | | | | | | | - Brittany Punches
- University of Cincinnati College of Medicine & University of Cincinnati College of Nursing
| | | | | | | | | | - Jose Pascual
- Perelman School of Medicine at the University of Pennsylvania
| | | | | | | | | | | | | | | | | | | | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth
| | | | | | | | | | | | | | | | | | - Vince Calhoun
- Georgia Institute of Technology, Emory University and Georgia State University
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10
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Kuang LD, Li HQ, Zhang J, Gui Y, Zhang J. Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework. J Neural Eng 2024; 21:016032. [PMID: 38335544 DOI: 10.1088/1741-2552/ad27ee] [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: 09/12/2023] [Accepted: 02/09/2024] [Indexed: 02/12/2024]
Abstract
Objective.Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.Approach.The proposed SLRCPD approach imposes two constraints. First, the L1regularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.Main results.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.Significance.This study significantly excavates spatio-temporal patterns for schizophrenia disease.
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Affiliation(s)
- Li-Dan Kuang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - He-Qiang Li
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jianming Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Yan Gui
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
| | - Jin Zhang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
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Valizadeh A, Moassefi M, Nakhostin-Ansari A, Heidari Some'eh S, Hosseini-Asl H, Saghab Torbati M, Aghajani R, Maleki Ghorbani Z, Menbari-Oskouie I, Aghajani F, Mirzamohamadi A, Ghafouri M, Faghani S, Memari AH. Automated diagnosis of autism with artificial intelligence: State of the art. Rev Neurosci 2024; 35:141-163. [PMID: 37678819 DOI: 10.1515/revneuro-2023-0050] [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: 04/26/2023] [Accepted: 07/28/2023] [Indexed: 09/09/2023]
Abstract
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71-0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.
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Affiliation(s)
- Amir Valizadeh
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Mana Moassefi
- Neuroscience Institute, Tehran University of Medical Sciences, PO: 1419733141, Tehran, Iran
| | - Amin Nakhostin-Ansari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Soheil Heidari Some'eh
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Hossein Hosseini-Asl
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | | | - Reyhaneh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Zahra Maleki Ghorbani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Iman Menbari-Oskouie
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Faezeh Aghajani
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Research Development Center, Arash Women's Hospital, Tehran University of Medical Sciences, PO: 14695542, Tehran, Iran
| | - Alireza Mirzamohamadi
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
- Students' Scientific Research Center, Tehran University of Medical Sciences, PO: 1417755331, Tehran, Iran
| | - Mohammad Ghafouri
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
| | - Shahriar Faghani
- Shariati Hospital, Department of Radiology, Tehran University of Medical Sciences, PO: 1411713135, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, PO: 1416634793, Tehran, Iran
| | - Amir Hossein Memari
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, PO: 14395578, Tehran, Iran
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12
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Cattarinussi G, Di Giorgio A, Moretti F, Bondi E, Sambataro F. Dynamic functional connectivity in schizophrenia and bipolar disorder: A review of the evidence and associations with psychopathological features. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110827. [PMID: 37473954 DOI: 10.1016/j.pnpbp.2023.110827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/05/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
Alterations of functional network connectivity have been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). Recent studies also suggest that the temporal dynamics of functional connectivity (dFC) can be altered in these disorders. Here, we summarized the existing literature on dFC in SCZ and BD, and their association with psychopathological and cognitive features. We systematically searched PubMed, Web of Science, and Scopus for studies investigating dFC in SCZ and BD and identified 77 studies. Our findings support a general model of dysconnectivity of dFC in SCZ, whereas a heterogeneous picture arose in BD. Although dFC alterations are more severe and widespread in SCZ compared to BD, dysfunctions of a triple network system underlying goal-directed behavior and sensory-motor networks were present in both disorders. Furthermore, in SCZ, positive and negative symptoms were associated with abnormal dFC. Implications for understanding the pathophysiology of disorders, the role of neurotransmitters, and treatments on dFC are discussed. The lack of standards for dFC metrics, replication studies, and the use of small samples represent major limitations for the field.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Federica Moretti
- Department of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
| | - Emi Bondi
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Italy; Padova Neuroscience Center, University of Padova, Italy.
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14
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Lyu W, Wu Y, Huang H, Chen Y, Tan X, Liang Y, Ma X, Feng Y, Wu J, Kang S, Qiu S, Yap PT. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals. Cogn Neurodyn 2023; 17:1525-1539. [PMID: 37969945 PMCID: PMC10640562 DOI: 10.1007/s11571-022-09899-8] [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: 07/04/2022] [Revised: 09/11/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022] Open
Abstract
An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu China
| | - Haoming Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yuna Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei Province, Jingzhou, Hubei China
| | - Yue Feng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Jinjian Wu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shangyu Kang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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15
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Cattarinussi G, Grimaldi DA, Sambataro F. Spontaneous Brain Activity Alterations in First-Episode Psychosis: A Meta-analysis of Functional Magnetic Resonance Imaging Studies. Schizophr Bull 2023; 49:1494-1507. [PMID: 38029279 PMCID: PMC10686347 DOI: 10.1093/schbul/sbad044] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
BACKGROUND AND HYPOTHESIS Several studies have shown that spontaneous brain activity, including the total and fractional amplitude of low-frequency fluctuations (LFF) and regional homogeneity (ReHo), is altered in psychosis. Nonetheless, neuroimaging results show a high heterogeneity. For this reason, we gathered the extant literature on spontaneous brain activity in first-episode psychosis (FEP), where the effects of long-term treatment and chronic disease are minimal. STUDY DESIGN A systematic research was conducted on PubMed, Scopus, and Web of Science to identify studies exploring spontaneous brain activity and local connectivity in FEP estimated using functional magnetic resonance imaging. 20 LFF and 15 ReHo studies were included. Coordinate-Based Activation Likelihood Estimation Meta-Analyses stratified by brain measures, age (adolescent vs adult), and drug-naïve status were performed to identify spatially-convergent alterations in spontaneous brain activity in FEP. STUDY RESULTS We found a significant increase in LFF in FEP compared to healthy controls (HC) in the right striatum and in ReHo in the left striatum. When pooling together all studies on LFF and ReHo, spontaneous brain activity was increased in the bilateral striatum and superior and middle frontal gyri and decreased in the right precentral gyrus and the right inferior frontal gyrus compared to HC. These results were also replicated in the adult and drug-naïve samples. CONCLUSIONS Abnormalities in the frontostriatal circuit are present in early psychosis independently of treatment status. Our findings support the view that altered frontostriatal can represent a core neural alteration of the disorder and could be a target of treatment.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, Padua, Italy
| | | | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, Padua, Italy
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16
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Luo L, Li Q, Wang Y, He N, Wang Y, You W, Zhang Q, Long F, Chen L, Zhao Y, Yao L, Sweeney JA, Gong Q, Li F. Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia. Schizophr Bull 2023; 49:1387-1398. [PMID: 37030006 PMCID: PMC10483459 DOI: 10.1093/schbul/sbad042] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) and schizophrenia have distinct but also overlapping symptoms. Few studies have examined the shared and disorder-specific disturbances in dynamic brain function in the 2 disorders. STUDY DESIGN Resting-state functional magnetic resonance imaging data of 31 patients with OCD and 49 patients with schizophrenia, all untreated, and 45 healthy controls (HCs) were analyzed using spatial group independent component (IC) analysis. Time-varying degree centrality patterns across the whole brain were clustered into 3 reoccurring states, and state transition metrics were obtained. We further explored regional temporal variability of degree centrality for each IC across all time windows. STUDY RESULTS Patients with OCD and patients with schizophrenia both showed decreased occurrence of a state having the highest centrality in the sensorimotor and auditory networks. Additionally, patients with OCD and patients with schizophrenia both exhibited reduced dynamics of degree centrality in the superior frontal gyrus than controls, while dynamic degree centrality of the cerebellum was lower in patients with schizophrenia than with OCD and HCs. Altered dynamics of degree centrality nominally correlated with symptom severity in both patient groups. CONCLUSIONS Our study provides evidence of transdiagnostic and clinically relevant functional brain abnormalities across OCD and schizophrenia in neocortex, as well as functional dynamic alterations in the cerebellum specific to schizophrenia. These findings add to the recognition of overlap in neocortical alterations in the 2 disorders, and indicate that cerebellar alterations in schizophrenia may be specifically important in schizophrenia pathophysiology via impact on cerebellar thalamocortical circuitry.
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Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Radiology, West China Second Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Lizhou Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
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17
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Wang Y, Long H, Zhou Q, Bo T, Zheng J. PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting. Comput Biol Med 2023; 163:107184. [PMID: 37356292 DOI: 10.1016/j.compbiomed.2023.107184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 06/27/2023]
Abstract
Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.
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Affiliation(s)
- Yibin Wang
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Haixia Long
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Qianwei Zhou
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
| | - Tao Bo
- Scientific Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Jianwei Zheng
- College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
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18
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Wen G, Cao P, Liu L, Yang J, Zhang X, Wang F, Zaiane OR. Graph Self-Supervised Learning With Application to Brain Networks Analysis. IEEE J Biomed Health Inform 2023; 27:4154-4165. [PMID: 37159311 DOI: 10.1109/jbhi.2023.3274531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The less training data and insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is significant to construct a learning framework that can capture more information in limited data and insufficient supervision. To address these issues, we focus on self-supervised learning and aim to generalize the self-supervised learning to the brain networks, which are non-Euclidean graph data. More specifically, we propose an ensemble masked graph self-supervised framework named BrainGSLs, which incorporates 1) a local topological-aware encoder that takes the partially visible nodes as input and learns these latent representations, 2) a node-edge bi-decoder that reconstructs the masked edges by the representations of both the masked and visible nodes, 3) a signal representation learning module for capturing temporal representations from BOLD signals and 4) a classifier used for the classification. We evaluate our model on three real medical clinical applications: diagnosis of Autism Spectrum Disorder (ASD), diagnosis of Bipolar Disorder (BD) and diagnosis of Major Depressive Disorder (MDD). The results suggest that the proposed self-supervised training has led to remarkable improvement and outperforms state-of-the-art methods. Moreover, our method is able to identify the biomarkers associated with the diseases, which is consistent with the previous studies. We also explore the correlation of these three diseases and find the strong association between ASD and BD. To the best of our knowledge, our work is the first attempt of applying the idea of self-supervised learning with masked autoencoder on the brain network analysis.
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19
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Zhang M, Chowdhury S, Saggar M. Temporal Mapper: Transition networks in simulated and real neural dynamics. Netw Neurosci 2023; 7:431-460. [PMID: 37397880 PMCID: PMC10312258 DOI: 10.1162/netn_a_00301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/07/2022] [Indexed: 07/26/2023] Open
Abstract
Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.
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Affiliation(s)
- Mengsen Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Samir Chowdhury
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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20
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Ramirez-Mahaluf JP, Tepper Á, Alliende LM, Mena C, Castañeda CP, Iruretagoyena B, Nachar R, Reyes-Madrigal F, León-Ortiz P, Mora-Durán R, Ossandon T, Gonzalez-Valderrama A, Undurraga J, de la Fuente-Sandoval C, Crossley NA. Dysconnectivity in Schizophrenia Revisited: Abnormal Temporal Organization of Dynamic Functional Connectivity in Patients With a First Episode of Psychosis. Schizophr Bull 2023; 49:706-716. [PMID: 36472382 PMCID: PMC10154721 DOI: 10.1093/schbul/sbac187] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND HYPOTHESIS Abnormal functional connectivity between brain regions is a consistent finding in schizophrenia, including functional magnetic resonance imaging (fMRI) studies. Recent studies have highlighted that connectivity changes in time in healthy subjects. We here examined the temporal changes in functional connectivity in patients with a first episode of psychosis (FEP). Specifically, we analyzed the temporal order in which whole-brain organization states were visited. STUDY DESIGN Two case-control studies, including in each sample a subgroup scanned a second time after treatment. Chilean sample included 79 patients with a FEP and 83 healthy controls. Mexican sample included 21 antipsychotic-naïve FEP patients and 15 healthy controls. Characteristics of the temporal trajectories between whole-brain functional connectivity meta-states were examined via resting-state functional MRI using elements of network science. We compared the cohorts of cases and controls and explored their differences as well as potential associations with symptoms, cognition, and antipsychotic medication doses. STUDY RESULTS We found that the temporal sequence in which patients' brain dynamics visited the different states was more redundant and segregated. Patients were less flexible than controls in changing their network in time from different configurations, and explored the whole landscape of possible states in a less efficient way. These changes were related to the dose of antipsychotics the patients were receiving. We replicated the relationship with antipsychotic medication in the antipsychotic-naïve FEP sample scanned before and after treatment. CONCLUSIONS We conclude that psychosis is related to a temporal disorganization of the brain's dynamic functional connectivity, and this is associated with antipsychotic medication use.
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Affiliation(s)
- Juan P Ramirez-Mahaluf
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ángeles Tepper
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Luz Maria Alliende
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carlos Mena
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Carmen Paz Castañeda
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Barbara Iruretagoyena
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Ruben Nachar
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
| | - Francisco Reyes-Madrigal
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Pablo León-Ortiz
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Ricardo Mora-Durán
- Emergency Department, Hospital Fray Bernardino Álvarez, Mexico City, Mexico
| | - Tomas Ossandon
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Center for Integrative Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alfonso Gonzalez-Valderrama
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
- School of Medicine, Universidad Finis Terrae, Santiago, Chile
| | - Juan Undurraga
- Early Intervention Program, Instituto Psiquiátrico Dr. J Horwitz Barak, Santiago, Chile
- Department of Neurology and Psychiatry, Faculty of Medicine, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
- Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Nicolas A Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Católica de, Santiago, Chile
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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21
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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22
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Uscătescu LC, Kronbichler M, Said-Yürekli S, Kronbichler L, Calhoun V, Corbera S, Bell M, Pelphrey K, Pearlson G, Assaf M. Intrinsic neural timescales in autism spectrum disorder and schizophrenia. A replication and direct comparison study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:18. [PMID: 36997542 PMCID: PMC10063601 DOI: 10.1038/s41537-023-00344-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/06/2023] [Indexed: 04/03/2023]
Abstract
Intrinsic neural timescales (INT) reflect the duration for which brain areas store information. A posterior-anterior hierarchy of increasingly longer INT has been revealed in both typically developed individuals (TD), as well as persons diagnosed with autism spectrum disorder (ASD) and schizophrenia (SZ), though INT are, overall, shorter in both patient groups. In the present study, we aimed to replicate previously reported group differences by comparing INT of TD to ASD and SZ. We partially replicated the previously reported result, showing reduced INT in the left lateral occipital gyrus and the right post-central gyrus in SZ compared to TD. We also directly compared the INT of the two patient groups and found that these same two areas show significantly reduced INT in SZ compared to ASD. Previously reported correlations between INT and symptom severity were not replicated in the current project. Our findings serve to circumscribe the brain areas that can potentially play a determinant role in observed sensory peculiarities in ASD and SZ.
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Affiliation(s)
| | - Martin Kronbichler
- Centre for Cognitive Neuroscience & Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Sarah Said-Yürekli
- Centre for Cognitive Neuroscience & Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Lisa Kronbichler
- Centre for Cognitive Neuroscience & Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian-Doppler Medical University Hospital, Paracelsus Medical University, Salzburg, Austria
- Department of Psychiatry, Psychotherapy & Psychosomatics, Christian-Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Silvia Corbera
- Central Connecticut State University, Department of Psychological Science, New Britain, CT, USA
| | - Morris Bell
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Kevin Pelphrey
- University of Virginia, Department of Neurology, Charlottesville, VA, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
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23
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Wang C, Yang L, Lin Y, Wang C, Tian P. Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis. Front Integr Neurosci 2023; 16:922577. [PMID: 36743477 PMCID: PMC9892631 DOI: 10.3389/fnint.2022.922577] [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: 04/18/2022] [Accepted: 12/23/2022] [Indexed: 01/20/2023] Open
Abstract
Background Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients. Methods We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes. Results Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN. Conclusion Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.
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Affiliation(s)
- Chaoyan Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu Yang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanan Lin
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peichao Tian
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China,*Correspondence: Peichao Tian,
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24
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Kunda M, Zhou S, Gong G, Lu H. Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:55-65. [PMID: 36054402 DOI: 10.1109/tmi.2022.3203899] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Machine learning has been widely used to develop classification models for autism spectrum disorder (ASD) using neuroimaging data. Recently, studies have shifted towards using large multi-site neuroimaging datasets to boost the clinical applicability and statistical power of results. However, the classification performance is hindered by the heterogeneous nature of agglomerative datasets. In this paper, we propose new methods for multi-site autism classification using the Autism Brain Imaging Data Exchange (ABIDE) dataset. We firstly propose a new second-order measure of functional connectivity (FC) named as Tangent Pearson embedding to extract better features for classification. Then we assess the statistical dependence between acquisition sites and FC features, and take a domain adaptation approach to minimize the site dependence of FC features to improve classification. Our analysis shows that 1) statistical dependence between site and FC features is statistically significant at the 5% level, and 2) extracting second-order features from neuroimaging data and minimizing their site dependence can improve over state-of-the-art (SOTA) classification results, achieving a classification accuracy of 73%. The code is available at https://github.com/kundaMwiza/fMRI-site-adaptation.
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25
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Xie Y, Guan M, He Y, Wang Z, Ma Z, Fang P, Wang H. The Static and dynamic functional connectivity characteristics of the left temporoparietal junction region in schizophrenia patients with auditory verbal hallucinations during low-frequency rTMS treatment. Front Psychiatry 2023; 14:1071769. [PMID: 36761865 PMCID: PMC9907463 DOI: 10.3389/fpsyt.2023.1071769] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Auditory verbal hallucinations (AVH) are a core symptom of schizophrenia. Low-frequency (e.g., 1 Hz) repetitive transcranial magnetic stimulation (rTMS) targeting language processing regions (e.g., left TPJ) has been evident as a potential treatment for AVH. However, the underlying neural mechanisms of the rTMS treatment effect remain unclear. The present study aimed to investigate the effects of 1 Hz rTMS on functional connectivity (FC) of the temporoparietal junction area (TPJ) seed with the whole brain in schizophrenia patients with AVH. METHODS Using a single-blind placebo-controlled randomized clinical trial, 55 patients with AVH were randomly divided into active treatment group (n = 30) or placebo group (n = 25). The active treatment group receive 15-day 1 Hz rTMS stimulation to the left TPJ, whereas the placebo group received sham rTMS stimulation to the same site. Resting-state fMRI scans and clinical measures were acquired for all patients before and after treatment. The seed-based (left TPJ) static and DFC was used to assess the connectivity characteristics during rTMS treatment in patients with AVH. RESULTS Overall, symptom improvement following 1 Hz rTMS treatment was found in the active treatment group, whereas no change occurred in the placebo group. Moreover, decreased static FC (SFC) of the left TPJ with the right temporal lobes, as well as increased SFC with the prefrontal cortex and subcortical structure were observed in active rTMS group. Increased dynamic FC (DFC) of the left TPJ with frontoparietal areas was also found in the active rTMS group. However, seed-based SFC and DFC were reduced to a great extent in the placebo group. In addition, these changed FC (SFC) strengths in the active rTMS group were associated with reduced severity of clinical outcomes (e.g., positive symptoms). CONCLUSION The application of 1 Hz rTMS over the left TPJ may affect connectivity characteristics of the targeted region and contribute to clinical improvement, which shed light on the therapeutic effect of rTMS on schizophrenia with AVH.
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Affiliation(s)
- Yuanjun Xie
- School of Education, Xinyang College, Xinyang, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Muzhen Guan
- Department of Mental Health, Xi'an Medical University, Xi'an, China
| | - Ying He
- Department of Psychiatry, Second Affiliated Hospital, Army Medical University, Chongqing, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhujing Ma
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Peng Fang
- Department of Military Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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26
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Marschall TM, Koops S, Brederoo SG, Cabral J, Ćurčić-Blake B, Sommer IEC. Time varying dynamics of hallucinations in clinical and non-clinical voice-hearers. Neuroimage Clin 2023; 37:103351. [PMID: 36805417 PMCID: PMC9969260 DOI: 10.1016/j.nicl.2023.103351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/24/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
Auditory verbal hallucinations (AVH) are frequently associated with psychotic disorders, yet also occur in non-clinical voice-hearers. AVH in this group are similar to those within clinical voice-hearers in terms of several phenomenological aspects, but non-clinical voice-hearers report to have more control over their AVH and attribute less emotional valence to them. These dissimilarities may stem from differences on the neurobiological level, as it is still under debate whether the mechanisms involved in AVH are the same in clinical and non-clinical voice-hearers. In this study, 21 clinical and 21 non-clinical voice-hearers indicated the onset and offsets of AVH during an fMRI scan. Using a method called leading eigenvector dynamics analysis (LEiDA), we examined time-varying dynamics of functional connectivity involved in AVH with a sub-second temporal resolution. We assessed differences between groups, and between hallucination and rest periods in dwell time, switching frequency, probability of occurrence, and transition probabilities of nine recurrent states of functional connectivity with a permutation ANOVA. Deviations in dwell times, switching frequencies, and switch probabilities in the hallucination period indicated more erratic dynamics during this condition regardless of their clinical status. Post-hoc analyses of the dwell times exhibited the most distinct differences between the rest and hallucination condition for the non-clinical sample, suggesting stronger differences between the two conditions in this group. Overall, these findings suggest that the neurobiological mechanisms involved in AVH are similar in clinical and non-clinical individuals.
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Affiliation(s)
- Theresa M Marschall
- University of Groningen, Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands.
| | - Sanne Koops
- University of Groningen, Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Sanne G Brederoo
- University of Groningen, Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Branislava Ćurčić-Blake
- University of Groningen, Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Iris E C Sommer
- University of Groningen, Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
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27
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Mahmood U, Fu Z, Ghosh S, Calhoun V, Plis S. Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI. Neuroimage 2022; 264:119737. [PMID: 36356823 PMCID: PMC9844250 DOI: 10.1016/j.neuroimage.2022.119737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/01/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022] Open
Abstract
Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.
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Affiliation(s)
- Usman Mahmood
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA.
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Satrajit Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA; Georgia Institute of Technology, Department of Electrical and Computer Engineering, Atlanta, GA, USA
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Georgia State University, Department of Computer Science, Atlanta, GA, USA
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28
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Impaired dynamic functional brain properties and their relationship to symptoms in never treated first-episode patients with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:90. [PMID: 36309537 PMCID: PMC9617869 DOI: 10.1038/s41537-022-00299-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022]
Abstract
Studies of dynamic functional connectivity (dFC) and topology can provide novel insights into the neurophysiology of brain dysfunction in schizophrenia and its relation to core symptoms of psychosis. Limited investigations of these disturbances have been conducted with never-treated first-episode patients to avoid the confounds of treatment or chronic illness. Therefore, we recruited 95 acutely ill, first-episode, never-treated patients with schizophrenia and examined brain dFC patterns relative to healthy controls using resting-state functional magnetic resonance imaging and a sliding-window approach. We compared the dynamic attributes at the group level and found patients spent more time in a hypoconnected state and correspondingly less time in a hyperconnected state. Patients demonstrated decreased dynamics of nodal efficiency and eigenvector centrality (EC) in the right medial prefrontal cortex, which was associated with psychosis severity reflected in Positive and Negative Syndrome Scale ratings. We also observed increased dynamics of EC in temporal and sensorimotor regions. These findings were supported by validation analysis. To supplement the group comparison analyses, a support vector classifier was used to identify the dynamic attributes that best distinguished patients from controls at the individual level. Selected features for case-control classification were highly coincident with the properties having significant between-group differences. Our findings provide novel neuroimaging evidence about dynamic characteristics of brain physiology in acute schizophrenia. The clinically relevant atypical pattern of dynamic shifting between brain states in schizophrenia may represent a critical aspect of illness pathophysiology underpinning its defining cognitive, behavioral, and affective features.
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29
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Xu M, Zhang X, Li Y, Chen S, Zhang Y, Zhou Z, Lin S, Dong T, Hou G, Qiu Y. Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning. Transl Psychiatry 2022; 12:383. [PMID: 36097160 PMCID: PMC9467986 DOI: 10.1038/s41398-022-02147-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 11/09/2022] Open
Abstract
Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.
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Affiliation(s)
- Manxi Xu
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China ,grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Xiaojing Zhang
- grid.263488.30000 0001 0472 9649Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, 518060 People’s Republic of China
| | - Yanqing Li
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China
| | - Shengli Chen
- grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Yingli Zhang
- grid.452897.50000 0004 6091 8446Department of Psychiatry, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020 People’s Republic of China
| | - Zhifeng Zhou
- grid.452897.50000 0004 6091 8446Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020 People’s Republic of China
| | - Shiwei Lin
- grid.33199.310000 0004 0368 7223Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000 People’s Republic of China
| | - Tianfa Dong
- grid.410737.60000 0000 8653 1072Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, People’s Republic of China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518020, People's Republic of China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, 518000, People's Republic of China.
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30
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Qiao J, Wang R, Liu H, Xu G, Wang Z. Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer’s disease and autism spectrum disorder. Front Aging Neurosci 2022; 14:912895. [PMID: 36110425 PMCID: PMC9468323 DOI: 10.3389/fnagi.2022.912895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/05/2022] [Indexed: 11/16/2022] Open
Abstract
The dynamic functional connectivity (dFC) in functional magnetic resonance imaging (fMRI) is beneficial for the analysis and diagnosis of neurological brain diseases. The dFCs between regions of interest (ROIs) are generally delineated by a specific template and clustered into multiple different states. However, these models inevitably fell into the model-driven self-contained system which ignored the diversity at spatial level and the dynamics at time level of the data. In this study, we proposed a spatial and time domain feature extraction approach for Alzheimer’s disease (AD) and autism spectrum disorder (ASD)-assisted diagnosis which exploited the dynamic connectivity among independent functional sub networks in brain. Briefly, independent sub networks were obtained by applying spatial independent component analysis (SICA) to the preprocessed fMRI data. Then, a sliding window approach was used to segment the time series of the spatial components. After that, the functional connections within the window were obtained sequentially. Finally, a temporal signal-sensitive long short-term memory (LSTM) network was used for classification. The experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets showed that the proposed method effectively predicted the disease at the early stage and outperformed the existing algorithms. The dFCs between the different components of the brain could be used as biomarkers for the diagnosis of diseases such as AD and ASD, providing a reliable basis for the study of brain connectomics.
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Affiliation(s)
- Jianping Qiao
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
- *Correspondence: Jianping Qiao,
| | - Rong Wang
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Hongjia Liu
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Guangrun Xu
- Department of Neurology, Qilu Hospital of Shandong University, Jinan, China
- Guangrun Xu,
| | - Zhishun Wang
- Department of Psychiatry, Columbia University, New York, NY, United States
- Zhishun Wang,
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31
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Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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32
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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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33
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Hyatt CJ, Wexler BE, Pittman B, Nicholson A, Pearlson GD, Corbera S, Bell MD, Pelphrey K, Calhoun VD, Assaf M. Atypical Dynamic Functional Network Connectivity State Engagement during Social-Emotional Processing in Schizophrenia and Autism. Cereb Cortex 2022; 32:3406-3422. [PMID: 34875687 PMCID: PMC9376868 DOI: 10.1093/cercor/bhab423] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 01/30/2023] Open
Abstract
Autism spectrum disorder (ASD) and schizophrenia (SZ) are separate clinical entities but share deficits in social-emotional processing and static neural functional connectivity patterns. We compared patients' dynamic functional network connectivity (dFNC) state engagement with typically developed (TD) individuals during social-emotional processing after initially characterizing such dynamics in TD. Young adults diagnosed with ASD (n = 42), SZ (n = 41), or TD (n = 55) completed three functional MRI runs, viewing social-emotional videos with happy, sad, or neutral content. We examined dFNC of 53 spatially independent networks extracted using independent component analysis and applied k-means clustering to windowed dFNC matrices, identifying four unique whole-brain dFNC states. TD showed differential engagement (fractional time, mean dwell time) in three states as a function of emotion. During Happy videos, patients spent less time than TD in a happy-associated state and instead spent more time in the most weakly connected state. During Sad videos, only ASD spent more time than TD in a sad-associated state. Additionally, only ASD showed a significant relationship between dFNC measures and alexithymia and social-emotional recognition task scores, potentially indicating different neural processing of emotions in ASD and SZ. Our results highlight the importance of examining temporal whole-brain reconfiguration of FNC, indicating engagement in unique emotion-specific dFNC states.
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Affiliation(s)
- Christopher J Hyatt
- Address correspondence to Christopher J. Hyatt, PhD, Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, Hartford, CT, USA.
| | - Bruce E Wexler
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Brian Pittman
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Alycia Nicholson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
- Department of Psychiatry and Neuroscience, School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Silvia Corbera
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06510, USA
- Department of Psychological Science, Central Connecticut State University, New Britain, CT 06050, USA
| | - Morris D Bell
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06510, USA
- Department of Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Kevin Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA 22903, 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, GA 30303, USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT 06510, USA
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34
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Farina EA, Assaf M, Corbera S, Chen CM. Factors Related to Passive Social Withdrawal and Active Social Avoidance in Schizophrenia. J Nerv Ment Dis 2022; 210:490-496. [PMID: 35766542 PMCID: PMC9243431 DOI: 10.1097/nmd.0000000000001502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Social withdrawal in schizophrenia may be a result of "passive" motivation (reduced drive to engage) or "active" motivation (increased drive to avoid). We conducted a cross-sectional, between-subjects study using self-report measures and social cognition tasks to evaluate the relationships between motivational subtypes, social abilities, and social functioning in schizophrenia spectrum (n = 52, ages 19-34). Regression models showed significant differences in passive and active withdrawal. Passive, but not active, motivation predicted social functioning as measured by a clinical interview. This suggests that motivation, especially passive type, plays an important role in social withdrawal in schizophrenia. However, on a self-report measure of social functioning, neither passive nor active motivation predicted outcomes, suggesting a potential disconnect between observer versus self-report measures when assessing social motivation. Furthermore, performance on tasks of social abilities did not predict motivation, which supports the idea that motivation is distinct from social ability and should be specifically addressed in treatment.
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Affiliation(s)
- Emily A. Farina
- Department of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT, USA
- Olin Neuropsychiatry Research Center, Hartford Hospital, 400 Washington Street, Hartford, CT, USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Hartford Hospital, 400 Washington Street, Hartford, CT, USA
| | - Silvia Corbera
- Department of Psychological Science, Central Connecticut State University, New Britain, CT, USA
| | - Chi-Ming Chen
- Department of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Unit 1020, Storrs, CT, USA
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35
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Xue SW, Kuai C, Xiao Y, Zhao L, Lan Z. Abnormal Dynamic Functional Connectivity of the Left Rostral Hippocampus in Predicting Antidepressant Efficacy in Major Depressive Disorder. Psychiatry Investig 2022; 19:562-569. [PMID: 35903058 PMCID: PMC9334807 DOI: 10.30773/pi.2021.0386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/06/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Some pharmacological treatments are ineffective in parts of patients with major depressive disorder (MDD), hence this needs prediction of effective treatment responses. The study aims to examine the relationship between dynamic functional connectivity (dFC) of the hippocampal subregion and antidepressant improvement of MDD patients and to estimate the capability of dFC to predict antidepressant efficacy. METHODS The data were from 70 MDD patients and 43 healthy controls (HC); the dFC of hippocampal subregions was estimated by sliding-window approach based on resting-state functional magnetic resonance imaging (R-fMRI). After 3 months treatment, 36 patients underwent second R-fMRI scan and were then divided into the response group and non-response group according to clinical responses. RESULTS The result manifested that MDD patients exhibited lower mean dFC of the left rostral hippocampus (rHipp.l) compared with HC. After 3 months therapy, the response group showed lower dFC of rHipp.l compared with the non-response group. The dFC of rHipp.l was also negatively correlated with the reduction rate of Hamilton Depression Rating Scale. CONCLUSION These findings highlighted the importance of rHipp in MDD from the dFC perspective. Detection and estimation of these changes might demonstrate helpful for comprehending the pathophysiological mechanism and for assessment of treatment reaction of MDD.
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Affiliation(s)
- Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Yang Xiao
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Lei Zhao
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
| | - Zhihui Lan
- Center for Cognition and Brain Disorders, The Affiliated Hospital and Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China.,Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
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36
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Miller RL, Vergara VM, Pearlson GD, Calhoun VD. Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs. Front Neurosci 2022; 16:770468. [PMID: 35516809 PMCID: PMC9063321 DOI: 10.3389/fnins.2022.770468] [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: 09/03/2021] [Accepted: 01/24/2022] [Indexed: 11/28/2022] Open
Abstract
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.
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Affiliation(s)
- Robyn L. Miller
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- *Correspondence: Robyn L. Miller,
| | - Victor M. Vergara
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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37
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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38
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Farinha M, Amado C, Morgado P, Cabral J. Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task. Front Neurosci 2022; 16:821179. [PMID: 35360175 PMCID: PMC8963765 DOI: 10.3389/fnins.2022.821179] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/10/2022] [Indexed: 01/10/2023] Open
Abstract
Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.
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Affiliation(s)
- Miguel Farinha
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Centro Clínico Académico Hospital de Braga, Braga, Portugal
- *Correspondence: Miguel Farinha
| | - Conceição Amado
- Department of Mathematics and CEMAT, Instituto Superior Tècnico, University of Lisbon, Lisbon, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Centro Clínico Académico Hospital de Braga, Braga, Portugal
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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39
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Jutla A, Foss-Feig J, Veenstra-VanderWeele J. Autism spectrum disorder and schizophrenia: An updated conceptual review. Autism Res 2022; 15:384-412. [PMID: 34967130 PMCID: PMC8931527 DOI: 10.1002/aur.2659] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/08/2021] [Accepted: 12/12/2021] [Indexed: 12/19/2022]
Abstract
Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate disorders, with distinct clinical profiles and natural histories. ASD, typically diagnosed in childhood, is characterized by restricted or repetitive interests or behaviors and impaired social communication, and it tends to have a stable course. SCZ, typically diagnosed in adolescence or adulthood, is characterized by hallucinations and delusions, and tends to be associated with declining function. However, youth with ASD are three to six times more likely to develop SCZ than their neurotypical counterparts, and increasingly, research has shown that ASD and SCZ converge at several levels. We conducted a systematic review of studies since 2013 relevant to understanding this convergence, and present here a narrative synthesis of key findings, which we have organized into four broad categories: symptoms and behavior, perception and cognition, biomarkers, and genetic and environmental risk. We then discuss opportunities for future research into the phenomenology and neurobiology of overlap between ASD and SCZ. Understanding this overlap will allow for researchers, and eventually clinicians, to understand the factors that may make a child with ASD vulnerable to developing SCZ. LAY SUMMARY: Autism spectrum disorder and schizophrenia are distinct diagnoses, but people with autism and people with schizophrena share several characteristics. We review recent studies that have examined these areas of overlap, and discuss the kinds of studies we will need to better understand how these disorders are related. Understanding this will be important to help us identify which autistic children are at risk of developing schizophrenia.
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Affiliation(s)
- Amandeep Jutla
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
| | - Jennifer Foss-Feig
- Seaver Autism Center for Research and Treatment, Icahn
School of Medicine at Mount Sinai, Department of Psychiatry, 1 Gustave L. Levy
Place, Box 1230, New York, NY 10029, United States
| | - Jeremy Veenstra-VanderWeele
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
- Center for Autism and the Developing Brain, New
York-Presbyterian Westchester Behavioral Health Center, 21 Bloomingdale Road, White
Plains, NY 10605, United States
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40
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Zhao L, Xue SW, Sun YK, Lan Z, Zhang Z, Xue Y, Wang X, Jin Y. Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder. J Affect Disord 2022; 299:504-512. [PMID: 34953921 DOI: 10.1016/j.jad.2021.12.093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/15/2021] [Accepted: 12/19/2021] [Indexed: 12/28/2022]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties with social communication and restricted or repetitive patterns of behavior. This disorder was characterized by widespread abnormalities involving distributed brain networks. As one such key network node, the insular cortex has been regarded as a research focus of ASD neuropathology. The insula is a functionally complex brain structure. However, it is not fully clear if dynamic characteristics of resting-state functional magnetic resonance imaging (R-fMRI) signals in insular heterogeneous could be used to depict abnormalities in ASD. To address this question, we investigated dynamic functional connectivity (dFC) of 12 insular subregions. Data were obtained from 44 individuals with ASD and 65 typically developing age-matched controls (TDC). We assessed dFC by sliding-window method and quantified its temporal variability. Multivariable linear regression models were constructed to determine whether dFC support complementary information about symptom severity of individuals with ASD rather than static functional connectivity (sFC). The results showed that individuals with ASD exhibited dFC and sFC alterations in distinct insular subregions. Some brain regions showed only abnormal dFC but not sFC with insular subregions. These abnormal dFC could significantly predict the symptom severity of individuals with ASD. Our findings might advance our knowledge about the potential of insular heterogeneity and dynamic characteristics in understanding the neuropathology mechanism of ASD and in developing neuroimaging biomarkers for clinical applications.
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Affiliation(s)
- Lei Zhao
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Shao-Wei Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China.
| | - Yun-Kai Sun
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Zhihui Lan
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Ziqi Zhang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yichen Xue
- Centre for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, No.2318, Yuhangtang Rd, Hangzhou, Zhejiang 311121, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 311121, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| | - Xuan Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Jin
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, China
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41
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Lin P, Zang S, Bai Y, Wang H. Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model. Front Hum Neurosci 2022; 16:774921. [PMID: 35211000 PMCID: PMC8861306 DOI: 10.3389/fnhum.2022.774921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD.
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Affiliation(s)
- Pingting Lin
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Shiyi Zang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Yi Bai
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
| | - Haixian Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
- Research Center for Learning Science, Southeast University, Nanjing, China
- *Correspondence: Haixian Wang,
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Núñez P, Gomez C, Rodríguez-González V, Hillebrand A, Tewarie P, Gomez-Pilar J, Molina V, Hornero R, Poza J. Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task. J Neural Eng 2022; 19. [PMID: 35108688 DOI: 10.1088/1741-2552/ac514e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/02/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information. APPROACH A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls. MAIN RESULTS The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations. SIGNIFICANCE Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.
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Affiliation(s)
- Pablo Núñez
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
| | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47011, SPAIN
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, 1081 HV, NETHERLANDS
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre Amsterdam, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, Noord-Holland, 1081 HV, NETHERLANDS
| | - Javier Gomez-Pilar
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Vicente Molina
- Universidad de Valladolid, School of Medicine, University of Valladolid, 47005 - Valladolid, Valladolid, 47002, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, University of Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
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Xue K, Chen J, Wei Y, Chen Y, Han S, Wang C, Zhang Y, Song X, Cheng J. Altered dynamic functional connectivity of auditory cortex and medial geniculate nucleus in first-episode, drug-naïve schizophrenia patients with and without auditory verbal hallucinations. Front Psychiatry 2022; 13:963634. [PMID: 36159925 PMCID: PMC9489854 DOI: 10.3389/fpsyt.2022.963634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND AND OBJECTIVE As a key feature of schizophrenia, auditory verbal hallucination (AVH) is causing concern. Altered dynamic functional connectivity (dFC) patterns involving in auditory related regions were rarely reported in schizophrenia patients with AVH. The goal of this research was to find out the dFC abnormalities of auditory related regions in first-episode, drug-naïve schizophrenia patients with and without AVH using resting state functional magnetic resonance imaging (rs-fMRI). METHODS A total of 107 schizophrenia patients with AVH, 85 schizophrenia patients without AVH (NAVH) underwent rs-fMRI examinations, and 104 healthy controls (HC) were matched. Seed-based dFC of the primary auditory cortex (Heschl's gyrus, HES), auditory association cortex (AAC, including Brodmann's areas 22 and 42), and medial geniculate nucleus (MGN) was conducted to build a whole-brain dFC diagram, then inter group comparison and correlation analysis were performed. RESULTS In comparison to the NAVH and HC groups, the AVH group showed increased dFC from left ACC to the right middle temporal gyrus and right middle occipital gyrus, decreased dFC from left HES to the left superior occipital gyrus, left cuneus gyrus, left precuneus gyrus, decreased dFC from right HES to the posterior cingulate gyrus, and decreased dFC from left MGN to the bilateral calcarine gyrus, bilateral cuneus gyrus, bilateral lingual gyrus. The Auditory Hallucination Rating Scale (AHRS) was significantly positively correlated with the dFC values of cluster 1 (bilateral calcarine gyrus, cuneus gyrus, lingual gyrus, superior occipital gyrus, precuneus gyrus, and posterior cingulate gyrus) using left AAC seed, cluster 2 (right middle temporal gyrus and right middle occipital gyrus) using left AAC seed, cluster 1 (bilateral calcarine gyrus, cuneus gyrus, lingual gyrus, superior occipital gyrus, precuneus gyrus and posterior cingulate gyrus) using right AAC seed and cluster 2 (posterior cingulate gyrus) using right HES seed in the AVH group. In both AVH and NAVH groups, a significantly negative correlation is also found between the dFC values of cluster 2 (posterior cingulate gyrus) using the right HES seed and the PANSS negative sub-scores. CONCLUSIONS The present findings demonstrate that schizophrenia patients with AVH showed multiple abnormal dFC regions using auditory related cortex and nucleus as seeds, particularly involving the occipital lobe, default mode network (DMN), and middle temporal lobe, implying that the different dFC patterns of auditory related areas could provide a neurological mechanism of AVH in schizophrenia.
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Affiliation(s)
- Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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D'Souza NS, Nebel MB, Crocetti D, Robinson J, Wymbs N, Mostofsky SH, Venkataraman A. Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. Neuroimage 2021; 241:118388. [PMID: 34271159 PMCID: PMC8528511 DOI: 10.1016/j.neuroimage.2021.118388] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/05/2021] [Accepted: 07/10/2021] [Indexed: 11/27/2022] Open
Abstract
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.
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Affiliation(s)
- N S D'Souza
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
| | - M B Nebel
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - D Crocetti
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - J Robinson
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - N Wymbs
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - S H Mostofsky
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, USA
| | - A Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
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Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity. Neuroimage 2021; 243:118555. [PMID: 34492293 PMCID: PMC10018461 DOI: 10.1016/j.neuroimage.2021.118555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/24/2021] [Accepted: 09/03/2021] [Indexed: 02/04/2023] Open
Abstract
Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.
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46
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Bai L, JI G, Song Y, Sun J, Wei J, Xue F, Zhu L, Li R, Han Y, Zhang L, Yang J, Qiu B, Wu G, Zhang J, Hong J, Wang K, Zhu C. Dynamic brain connectome and high risk of mental problem in clinical nurses. Hum Brain Mapp 2021; 42:5300-5308. [PMID: 34331489 PMCID: PMC8519872 DOI: 10.1002/hbm.25617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022] Open
Abstract
With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.
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Affiliation(s)
- Ling Bai
- School of Nursing, Anhui Medical UniversityHefeiChina
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Gong‐Jun JI
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yongxia Song
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinmei Sun
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Junjie Wei
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Fang Xue
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Lu Zhu
- Institute of Literature in Chinese Medicine, Nanjing University of Chinese MedicineNanjingChina
| | - Rui Li
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Yanfang Han
- Department of PneumologyThe First Affiliated Hospital of Anhui Traditional Chinese Medicine UniversityHefeiChina
| | - Liu Zhang
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Jinying Yang
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Bensheng Qiu
- Laboratory Center for Information Science, University of Science and Technology of ChinaHefeiChina
- Hefei National Lab for Physical Sciences at the Microscale and the Centers for Biomedical Engineering, University of Science and Technology of ChinaHefeiChina
| | - Guo‐Rong Wu
- Key Laboratory of Cognition and Personality, Southwest UniversityChongqingChina
| | - Jing Zhang
- College of Nursing, Bengbu Medical UniversityBengbuChina
| | - Jingfang Hong
- School of Nursing, Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
| | - Chunyan Zhu
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical UniversityHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Anhui Provincial Institute of Translational Medicine, Anhui Medical UniversityHefeiChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
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47
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Kupis L, Goodman ZT, Kornfeld S, Hoang S, Romero C, Dirks B, Dehoney J, Chang C, Spreng RN, Nomi JS, Uddin LQ. Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan. Cereb Cortex 2021; 31:5263-5274. [PMID: 34145442 PMCID: PMC8491685 DOI: 10.1093/cercor/bhab156] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 11/14/2022] Open
Abstract
The neural mechanisms contributing to flexible cognition and behavior and how they change with development and aging are incompletely understood. The current study explored intrinsic brain dynamics across the lifespan using resting-state fMRI data (n = 601, 6-85 years) and examined the interactions between age and brain dynamics among three neurocognitive networks (midcingulo-insular network, M-CIN; medial frontoparietal network, M-FPN; and lateral frontoparietal network, L-FPN) in relation to behavioral measures of cognitive flexibility. Hierarchical multiple regression analysis revealed brain dynamics among a brain state characterized by co-activation of the L-FPN and M-FPN, and brain state transitions, moderated the relationship between quadratic effects of age and cognitive flexibility as measured by scores on the Delis-Kaplan Executive Function System (D-KEFS) test. Furthermore, simple slope analyses of significant interactions revealed children and older adults were more likely to exhibit brain dynamic patterns associated with poorer cognitive flexibility compared with younger adults. Our findings link changes in cognitive flexibility observed with age with the underlying brain dynamics supporting these changes. Preventative and intervention measures should prioritize targeting these networks with cognitive flexibility training to promote optimal outcomes across the lifespan.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Zachary T Goodman
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Salome Kornfeld
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Stephanie Hoang
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Joseph Dehoney
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124, USA
- Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA
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48
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Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder. Commun Biol 2021; 4:1073. [PMID: 34521980 PMCID: PMC8440519 DOI: 10.1038/s42003-021-02592-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/06/2021] [Indexed: 02/08/2023] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.
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49
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Corbera S, Wexler BE, Bell MD, Pearlson G, Mayer S, Pittman B, Belamkar V, Assaf M. Predictors of social functioning and quality of life in schizophrenia and autism spectrum disorder. Psychiatry Res 2021; 303:114087. [PMID: 34246005 PMCID: PMC8373814 DOI: 10.1016/j.psychres.2021.114087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/18/2021] [Accepted: 06/26/2021] [Indexed: 10/21/2022]
Abstract
Schizophrenia (SZ) and Autism Spectrum Disorder (ASD) show overlap in social cognitive and functioning impairments. Proposed predictors of social functioning (SF) and quality of life (QL) have been symptom severity, IQ and social cognition. Empathy has rarely been compared between ASD and SZ and its predictive power on functional outcomes is unclear. We investigated general, affective, and cognitive empathy in 46 SZ, 30 ASD and 51 healthy controls (HC) and examined their relationship to SF and QL in addition to IQ and symptoms. SZ and ASD shared deficits in general and cognitive empathy, and personal distress, but only SZ showed deficits in affective empathy. Both groups showed lower performance-based empathy scores and only ASD showed slower responses compared to HC. Negative symptoms predicted QL in both groups, the more negative symptoms the worse QL (ASD t=-3.22; SZ t= -3.43; p<0.01), and only in ASD, IQ predicted QL, the higher the IQ the higher QL (t = 2.1; p<0.05). In ASD only, negative symptoms predicted SF, the greater negative symptoms the worse SF (t=-3.45; p<0.01), and communication deficits predicted SF, the higher deficits, the higher SF (t = 2.9; p<0.01). Negative symptoms but not empathy were the shared predictors of functioning across ASD and SZ.
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Affiliation(s)
- Silvia Corbera
- Department of Psychological Science, Central Connecticut State University, New Britain, CT, United States; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States.
| | - Bruce E. Wexler
- Department of Psychiatry, Yale University, New Haven, CT,
USA
| | - Morris D. Bell
- Department of Psychiatry, Yale University, New Haven, CT,
USA,VA Connecticut Healthcare System, West Haven, CT, USA
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University, New Haven, CT,
USA,Olin Neuropsychiatry Research Center, Institute of Living,
Hartford Hospital, Hartford, CT, USA
| | - Sophy Mayer
- Olin Neuropsychiatry Research Center, Institute of Living,
Hartford Hospital, Hartford, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale University, New Haven, CT,
USA
| | - Vaishali Belamkar
- Department of Psychological Science, Central Connecticut
State University, New Britain, CT, USA
| | - Michal Assaf
- Department of Psychiatry, Yale University, New Haven, CT,
USA,Olin Neuropsychiatry Research Center, Institute of Living,
Hartford Hospital, Hartford, CT, USA
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Functional network topology of the right insula affects emotion dysregulation in hyperactive-impulsive attention-deficit/hyperactivity disorder. Sci Rep 2021; 11:15045. [PMID: 34294803 PMCID: PMC8298497 DOI: 10.1038/s41598-021-94426-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/07/2021] [Indexed: 02/07/2023] Open
Abstract
Emotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD.
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