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Yang YL, Liu YX, Wei J, Guo QL, Hao ZP, Xue JY, Liu JY, Guo H, Li Y. Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis. J Neurophysiol 2024; 132:744-756. [PMID: 39015075 DOI: 10.1152/jn.00027.2024] [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: 01/16/2024] [Revised: 06/10/2024] [Accepted: 07/11/2024] [Indexed: 07/18/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called leading eigenvector dynamics analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, intergroup differences in brain dynamic activity indicators are examined, and the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual region activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state and more active in limbic region activity state and visual region activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.NEW & NOTEWORTHY Alzheimer's disease (AD) is a neurodegenerative disease, but few studies have explored the characteristics of abnormal dynamic brain activity in AD patients. Here, our report reveals the abnormal dynamic activity of the patients' resting-state network, providing new insights into the dynamic analysis of brain networks and helping to gain a deeper understanding of the abnormal spatial dynamic patterns in AD patients.
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Affiliation(s)
- Yan-Li Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yu-Xuan Liu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Jing Wei
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Qi-Li Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Zhi-Peng Hao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Jia-Yue Xue
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Jin-Yi Liu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yao Li
- School of Software, Taiyuan University of Technology, Taiyuan, China
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Ellis CA, Miller RL, Calhoun VD. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Front Psychiatry 2024; 15:1165424. [PMID: 38495909 PMCID: PMC10941842 DOI: 10.3389/fpsyt.2024.1165424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 01/30/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features. Methods We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity. Results Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states. Conclusion We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
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Affiliation(s)
- Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Atlanta, GA, United States
- Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
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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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Yang W, Niu H, Jin Y, Cui J, Li M, Qiu Y, Lu D, Li G, Li J. Altered dynamic functional connectivity of the thalamus subregions in patients with schizophrenia. J Psychiatr Res 2023; 167:86-92. [PMID: 37862908 DOI: 10.1016/j.jpsychires.2023.09.021] [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: 03/12/2023] [Revised: 06/05/2023] [Accepted: 09/27/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Previous neuroimaging studies indicated that patients with schizophrenia showed impaired thalamus and thalamo-cortical circuits. However, the dynamic functional connectivity (dFC) patterns of the thalamus remain unclear. In this study, we explored the dFC of the thalamus in SZ patients and whether clinical features are correlated with altered dFC. METHODS Forty-three patients with schizophrenia and 31 healthy controls underwent 3.0 T rs-fMRI. Based on the human Brainnetome atlas, the thalamus is divided into 8 subregions. Subsequently, we performed flexible least squares method to calculate the dFC of each thalamus subregions. RESULTS Compared with healthy controls, patients with schizophrenia exhibited increased dFC between the thalamus and cerebellar, visual-related cortex, sensorimotor-related cortex, and frontal lobe. In addition, we found that the dFC of the thalamus and the right fusiform gyrus was negatively associated with age of onset. CONCLUSIONS Our findings demonstrated that the dFC of specific thalamus sub-regions is altered in patients with schizophrenia. Our results further suggested the dysconnectivity of thalamus plays an important role in the pathophysiology of schizophrenia.
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Affiliation(s)
- Weiliang Yang
- Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Huiming Niu
- The Third People's Hospital of Tianshui, Tianshui, 741000, China
| | - Yiqiong Jin
- The Third People's Hospital of Tianshui, Tianshui, 741000, China
| | - Jie Cui
- The Third People's Hospital of Tianshui, Tianshui, 741000, China
| | - Meijuan Li
- Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Yuying Qiu
- Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China
| | - Duihong Lu
- The Third People's Hospital of Tianshui, Tianshui, 741000, China
| | - Gang Li
- The Third People's Hospital of Tianshui, Tianshui, 741000, China
| | - Jie Li
- Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, 300222, China.
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Xie Y, Guan M, Wang Z, Ma Z, Wang H, Fang P. Alterations in brain connectivity patterns in schizophrenia patients with auditory verbal hallucinations during low frequency repetitive transcranial magnetic stimulation. Psychiatry Res 2023; 328:115457. [PMID: 37716322 DOI: 10.1016/j.psychres.2023.115457] [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: 04/15/2022] [Revised: 08/28/2023] [Accepted: 08/31/2023] [Indexed: 09/18/2023]
Abstract
OBJECTIVE Auditory verbal hallucinations (AVH) are a characteristic symptom of schizophrenia. Although low-frequency repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to alleviate the severity of AVH, its exact neurophysiological mechanisms remain unclear. This study aimed to elucidate the alterations in brain connectivity patterns in schizophrenia patients with AVH after low frequency rTMS. Furthermore, the relationship between these alterations and clinical outcomes was examined, thereby identifying potential biomarkers for rTMS treatment efficacy. METHODS A total of 30 schizophrenia patients with AVH and 33 healthy controls were recruited. The patients received 1 Hz rTMS applied to the left temporoparietal junction region over 15 days. Resting-state functional magnetic resonance imaging scans were conducted for all participants. Subsequently, degree centrality (DC) and seed-based functional connectivity (FC) analyses were employed to identify specific alterations in brain connectivity patterns after rTMS treatment. RESULTS At baseline, patients exhibited divergent DC patterns in the frontal, occipital, and limbic lobes compared to healthy controls. In addition, prior to treatment, patients demonstrated altered FC from the superior frontal gyrus seeds that linked to the frontal, temporal, and somatosensory regions. Following rTMS treatment, these abnormalities were notably reversed, correlating with improved clinical outcomes. CONCLUSIONS These findings demonstrate that schizophrenia patients with AVH exhibited atypical interactions within the frontal and temporal lobes. These alterations might be crucial biomarkers for predicting the efficacy of low frequency rTMS.
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Affiliation(s)
- Yuanjun Xie
- Military Medical Psychology School , Fourth Military Medical University, Xi'an, China; Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Muzhen Guan
- Department of Mental Health, Xi'an Medical College, Xi'an, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhujing Ma
- Military Medical Psychology School , Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Peng Fang
- Military Medical Psychology School , Fourth Military Medical University, Xi'an, China; Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China.
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Ellis CA, Miller RL, Calhoun VD. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083012 DOI: 10.1109/embc40787.2023.10340837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Identifying subtypes of neuropsychiatric disorders based on characteristics of their brain activity has tremendous potential to contribute to a better understanding of those disorders and to the development of new diagnostic and personalized treatment approaches. Many studies focused on neuropsychiatric disorders examine the interaction of brain networks over time using dynamic functional network connectivity (dFNC) extracted from resting-state functional magnetic resonance imaging data. Some of these studies involve the use of either deep learning classifiers or traditional clustering approaches, but usually not both. In this study, we present a novel approach for subtyping individuals with neuropsychiatric disorders within the context of schizophrenia (SZ). We trained an explainable deep learning classifier to differentiate between dFNC data from individuals with SZ and controls, obtaining a test accuracy of 79%. We next used cross-validation to obtain robust average explanations for SZ training participants across folds, identifying 5 SZ subtypes that each differed from controls in a distinct manner and that had different degrees of symptom severity. These subtypes specifically differed from one another in their interactions between the visual network and the subcortical, sensorimotor, and auditory networks and between the cerebellar network and the cognitive control and subcortical networks. Additionally, we uncovered statistically significant differences in negative symptom scores between the subtypes. It is our hope that the proposed novel subtyping approach will contribute to the improved understanding and characterization of SZ and other neuropsychiatric disorders.
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Chen J, Wei Y, Xue K, Han S, Wang C, Wen B, Cheng J. The interaction between first-episode drug-naïve schizophrenia and age based on gray matter volume and its molecular analysis: a multimodal magnetic resonance imaging study. Psychopharmacology (Berl) 2023; 240:813-826. [PMID: 36719459 DOI: 10.1007/s00213-023-06323-9] [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: 11/03/2022] [Accepted: 01/19/2023] [Indexed: 02/01/2023]
Abstract
OBJECTIVES Schizophrenia is a neurodevelopmental disorder characterized by progressive and widespread gray matter (GM) atrophy. Studies have shown that normal brain development has an impact on schizophrenia-induced GM alterations. However, the neuropathology and underlying molecular mechanisms of interaction between age and schizophrenia are unclear. METHODS This study enrolled 66/84 first-episode drug-naïve patients with early-onset/adult-onset schizophrenia ((EOS)/(AOS)) and matched normal controls (NC) (46 adolescents/73 adults), undergoing T1-weighted high-resolution magnetic resonance imaging. Gray matter volume (GMV) in four groups was detected using 2-way analyses of variance with diagnosis and age as factors. Then, factors-related volume maps and neurotransmitter maps were spatially correlated using JuSpace to determine the relationship to molecular structure. RESULTS Compared to AOS, EOS and adult NC had larger GMV in right middle frontal gyrus. Compared to adolescent NC, EOS and adult NC had smaller GMV in right lingual gyrus, right fusiform gyrus, and right cerebellum_6. Disease-induced GMV reductions were mainly distributed in frontal, parietal, thalamus, visual, motor cortex, and medial temporal lobe structures. Age-induced GMV alterations were mainly distributed in visual and motor cortex. The changed GMV induced by schizophrenia, age, and their interaction was related to dopaminergic and serotonergic receptors. Age is also related to glutamate receptors, and schizophrenia is also associated with GABAaergic and noradrenergic receptors. CONCLUSIONS Our results revealed the multimodal neural mechanism of interaction between disease and age. We emphasized age-related GM abnormalities of ventral stream of visual perceptual pathways and high-level cognitive brain in EOS, which may be affected by imbalance of excitatory and inhibitory neurotransmitters.
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Affiliation(s)
- Jingli Chen
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Kangkang Xue
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, Two Seven District, The First Affiliated Hospital of Zhengzhou University, 1St Construction of E Rd, Zhengzhou, 450052, China.
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
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Sendi MSE, Zendehrouh E, Ellis CA, Fu Z, Chen J, Miller RL, Mormino EC, Salat DH, Calhoun VD. The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer's disease. Neuroimage Clin 2023; 37:103363. [PMID: 36871405 PMCID: PMC9999198 DOI: 10.1016/j.nicl.2023.103363] [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: 03/27/2021] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/03/2023]
Abstract
Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer's disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N = 886) between 42 and 95 years of age (mean = 70 years). We separated individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the proportion of time each subject spent in each state, called occupancy rate or OCR and frequency of visits. We compared both sFNC and dFNC features across individuals with different genetic risks and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. We also found that AD genetic risk affects whole-brain sFNC and dFNC in women but not men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.
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Affiliation(s)
- Mohammad S E Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA; Current affiliation: McLean Hospital and Harvard Medical School, Boston, MA, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA.
| | - Elaheh Zendehrouh
- Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA
| | - Charles A Ellis
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA; Georgia State University, Atlanta, GA, USA
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA; Georgia State University, Atlanta, GA, USA
| | - Robyn L Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA; Georgia State University, Atlanta, GA, USA
| | | | - David H Salat
- Harvard Medical School, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, USA; Georgia State University, Atlanta, GA, USA.
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Ellis CA, Miller RL, Calhoun VD. Explainable Fuzzy Clustering Framework Reveals Divergent Default Mode Network Connectivity Dynamics in Schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.528329. [PMID: 36824777 PMCID: PMC9949005 DOI: 10.1101/2023.02.13.528329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics. We apply our framework for schizophrenia (SZ) default mode network analysis, identifying 5 states and characterizing those states with a new explainability approach. While also showing that features typically used in hard clustering can be extracted in our framework, we present a variety of unique features to quantify state dynamics and identify effects of SZ upon network dynamics. We further uncover relationships between symptom severity and interactions of the precuneus with the anterior and posterior cingulate cortex. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
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Affiliation(s)
- Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
- Department of Computer Science, Georgia State University, Atlanta, Georgia, United States
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Ellis CA, Miller RL, Calhoun VD. Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches. INFORMATICS IN MEDICINE UNLOCKED 2023; 37:101176. [PMID: 37035832 PMCID: PMC10078989 DOI: 10.1016/j.imu.2023.101176] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of transparency are (1) confidence estimation and (2) explainability. Confidence estimation approaches indicate confidence in individual predictions. Explainability methods give insight into the importance of features to model predictions. In this study, we integrate confidence estimation and explainability approaches for the first time. We demonstrate their viability for schizophrenia diagnosis using resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data. We compare two confidence estimation approaches: Monte Carlo dropout (MCD) and MC batch normalization (MCBN). We combine them with two gradient-based explainability approaches, saliency and layer-wise relevance propagation (LRP), and examine their effects upon explanations. We find that MCD often adversely affects model gradients, making it ill-suited for integration with gradient-based explainability methods. In contrast, MCBN does not affect model gradients. Additionally, we find many participant-level differences between regular explanations and the distributions of explanations for combined explainability and confidence estimation approaches. This suggests that a similar confidence estimation approach used in a clinical context with explanations only output for the regular model would likely not yield adequate explanations. We hope that our findings will provide a starting point for the integration of the two fields, provide useful guidance for future studies, and accelerate the development of transparent neuroimaging clinical decision support systems.
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Affiliation(s)
- Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, United States
- Department of Computer Science, Georgia State University, 25 Park PlaceSuite 700, Atlanta, GA, 30303, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, 30332, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, United States
- Department of Computer Science, Georgia State University, 25 Park PlaceSuite 700, Atlanta, GA, 30303, United States
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Alaçam D, Miller R, Agcaoglu O, Preda A, Ford J, Calhoun V. A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia. Front Neurosci 2023; 17:1078995. [PMID: 37179560 PMCID: PMC10174238 DOI: 10.3389/fnins.2023.1078995] [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: 10/24/2022] [Accepted: 04/03/2023] [Indexed: 05/15/2023] Open
Abstract
Introduction Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA). Methods Motivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC). Results Our proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains. Conclusions The results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments.
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Affiliation(s)
- Deniz Alaçam
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
- Department of Mathematics, Bursa Uludag University, Bursa, Türkiye
- *Correspondence: Deniz Alaçam
| | - Robyn Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Oktay Agcaoglu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Judith Ford
- San Francisco VA Medical Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
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Fu Y, Niu M, Gao Y, Dong S, Huang Y, Zhang Z, Zhuo C. Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia. J Neural Eng 2022; 19. [PMID: 36579785 DOI: 10.1088/1741-2552/acabe7] [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: 07/15/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.
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Affiliation(s)
- Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Meng Niu
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, People's Republic of China
| | - Yuanhang Gao
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Shunjie Dong
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Yanyan Huang
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- School of Physics, Hangzhou Normal University, Hangzhou, People's Republic of China.,Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China.,Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Hangzhou, People's Republic of China
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Sendi MSE, Salat DH, Miller RL, Calhoun VD. Two-step clustering-based pipeline for big dynamic functional network connectivity data. Front Neurosci 2022; 16:895637. [PMID: 35958983 PMCID: PMC9358255 DOI: 10.3389/fnins.2022.895637] [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: 03/14/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power. Methods In the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP). Results We found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP. Conclusion We developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- *Correspondence: Mohammad S. E. Sendi,
| | - David H. Salat
- Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Vince D. Calhoun,
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Ellis CA, Sendi MSE, Miller RL, Calhoun VD. An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4449-4452. [PMID: 36086408 DOI: 10.1109/embc48229.2022.9871548] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dynamic functional network connectivity (dFNC) data extracted from resting state functional magnetic resonance imaging (rs-fMRI) recordings has played a significant role in characterizing the role that brain network interactions play in a variety of brain disorders and cognitive functions. dFNC analyses frequently use clustering methods to identify states of network activity. However, it is possible that these states are dominated by a few highly influential networks or nodes, which could obscure condition-related insights that might be gained from networks or nodes less influential to the clustering. In this study, we propose a novel feature learning-based approach that could contribute to the identification of condition-related activity in formerly less influential networks or nodes. We demonstrate the viability of our approach within the context of schizophrenia (SZ), applying our approach to a dataset consisting of 151 participants with SZ and 160 controls (HCs). We find that the removal of some connectivity pairs significantly affects the underlying states and magnifies the differences between participants with SZ and HCs in each state. Given our findings, we hope that our approach will contribute to the characterization and improved diagnosis of a variety of neurological conditions and functions. Clinical Relevance- Our approach could contribute to the characterization and diagnosis of many neurological conditions.
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Yamamoto M, Bagarinao E, Shimamoto M, Iidaka T, Ozaki N. Involvement of cerebellar and subcortical connector hubs in schizophrenia. NEUROIMAGE: CLINICAL 2022; 35:103140. [PMID: 36002971 PMCID: PMC9421528 DOI: 10.1016/j.nicl.2022.103140] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/29/2022] [Accepted: 07/30/2022] [Indexed: 11/14/2022] Open
Abstract
Hubs with altered connectivity to multiple networks were identified in patients. Identified hubs were located in the cerebellum, midbrain, thalamus, and insula. In controls, these hubs were strongly connected with the basal ganglia network. Hubs’ connections to large-scale networks were associated with clinical data. Their connections were also highly predictive of patients from controls.
Background Schizophrenia is considered a brain connectivity disorder in which functional integration within the brain fails. Central to the brain’s integrative function are connector hubs, brain regions characterized by strong connections with multiple networks. Given their critical role in functional integration, we hypothesized that connector hubs, including those located in the cerebellum and subcortical regions, are severely impaired in patients with schizophrenia. Methods We identified brain voxels with significant connectivity alterations in patients with schizophrenia (n = 76; men = 43) compared to healthy controls (n = 80; men = 43) across multiple large-scale resting state networks (RSNs) using a network metric called functional connectivity overlap ratio (FCOR). From these voxels, candidate connector hubs were identified and verified using seed-based connectivity analysis. Results We found that most networks exhibited connectivity alterations in the patient group. Specifically, connectivity with the basal ganglia and high visual networks was severely affected over widespread brain areas in patients, affecting subcortical and cerebellar regions and the regions involved in visual and sensorimotor processing. Furthermore, we identified critical connector hubs in the cerebellum, midbrain, thalamus, insula, and calcarine with connectivity to multiple RSNs affected in the patients. FCOR values of these regions were also associated with clinical data and could classify patient and control groups with > 80 % accuracy. Conclusions These findings highlight the critical role of connector hubs, particularly those in the cerebellum and subcortical regions, in the pathophysiology of schizophrenia and the potential role of FCOR as a clinical biomarker for the disorder.
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Sendi MSE, Zendehrouh E, Ellis CA, Liang Z, Fu Z, Mathalon DH, Ford JM, Preda A, van Erp TGM, Miller RL, Pearlson GD, Turner JA, Calhoun VD. Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity. Front Neural Circuits 2021; 15:649417. [PMID: 33815070 PMCID: PMC8013735 DOI: 10.3389/fncir.2021.649417] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 02/24/2021] [Indexed: 01/10/2023] Open
Abstract
Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Zhijia Liang
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Daniel H. Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Judith M. Ford
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Theo G. M. van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States
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