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Zheng Y, Wang L, Dong H, Lin X, Zhao L, Ye S, Dong GH. Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111119. [PMID: 39159804 DOI: 10.1016/j.pnpbp.2024.111119] [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: 11/19/2023] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Internet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis. METHOD Resting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns. RESULTS IGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions. CONCLUSION The dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.
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Affiliation(s)
- Yanbin Zheng
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China; Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Centre for Cognition and Brain disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Lingxiao Wang
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Centre for Cognition and Brain disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Haohao Dong
- Centre for Cognition and Brain disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health, Beijing, China; National Clinical Research Center for Mental Disorders of Peking University Sixth Hospital, Chinese Academy of Medical Sciences Research Unit, Peking University, Beijing, China
| | - Lei Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shuer Ye
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Guang-Heng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China.
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Clancy KJ, Devignes Q, Ren B, Pollmann Y, Nielsen SR, Howell K, Kumar P, Belleau EL, Rosso IM. Spatiotemporal dynamics of hippocampal-cortical networks underlying the unique phenomenological properties of trauma-related intrusive memories. Mol Psychiatry 2024; 29:2161-2169. [PMID: 38454081 DOI: 10.1038/s41380-024-02486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Trauma-related intrusive memories (TR-IMs) possess unique phenomenological properties that contribute to adverse post-traumatic outcomes, positioning them as critical intervention targets. However, transdiagnostic treatments for TR-IMs are scarce, as their underlying mechanisms have been investigated separate from their unique phenomenological properties. Extant models of more general episodic memory highlight dynamic hippocampal-cortical interactions that vary along the anterior-posterior axis of the hippocampus (HPC) to support different cognitive-affective and sensory-perceptual features of memory. Extending this work into the unique properties of TR-IMs, we conducted a study of eighty-four trauma-exposed adults who completed daily ecological momentary assessments of TR-IM properties followed by resting-state functional magnetic resonance imaging (rs-fMRI). Spatiotemporal dynamics of anterior and posterior hippocampal (a/pHPC)-cortical networks were assessed using co-activation pattern analysis to investigate their associations with different properties of TR-IMs. Emotional intensity of TR-IMs was inversely associated with the frequency and persistence of an aHPC-default mode network co-activation pattern. Conversely, sensory features of TR-IMs were associated with more frequent co-activation of the HPC with sensory cortices and the ventral attention network, and the reliving of TR-IMs in the "here-and-now" was associated with more persistent co-activation of the pHPC and the visual cortex. Notably, no associations were found between HPC-cortical network dynamics and conventional symptom measures, including TR-IM frequency or retrospective recall, underscoring the utility of ecological assessments of memory properties in identifying their neural substrates. These findings provide novel insights into the neural correlates of the unique features of TR-IMs that are critical for the development of individualized, transdiagnostic treatments for this pervasive, difficult-to-treat symptom.
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Affiliation(s)
- Kevin J Clancy
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Quentin Devignes
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Sienna R Nielsen
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Emily L Belleau
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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3
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Shan X, Wang P, Yin Q, Li Y, Wang X, Feng Y, Xiao J, Li L, Huang X, Chen H, Duan X. Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02476-w. [PMID: 38861168 DOI: 10.1007/s00787-024-02476-w] [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] [Received: 02/06/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.
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Affiliation(s)
- Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Peng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Qing Yin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Youyi Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xiaotian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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Li L, Zheng Q, Xue Y, Bai M, Mu Y. Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02474-y. [PMID: 38814465 DOI: 10.1007/s00787-024-02474-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.
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Affiliation(s)
- Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Qingyu Zheng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, People's Republic of China
| | - Yang Xue
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Miaoshui Bai
- Department of Developmental and Behavioral Pediatrics, The First Hospital of Jilin University, Jilin University, Changchun, People's Republic of China
| | - Yueming Mu
- Department of Dermatology, The First Hospital of Jilin University, Jilin University, 71 Xinmin Street, Changchun, 130021, People's Republic of China.
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Nabizadeh F, Pirahesh K, Aarabi MH, Wennberg A, Pini L. Behavioral and dysexecutive variant of Alzheimer's disease: Insights from structural and molecular imaging studies. Heliyon 2024; 10:e29420. [PMID: 38638964 PMCID: PMC11024599 DOI: 10.1016/j.heliyon.2024.e29420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/02/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
Frontal variant Alzheimer's disease (AD) manifests with either behavioral or dysexecutive syndromes. Recent efforts to gain a deeper understanding of this phenotype have led to a re-conceptualization of frontal AD. Behavioral (bAD) and dysexecutive (dAD) phenotypes could be considered subtypes, as suggested by both clinical and neuroimaging studies. In this review, we focused on imaging studies to highlight specific brain patterns in these two uncommon clinical AD phenotypes. Although studies did not compare directly these two variants, a common epicenter located in the frontal cortex could be inferred. On the contrary, 18F-FDG-PET findings suggested differing metabolic patterns, with bAD showing specific involvement of frontal regions and dAD exhibiting widespread alterations. Structural MRI findings confirmed this pattern, suggesting that degeneration might involve neural circuits associated with behavioral control in bAD and attentional networks in dAD. Furthermore, molecular imaging has identified different neocortical tau distribution in bAD and dAD patients compared to typical AD patients, although the distribution is remarkably heterogeneous. In contrast, Aβ deposition patterns are less differentiated between these atypical variants and typical AD. Although preliminary, these findings underscore the complexity of AD frontal phenotypes and suggest that they represent distinct entities. Further research is essential to refine our understanding of the pathophysiological mechanisms in frontal AD.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Kasra Pirahesh
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | | | - Alexandra Wennberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lorenzo Pini
- Padova Neuroscience Center, University of Padova, Italy
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MacDowell CJ, Briones BA, Lenzi MJ, Gustison ML, Buschman TJ. Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype. Curr Biol 2024; 34:1333-1340.e6. [PMID: 38417445 PMCID: PMC10965364 DOI: 10.1016/j.cub.2024.02.004] [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: 04/03/2023] [Revised: 01/12/2024] [Accepted: 02/05/2024] [Indexed: 03/01/2024]
Abstract
Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.
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Affiliation(s)
- Camden J MacDowell
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, New Brunswick, NJ 08901, USA
| | - Brandy A Briones
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Psychology, Princeton University, Washington Road, Princeton, NJ 08540, USA; Department of Anesthesiology and Pain Medicine at University of Washington, Seattle, WA 98105, USA
| | - Michael J Lenzi
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA
| | - Morgan L Gustison
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA; Department of Psychology, Western University, London, ON N6A 3K7, Canada
| | - Timothy J Buschman
- Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08540 USA; Department of Psychology, Princeton University, Washington Road, Princeton, NJ 08540, USA.
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Shang J, Shen E, Yu Y, Jin A, Wang X, Xiang D. Relationship between abnormal intrinsic functional connectivity of subcortices and autism symptoms in high-functioning adults with autism spectrum disorder. Psychiatry Res Neuroimaging 2024; 337:111762. [PMID: 38043369 DOI: 10.1016/j.pscychresns.2023.111762] [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: 04/11/2023] [Revised: 10/02/2023] [Accepted: 11/03/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE This study explores subcortices and their intrinsic functional connectivity (iFC) in autism spectrum disorder (ASD) adults and investigates their relationship with clinical severity. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 74 ASD patients, and 63 gender and age-matched typically developing (TD) adults. Independent component analysis (ICA) was conducted to evaluate subcortical patterns of basal ganglia (BG) and thalamus. These two brain areas were treated as regions of interest to further calculate whole-brain FC. In addition, we employed multivariate machine learning to identify subcortices-based FC brain patterns and clinical scores to classify ASD adults from those TD subjects. RESULTS In ASD individuals, autism diagnostic observation schedule (ADOS) was negatively correlated with the BG network. Similarly, social responsiveness scale (SRS) was negatively correlated with the thalamus network. The BG-based iFC analysis revealed adults with ASD versus TD had lower FC, and its FC with the right medial temporal lobe (MTL), was positively correlated with SRS and ADOS separately. ASD could be predicted with a balanced accuracy of around 60.0 % using brain patterns and 84.7 % using clinical variables. CONCLUSION Our results revealed the abnormal subcortical iFC may be related to autism symptoms.
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Affiliation(s)
- Jing Shang
- Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Erwei Shen
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China
| | - Yang Yu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Aiying Jin
- Department of Nursing, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Xuemei Wang
- Department of Psychiatry, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.
| | - Dehui Xiang
- School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu Province, China.
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Yang Y, Ye C, Ma T. A deep connectome learning network using graph convolution for connectome-disease association study. Neural Netw 2023; 164:91-104. [PMID: 37148611 DOI: 10.1016/j.neunet.2023.04.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/01/2023] [Accepted: 04/16/2023] [Indexed: 05/08/2023]
Abstract
Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD). Here, we propose a multivariate distance-based connectome network (MDCN) that addresses the local specificity problem by efficient parcellation-wise learning, as well as associating population and parcellation dependencies to map individual differences. The approach incorporating an explainable method, parcellation-wise gradient and class activation map (p-GradCAM), is feasible for identifying individual patterns of interest and pinpointing connectome associations with diseases. We demonstrate the utility of our method on two largely aggregated multicenter public datasets by distinguishing ASD and ADHD from healthy controls and assessing their associations with underlying diseases. Extensive experiments have demonstrated the superiority of MDCN in classification and interpretation, where MDCN outperformed competitive state-of-the-art methods and achieved a high proportion of overlap with previous findings. As a CWAS-guided deep learning method, our proposed MDCN framework may narrow the bridge between deep learning and CWAS approaches, and provide new insights for connectome-wide association studies.
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Affiliation(s)
- Yanwu Yang
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China.
| | - Chenfei Ye
- Peng Cheng Laboratory, Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China.
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China; Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
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Zong X, Wu K, Li L, Zhang J, Ma S, Kang L, Zhang N, Lv L, Sang D, Weng S, Chen H, Zheng J, Hu M. Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy. Front Psychiatry 2023; 14:1093030. [PMID: 37009110 PMCID: PMC10050338 DOI: 10.3389/fpsyt.2023.1093030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/03/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundEvidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients’ clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks.MethodsForty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson’s correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients’ psychopathological symptoms.ResultsIn the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P.ConclusionThis study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients’ variation in the short-term treatment response of positive symptoms.
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Affiliation(s)
- Xiaofen Zong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kai Wu
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiangbo Zhang
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Simeng Ma
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Nan Zhang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Deen Sang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Shenhong Weng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- Shenhong Weng,
| | - Huafu Chen
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Huafu Chen,
| | - Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
- Junjie Zheng,
| | - Maolin Hu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Maolin Hu,
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10
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Zheng K, Li B, Lu H, Wang H, Liu J, Yan B, Friston KJ, Wu Y, Liu J, Zhang X, Liu M, Li L, Qin J, Chen B, Hu D, Li L. Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression. Eur Arch Psychiatry Clin Neurosci 2023; 273:169-181. [PMID: 35419632 DOI: 10.1007/s00406-022-01403-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 03/25/2022] [Indexed: 11/28/2022]
Abstract
Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.
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Affiliation(s)
- Kaizhong Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Huaning Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Jin Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Baoyu Yan
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Karl J Friston
- The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Yuxia Wu
- Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, 710032, China
| | - Jian Liu
- Network Center, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Xi Zhang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Mengwan Liu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Liang Li
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, 27 Tai-Ping Road, Beijing, 100850, China
| | - Jian Qin
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China
| | - Badong Chen
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
| | - Dewen Hu
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China.
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.
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11
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Sigar P, Uddin LQ, Roy D. Altered global modular organization of intrinsic functional connectivity in autism arises from atypical node-level processing. Autism Res 2023; 16:66-83. [PMID: 36333956 DOI: 10.1002/aur.2840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by restricted interests and repetitive behaviors as well as social-communication deficits. These traits are associated with atypicality of functional brain networks. Modular organization in the brain plays a crucial role in network stability and adaptability for neurodevelopment. Previous neuroimaging research demonstrates discrepancies in studies of functional brain modular organization in ASD. These discrepancies result from the examination of mixed age groups. Furthermore, recent findings suggest that while much attention has been given to deriving atlases and measuring the connections between nodes, within node information may also be crucial in determining altered modular organization in ASD compared with typical development (TD). However, altered modular organization originating from systematic nodal changes are yet to be explored in younger children with ASD. Here, we used graph-theoretical measures to fill this knowledge gap. To this end, we utilized multicenter resting-state fMRI data collected from 5 to 10-year-old children-34 ASD and 40 TD obtained from the Autism Brain Image Data Exchange (ABIDE) I and II. We demonstrate that alterations in topological roles and modular cohesiveness are the two key properties of brain regions anchored in default mode, sensorimotor, and salience networks, and primarily relate to social and sensory deficits in children with ASD. These results demonstrate that atypical global network organization in children with ASD arises from nodal role changes, and contribute to the growing body of literature suggesting that there is interesting information within nodes providing critical markers of functional brain networks in autistic children.
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Affiliation(s)
- Priyanka Sigar
- Cognitive Brain Dynamics Lab, National Brain Research Center, Manesar, India.,Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.,Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Center, Manesar, India.,School of AIDE, Centre for Brain Science and Applications, Karwar, India
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12
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Scheinost D, Chang J, Lacadie C, Brennan-Wydra E, Foster R, Boxberger A, Macari S, Vernetti A, Constable RT, Ment LR, Chawarska K. Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates. Sci Rep 2022; 12:16230. [PMID: 36171268 PMCID: PMC9517994 DOI: 10.1038/s41598-022-20617-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
Altered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. Here we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD = 1.5) who due to family history have high likelihood (HL) for developing autism (n = 12) and in controls (n = 41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M = 17.3 months, SD = 2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). Compared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -0.561, p = .003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n = 14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. The study identifies left lateralized anterior insula-amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Joseph Chang
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | | | - Rachel Foster
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | | | - Suzanne Macari
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Angelina Vernetti
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Laura R Ment
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Katarzyna Chawarska
- Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA.
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, 06510, USA.
- Yale Child Study Center, Yale School of Medicine, 300 George Street, Suite 900, New Haven, CT, 06510, USA.
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13
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Matsui T, Yamashita KI. Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases. Brain Connect 2022. [PMID: 35994384 DOI: 10.1089/brain.2022.0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
To date, numerous studies have documented various alterations in resting brain activity in Alzheimer's disease (AD) and other neuropsychiatric diseases. In particular, disease-related alterations of functional connectivity (FC) in the resting state networks (RSN) have been documented. Altered FC in RSN is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting-brain activity as a useful marker for detecting altered RSNs related to AD and other diseases. In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused the on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dFC, several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation. In this mini-review, we review recent studies exploring alterations of static and dynamic functional connectivity in AD and other neuropsychiatric diseases. We then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.
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Affiliation(s)
- Teppei Matsui
- Okayama University - Tsushima Campus, Tsushima-kita 1-1-1, Okayama, Japan, 700-8530;
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14
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Khan AF, Zhang F, Shou G, Yuan H, Ding L. Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data. Neuroimage 2022; 260:119460. [PMID: 35868615 PMCID: PMC9472706 DOI: 10.1016/j.neuroimage.2022.119460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 11/17/2022] Open
Abstract
Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.
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Affiliation(s)
- Ali Fahim Khan
- Stephenson School of Biomedical Engineering, University of Oklahoma, 110 W. Boyd St. DEH room 150, Norman, OK 73019, USA
| | - Fan Zhang
- Stephenson School of Biomedical Engineering, University of Oklahoma, 110 W. Boyd St. DEH room 150, Norman, OK 73019, USA
| | - Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, 110 W. Boyd St. DEH room 150, Norman, OK 73019, USA
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, 110 W. Boyd St. DEH room 150, Norman, OK 73019, USA; Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, 110 W. Boyd St. DEH room 150, Norman, OK 73019, USA; Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA.
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15
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Lorenzini L, van Wingen G, Cerliani L. Atypically high influence of subcortical activity on primary sensory regions in autism. Neuroimage Clin 2022; 32:102839. [PMID: 34624634 PMCID: PMC8503568 DOI: 10.1016/j.nicl.2021.102839] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 12/20/2022]
Abstract
The age-dependent decrease of subcortico-cortical connectivity is attenuated in ASD. Primary sensory regions remain less segregated from subcortical activity in ASD. This could underlie an excessive amount of sensory input relayed to the cortex.
Background Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals. Methods To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups. Results We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. Conclusion This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.
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Affiliation(s)
- Luigi Lorenzini
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Dept. Radiology and Nuclear Medicine, Amsterdam UMC, VU University, Amsterdam Neuroscience, De Boelelaan 1117, 1081HV Amsterdam, The Netherlands.
| | - Guido van Wingen
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WT, University of Amsterdam, The Netherlands
| | - Leonardo Cerliani
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WT, University of Amsterdam, The Netherlands; Netherlands Institute for Neuroscience, Social Brain Lab, Meibergdreef 47, 1105BA Amsterdam, The Netherlands
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16
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Matsui T, Pham TQ, Jimura K, Chikazoe J. On co-activation pattern analysis and non-stationarity of resting brain activity. Neuroimage 2022; 249:118904. [PMID: 35031473 DOI: 10.1016/j.neuroimage.2022.118904] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 11/30/2022] Open
Abstract
The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.
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Affiliation(s)
- Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan; JST-PRESTO, Japan Science and Technology Agency, Tokyo, Japan.
| | - Trung Quang Pham
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | | | - Junichi Chikazoe
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan; Araya Inc., Tokyo, Japan
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17
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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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18
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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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19
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Tu Y, Cao J, Guler S, Chai-Zhang T, Camprodon JA, Vangel M, Gollub RL, Dougherty DD, Kong J. Perturbing fMRI brain dynamics using transcranial direct current stimulation. Neuroimage 2021; 237:118100. [PMID: 33933595 PMCID: PMC8291729 DOI: 10.1016/j.neuroimage.2021.118100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/24/2021] [Accepted: 04/18/2021] [Indexed: 12/29/2022] Open
Abstract
The dynamic nature of resting-state functional magnetic resonance imaging (fMRI) brain activity and connectivity has drawn great interest in the past decade. Specific temporal properties of fMRI brain dynamics, including metrics such as occurrence rate and transitions, have been associated with cognition and behaviors, indicating the existence of mechanism distruption in neuropsychiatric disorders. The development of new methods to manipulate fMRI brain dynamics will advance our understanding of these pathophysiological mechanisms from native observation to experimental mechanistic manipulation. In the present study, we applied repeated transcranial direct current stimulation (tDCS) to the right dorsolateral prefrontal cortex (rDLPFC) and the left orbitofrontal cortex (lOFC), during multiple simultaneous tDCS-fMRI sessions from 81 healthy participants to assess the modulatory effects of stimulating target brain regions on fMRI brain dynamics. Using the rDLPFC and the lOFC as seeds, respectively, we first identified two reoccurring co-activation patterns (CAPs) and calculated their temporal properties (e.g., occurrence rate and transitions) before administering tDCS. The spatial maps of CAPs were associated with different cognitive and disease domains using meta-analytical decoding analysis. We then investigated how active tDCS compared to sham tDCS in the modulation of the occurrence rates of these different CAPs and perturbations of transitions between CAPs. We found that by enhancing neuronal excitability of the rDLPFC and the lOFC, the occurrence rate of one CAP was significantly decreased while that of another CAP was significantly increased during the first 6 min of stimulation. Furthermore, these tDCS-associated changes persisted over subsequent testing sessions (both during and before/after tDCS) across three consecutive days. Active tDCS could perturb transitions between CAPs and a non-CAP state (when the rDLPFC and the lOFC were not activated), but not the transitions within CAPs. These results demonstrate the feasibility of modulating fMRI brain dynamics, and open new possibilities for discovering stimulation targets and dynamic connectivity patterns that can ensure the propagation of tDCS-induced neuronal excitability, which may facilitate the development of new treatments for disorders with altered dynamics.
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Affiliation(s)
- Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Jin Cao
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States
| | - Seyhmus Guler
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States
| | - Thalia Chai-Zhang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States
| | - Joan A Camprodon
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mark Vangel
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Randy L Gollub
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, United States; Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.
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20
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Snyder W, Uddin LQ, Nomi JS. Dynamic functional connectivity profile of the salience network across the life span. Hum Brain Mapp 2021; 42:4740-4749. [PMID: 34312945 PMCID: PMC8410581 DOI: 10.1002/hbm.25581] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 06/07/2021] [Accepted: 06/23/2021] [Indexed: 12/16/2022] Open
Abstract
The insular cortex and anterior cingulate cortex together comprise the salience or midcingulo-insular network, involved in detecting salient events and initiating control signals to mediate brain network dynamics. The extent to which functional coupling between the salience network and the rest of the brain undergoes changes due to development and aging is at present largely unexplored. Here, we examine dynamic functional connectivity (dFC) of the salience network in a large life span sample (n = 601; 6-85 years old). A sliding-window analysis and k-means clustering revealed five states of dFC formed with the salience network, characterized by either widespread asynchrony or different patterns of synchrony between the salience network and other brain regions. We determined the frequency, dwell time, total transitions, and specific state-to-state transitions for each state and subject, regressing the metrics with subjects' age to identify life span trends. A dynamic state characterized by low connectivity between the salience network and the rest of the brain had a strong positive quadratic relationship between age and both frequency and dwell time. Additional frequency, dwell time, total transitions, and state-to-state transition trends were observed with other salience network states. Our results highlight the metastable dynamics of the salience network and its role in the maturation of brain regions critical for cognition.
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Affiliation(s)
- William Snyder
- Program in Neuroscience, Bucknell University, Lewisburg, Pennsylvania
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida
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21
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Kupis L, Goodman ZT, Kircher L, Romero C, Dirks B, Chang C, Nomi JS, Uddin LQ. Altered patterns of brain dynamics linked with body mass index in youth with autism. Autism Res 2021; 14:873-886. [PMID: 33616282 DOI: 10.1002/aur.2488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 02/10/2021] [Indexed: 12/18/2022]
Abstract
Children with autism spectrum disorder (ASD) have higher rates of overweight and obesity (OWOB) compared with typically developing (TD) children. Brain functional connectivity differences have been shown in both ASD and OWOB. However, only one study to date has examined ASD and OWOB concurrently, so little is known regarding the neural mechanisms associated with the higher prevalence of OWOB and its behavioral impacts in ASD. We investigated co-activation patterns (CAPs) of brain regions identified by independent component analysis in 129 children and adolescents between 6 and 18 years of age (n = 68 ASD). We examined the interaction between body mass index (BMI) and diagnosis in predicting dynamic brain metrics (dwell time, DT; frequency of occurrence, and transitions between states) as well as dimensional brain-behavior relationships. The relationship between BMI and brain dynamics was moderated by diagnosis (ASD, TD), particularly among the frequency of CAP 4, characterized by co-activation of lateral frontoparietal, temporal, and frontal networks. This pattern was negatively associated with parent-reported inhibition skills. Children with ASD had shorter CAP 1, characterized by co-activation of the subcortical, temporal, sensorimotor, and frontal networks, and CAP 4 DTs compared with TD children. CAP 1 DT was negatively associated with cognitive flexibility, inhibition, social functioning, and BMI. Cognitive flexibility moderated the relationship between BMI and brain dynamics in the visual network. Our findings provide novel evidence of neural mechanisms associated with OWOB in children with ASD. Further, poorer cognitive flexibility may result in increased vulnerability for children with ASD and co-occurring OWOB. LAY SUMMARY: Obesity is a societal epidemic and is common in autism, however, little is known about the neural mechanisms associated with the higher rates of obesity in autism. Here, we find unique patterns of brain dynamics associated with obesity in autism that were not observed in typically developing children. Further, the relationship between body mass index and brain dynamics depended on cognitive flexibility. These findings suggest that individuals with autism may be more vulnerable to the effects of obesity on brain function. Autism Res 2021, 14: 873-886. © 2021 International Society for Autism Research, Wiley Periodicals LLC.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Zachary T Goodman
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Leigha Kircher
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida, USA.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida, USA
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22
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Kupis L, Romero C, Dirks B, Hoang S, Parladé MV, Beaumont AL, Cardona SM, Alessandri M, Chang C, Nomi JS, Uddin LQ. Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. Neuroimage Clin 2020; 28:102396. [PMID: 32891039 PMCID: PMC7479441 DOI: 10.1016/j.nicl.2020.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/26/2020] [Accepted: 08/19/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Brain dynamics underlie flexible cognition and behavior, yet little is known regarding this relationship in autism spectrum disorder (ASD). We examined time-varying changes in functional co-activation patterns (CAPs) across rest and task-evoked brain states to characterize differences between children with ASD and typically developing (TD) children and identify relationships with severity of social behaviors and restricted and repetitive behaviors. METHOD 17 children with ASD and 27 TD children ages 7-12 completed a resting-state fMRI scan and four runs of a non-cued attention switching task. Metrics indexing brain dynamics were generated from dynamic CAPs computed across three major large-scale brain networks: midcingulo-insular (M-CIN), medial frontoparietal (M-FPN), and lateral frontoparietal (L-FPN). RESULTS Five time-varying CAPs representing dynamic co-activations among network nodes were identified across rest and task fMRI datasets. Significant Diagnosis × Condition interactions were observed for the dwell time of CAP 3, representing co-activation between nodes of the M-CIN and L-FPN, and the frequency of CAP 1, representing co-activation between nodes of the L-FPN. A significant brain-behavior association between dwell time of CAP 5, representing co-activation between nodes of the M-FPN, and social abilities was also observed across both groups of children. CONCLUSION Analysis of brain co-activation patterns reveals altered dynamics among three core networks in children with ASD, particularly evident during later stages of an attention task. Dimensional analyses demonstrating relationships between M-FPN dwell time and social abilities suggest that metrics of brain dynamics may index individual differences in social cognition and behavior.
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Affiliation(s)
- Lauren Kupis
- Department of Psychology, University of Miami, Coral Gables, FL, USA.
| | - Celia Romero
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Bryce Dirks
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Stephanie Hoang
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Meaghan V Parladé
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Amy L Beaumont
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Sandra M Cardona
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | | | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
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