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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024:S0166-2236(24)00091-2. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [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: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
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Xing Y, van Erp TG, Pearlson GD, Kochunov P, Calhoun VD, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. iScience 2024; 27:109319. [PMID: 38482500 PMCID: PMC10933544 DOI: 10.1016/j.isci.2024.109319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/17/2023] [Accepted: 02/19/2024] [Indexed: 04/26/2024] Open
Abstract
The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.
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Affiliation(s)
- Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA 92617, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and of Neurobiology, Yale University, New Haven, CT 06519, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center and Department of Psychiatry, University of Maryland, School of Medicine, Baltimore, MD 21201, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
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van der Vaart AD, Ma Y, Chiappelli J, Bruce H, Kvarta MD, Warner A, Du X, Adhikari BM, Sampath H, Kochunov P, Hong LE. Revisiting delusion subtypes in schizophrenia based on their underlying structures. J Psychiatr Res 2024; 171:75-83. [PMID: 38246028 PMCID: PMC10923062 DOI: 10.1016/j.jpsychires.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024]
Abstract
A clear understanding of the pathophysiology of schizophrenia and related spectrum disorders has been limited by clinical heterogeneity. We investigated whether relative severity and predominance of one or more delusion subtypes might yield clinically differentiable patient profiles. Patients (N = 286) with schizophrenia spectrum disorders (SSD) completed the 21-item Peters et al. Delusions Inventory (PDI-21). We performed factor analysis followed by k-means clustering to identify delusion factors and patient subtypes. Patients were further assessed via the Brief Psychiatric Rating Scale, Brief Negative Symptom Scale, Digit Symbol and Digit Substitution tasks, use of cannabis and tobacco, and stressful life events. The overall patient sample clustered into subtypes corresponding to Low-Delusion, Grandiose-Predominant, Paranoid-Predominant, and Pan-Delusion patients. Paranoid-Predominant and Pan-Delusion patients showed significantly higher burden of positive symptoms, while Low-Delusion patients showed the highest burden of negative symptoms. The Paranoia delusion factor score showed a positive association with Digit Symbol and Digit Substitution tasks in the overall sample, and the Paranoid-Predominant subtype exhibited the best performance on both tasks. Grandiose-Predominant patients showed significantly higher tobacco smoking severity than other subtypes, while Paranoid-Predominant patients were significantly more likely to have a lifetime diagnosis of Cannabis Use Disorder. We suggest that delusion self-report inventories such as the PDI-21 may be of utility in identifying sub-syndromes in SSD. From the current study, a Paranoid-Predominant form may be most distinctive, with features including less cognitive impairment and a stronger association with cannabis use.
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Affiliation(s)
- Andrew D van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alia Warner
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiaoming Du
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bhim M Adhikari
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hemalatha Sampath
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Peter Kochunov
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - L Elliot Hong
- Louis A Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
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Zhou J, Zhou J, Feng Y, Feng L, Xiao L, Chen X, Feng Z, Yang J, Wang G. The novel subtype of major depressive disorder characterized by somatic symptoms is associated with poor treatment efficacy and prognosis: A data-driven cluster analysis of a prospective cohort in China. J Affect Disord 2024; 347:576-583. [PMID: 38065479 DOI: 10.1016/j.jad.2023.12.005] [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: 08/09/2023] [Revised: 11/15/2023] [Accepted: 12/02/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND There is not yet a valid and evidence-based system to classify patients with MDD into more homogeneous subtypes based on their clinical features. This study aims to identify symptom-based subtypes of MDD and investigate whether the treatment outcomes of those subtypes would be different. METHOD The cohort was established at 12 densely populated cities of China. A total of 1487 patients were enrolled. All participants were 18-65 years old and diagnosed with MDD. Participants were followed up at baseline, weeks 4, 8, and 12, and months 4 and 6. K-means algorithm was used to cluster patients with MDD according to clinical symptoms. The network analysis was adopted to characterize and compare the symptom patterns in the clusters. We also examined the associations between the clusters and the clinical outcomes. RESULTS The optimal number of the clusters was determined to be 2. Each cluster's maximum Jaccard Co-efficient was calculated to be >0.5 (cluster1 = 0.53, cluster 2 = 0.67). The symptom "depressed mood" and some other affective symptoms were the most prominent in cluster 1. Somatic symptoms, such as weight loss and general somatic symptoms, had the greatest expected influence in cluster 2. Compared with the response rates of the patients in the "somatic cluster", those of the patients in the "affective cluster" were significantly higher (P < 0.05). CONCLUSIONS Patients with MDD might be classified into two symptom-based subtypes featured with affective symptoms or somatic symptoms. The treatment efficacy and prognosis of the subtype featured with somatic symptoms may be worse.
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Affiliation(s)
- Jingjing Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jia Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yuan Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lei Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Le Xiao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xu Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zizhao Feng
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jian Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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van der Horn HJ, Ling JM, Wick TV, Dodd AB, Robertson-Benta CR, McQuaid JR, Zotev V, Vakhtin AA, Ryman SG, Cabral J, Phillips JP, Campbell RA, Sapien RE, Mayer AR. Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury. Neuroimage 2024; 285:120470. [PMID: 38016527 PMCID: PMC10815936 DOI: 10.1016/j.neuroimage.2023.120470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023] Open
Abstract
Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.
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Affiliation(s)
| | - Josef M Ling
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Tracey V Wick
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | - Andrew B Dodd
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Vadim Zotev
- The Mind Research Network/LBERI, Albuquerque, NM 87106
| | | | | | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Braga, Portugal
| | | | - Richard A Campbell
- Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131
| | - Robert E Sapien
- Department of Emergency Medicine, University of New Mexico, Albuquerque, NM 87131
| | - Andrew R Mayer
- The Mind Research Network/LBERI, Albuquerque, NM 87106; Department of Psychiatry & Behavioral Sciences, University of New Mexico, Albuquerque, NM 87131; Department of Psychology, University of New Mexico, Albuquerque, NM 87131; Department of Neurology, University of New Mexico, Albuquerque, NM 87131
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Wang F, Liu Z, Ford SD, Deng M, Zhang W, Yang J, Palaniyappan L. Aberrant Brain Dynamics in Schizophrenia During Working Memory Task: Evidence From a Replication Functional MRI Study. Schizophr Bull 2024; 50:96-106. [PMID: 37018464 PMCID: PMC10754176 DOI: 10.1093/schbul/sbad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
BACKGROUND AND HYPOTHESIS The integration of information that typifies working memory (WM) operation requires a flexible, dynamic functional relationship among brain regions. In schizophrenia, though WM capacity is prominently impaired at higher loads, the mechanistic underpinnings are unclear. As a result, we lack convincing cognitive remediation of load-dependent deficits. We hypothesize that reduced WM capacity arises from a disruption in dynamic functional connectivity when patients face cognitive demands. STUDY DESIGN We calculate the dynamic voxel-wise degree centrality (dDC) across the functional connectome in 142 patients with schizophrenia and 88 healthy controls (HCs) facing different WM loads during an n-back task. We tested associations of the altered variability in dDC and clinical symptoms and identified intermediate connectivity configurations (clustered states) across time during WM operation. These analyses were repeated in another independent dataset of 169 subjects (102 with schizophrenia). STUDY RESULTS Compared with HCs, patients showed an increased dDC variability of supplementary motor area (SMA) for the "2back vs. 0back" contrast. This instability at the SMA seen in patients correlated with increased positive symptoms and followed a limited "U-shape" pattern at rest-condition and 2 loads. In the clustering analysis, patients showed reduced centrality in the SMA, superior temporal gyrus, and putamen. These results were replicated in a constrained search in the second independent dataset. CONCLUSIONS Schizophrenia is characterized by a load-dependent reduction of stable centrality in SMA; this relates to the severity of positive symptoms, especially disorganized behaviour. Restoring SMA stability in the presence of cognitive demands may have a therapeutic effect in schizophrenia.
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Affiliation(s)
- Feiwen Wang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Sabrina D Ford
- Robarts Research Institute, Western University, London, ON, Canada
| | - Mengjie Deng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Wen Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
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Guo Y, Xu X, Li T, Chen H, Wang X, Huang W, Liu T, Kong Q, Chen F. Dynamic functional connectivity changes associated with decreased memory performance in betel quid dependence. Addict Biol 2023; 28:e13329. [PMID: 37753571 DOI: 10.1111/adb.13329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 07/17/2023] [Accepted: 08/15/2023] [Indexed: 09/28/2023]
Abstract
The temporal variability of the dynamic functional connectivity (dFC) has been suggested as a useful metric for studying abnormal cognitive function. This study aimed to explore the associations between the temporal properties of dFC and memory performance in betel quid dependence (BQD). Sixty-four BQD individuals and 47 gender- and age-matched healthy controls (HCs) underwent functional magnetic resonance imaging and a series of neuropsychological assessments. The dFC was constructed by calculating the Pearson correlation coefficients within a sliding window and was clustered into three functional connectivity states using k-means clustering. The dFC temporal properties derived from the cluster results were compared between the BQD and HC groups. The results showed that States 1 and 3 featured more frequent and weak connectivity, and State 2 featured less frequent and strong connectivity. There were significant differences for mean dwell time (MDT) in State 3 (p = 0.022) and fraction of time in State 2 (p = 0.018) between the BQD and HC groups. Pearson correlation analyses showed that the MDT in State 1 was negatively correlated with long delay free recall and short delay free recall, and the MDT in State 3 was positively correlated with false positive of long delay recall. Our findings provide strong evidence that MDT match the memory performance and suggest new insights into the pathophysiological mechanism of memory disorders in BQD individuals.
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Affiliation(s)
- Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Xiaoling Xu
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Tiansheng Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Huijuan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Tao Liu
- Department of Geriatric Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qingle Kong
- MR Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
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Fallahi A, Hashemi-Fesharaki SS, Hoseini-Tabatabaei N, Pooyan M, Nazem-Zadeh MR. Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy. 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: 38082832 DOI: 10.1109/embc40787.2023.10339957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.
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Xu L, Wei H, Sun Z, Chu T, Li M, Liu R, Jiang L, Liang Z. Dynamic alterations of spontaneous neural activity in post-stroke aphasia: a resting-state functional magnetic resonance imaging study. Front Neurosci 2023; 17:1177930. [PMID: 37250389 PMCID: PMC10213748 DOI: 10.3389/fnins.2023.1177930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/20/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose The dynamic alterations in spontaneous neural activity of the brain during the acute phase of post-stroke aphasia (PSA) remain unclear. Therefore, in this study, dynamic amplitude of low-frequency fluctuation (dALFF) was applied to explore abnormal temporal variability in local functional activity of the brain during acute PSA. Materials and methods Resting-state functional magnetic resonance imaging (rs-fMRI) data from 26 patients with PSA and 25 healthy controls (HCs) were acquired. The sliding window method was used to assess dALFF, with the k-means clustering method used to identify dALFF states. The two-sample t-test was applied to compare differences in dALFF variability and state metrics between the PSA and HC groups. Results (1) In the PSA group, greater variance of dALFF in the cerebellar network (CBN) and left fronto-temporo-parietal network (FTPN) was observed. (2) Three dALFF states were identified among all subjects. States 1 and 2 were identified in the PSA patients, and the two dALFF states shared a similar proportion. Moreover, the number of transitions between the two dALFF states was higher in the patients compared with that in HCs. Conclusion The results of this study provide valuable insights into brain dysfunction that occurs during the acute phase (6.00 ± 3.52 days) of PSA. The observed increase in variability of local functional activities in CBN and left FTPN may be related to the spontaneous functional recovery of language during acute PSA, and it also suggests that cerebellum plays an important role in language.
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Affiliation(s)
- Luyao Xu
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Hongchun Wei
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Zhongwen Sun
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Min Li
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Ruhui Liu
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Li Jiang
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
| | - Zhigang Liang
- Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China
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Lambert J, Leutenegger AL, Jannot AS, Baudot A. Tracking clusters of patients over time enables extracting information from medico-administrative databases. J Biomed Inform 2023; 139:104309. [PMID: 36796599 DOI: 10.1016/j.jbi.2023.104309] [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: 10/03/2022] [Revised: 12/22/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023]
Abstract
CONTEXT Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data. OBJECTIVE We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. MATERIAL AND METHODS We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. RESULTS Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches. CONCLUSION The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.
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Affiliation(s)
- Judith Lambert
- Sorbonne Université, Université Paris Cité, INSERM, Centre de Recherche des Cordeliers, F-75006 Paris, France; HeKA, Inria Paris, F-75015 Paris, France; Aix Marseille Univ, INSERM, MMG, UMR1251, Marseille, France.
| | | | - Anne-Sophie Jannot
- HeKA, Inria Paris, F-75015 Paris, France; Université Paris Cité, Sorbonne Université, INSERM, Centre de Recherche des Cordeliers, F-75006 Paris, France; French National Rare Disease Registry (BNDMR), Greater Paris University Hospitals (AP-HP), Paris, France
| | - Anaïs Baudot
- Aix Marseille Univ, INSERM, MMG, UMR1251, Marseille, France; CNRS, Marseille, France; Barcelona Supercomputing Center, Barcelona, Spain
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Palmer WC, Park SM, Levendovszky SR. Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls. Front Neurosci 2022; 16:975305. [PMID: 36248645 PMCID: PMC9555083 DOI: 10.3389/fnins.2022.975305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer's disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer's disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls. Methods We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies-Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05. Results We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04). Conclusion We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.
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Blair DS, Soriano-Mas C, Cabral J, Moreira P, Morgado P, Deco G. Complexity changes in functional state dynamics suggest focal connectivity reductions. Front Hum Neurosci 2022; 16:958706. [PMID: 36211126 PMCID: PMC9540393 DOI: 10.3389/fnhum.2022.958706] [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: 05/31/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.
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Affiliation(s)
| | - Carles Soriano-Mas
- Psychiatry and Mental Health Group, Neuroscience Program, Institut d’Investigació Biomèdica de Bellvitge, Barcelona, Spain
- Network Center for Biomedical Research on Mental Health, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Joana Cabral
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center—Braga, Braga, Portugal
| | - Gustavo Deco
- Facultad de Comunicación, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Clayton, VIC, Australia
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13
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Zhou LF, Zhao D, Cui X, Guo B, Zhu F, Feng C, Wang J, Meng M. Separate neural subsystems support goal-directed speech listening. Neuroimage 2022; 263:119613. [PMID: 36075539 DOI: 10.1016/j.neuroimage.2022.119613] [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: 01/11/2022] [Revised: 08/30/2022] [Accepted: 09/04/2022] [Indexed: 10/31/2022] Open
Abstract
How do humans excel at tracking the narrative of a particular speaker with a distracting noisy background? This feat places great demands on the collaboration between speech processing and goal-related regulatory functions. Here, we propose that separate subsystems with different cross-task dynamic activity properties and distinct functional purposes support goal-directed speech listening. We adopted a naturalistic dichotic speech listening paradigm in which listeners were instructed to attend to only one narrative from two competing inputs. Using functional magnetic resonance imaging with inter- and intra-subject correlation techniques, we discovered a dissociation in response consistency in temporal, parietal and frontal brain areas as the task demand varied. Specifically, some areas in the bilateral temporal cortex (SomMotB_Aud and TempPar) and lateral prefrontal cortex (DefaultB_PFCl and ContA_PFCl) always showed consistent activation across subjects and across scan runs, regardless of the task demand. In contrast, some areas in the parietal cortex (DefaultA_pCunPCC and ContC_pCun) responded reliably only when the task goal remained the same. These results suggested two dissociated functional neural networks that were independently validated by performing a data-driven clustering analysis of voxelwise functional connectivity patterns. A subsequent meta-analysis revealed distinct functional profiles for these two brain correlation maps. The different-task correlation map was strongly associated with language-related processes (e.g., listening, speech and sentences), whereas the same-task versus different-task correlation map was linked to self-referencing functions (e.g., default mode, theory of mind and autobiographical topics). Altogether, the three-pronged findings revealed two anatomically and functionally dissociated subsystems supporting goal-directed speech listening.
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Affiliation(s)
- Liu-Fang Zhou
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; Key Lab of BI-AI Collaborated Information Behavior, School of Business and Management, Shanghai International Studies University, Shanghai, 201620, China
| | - Dan Zhao
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Xuan Cui
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Bingbing Guo
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China; School of Teacher Education, Nanjing Xiaozhuang University, Nanjing, 211171, China
| | - Fangwei Zhu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Chunliang Feng
- Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Jinhui Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China.
| | - Ming Meng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China.
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14
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Garcia-Ramirez DL, Singh S, McGrath JR, Ha NT, Dougherty KJ. Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties. Front Neural Circuits 2022; 16:957084. [PMID: 35991345 PMCID: PMC9385948 DOI: 10.3389/fncir.2022.957084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Spinal cord neurons integrate sensory and descending information to produce motor output. The expression of transcription factors has been used to dissect out the neuronal components of circuits underlying behaviors. However, most of the canonical populations of interneurons are heterogeneous and require additional criteria to determine functional subpopulations. Neurons expressing the transcription factor Shox2 can be subclassified based on the co-expression of the transcription factor Chx10 and each subpopulation is proposed to have a distinct connectivity and different role in locomotion. Adult Shox2 neurons have recently been shown to be diverse based on their firing properties. Here, in order to subclassify adult mouse Shox2 neurons, we performed multiple analyses of data collected from whole-cell patch clamp recordings of visually-identified Shox2 neurons from lumbar spinal slices. A smaller set of Chx10 neurons was included in the analyses for validation. We performed k-means and hierarchical unbiased clustering approaches, considering electrophysiological variables. Unlike the categorizations by firing type, the clusters displayed electrophysiological properties that could differentiate between clusters of Shox2 neurons. The presence of clusters consisting exclusively of Shox2 neurons in both clustering techniques suggests that it is possible to distinguish Shox2+Chx10- neurons from Shox2+Chx10+ neurons by electrophysiological properties alone. Computational clusters were further validated by immunohistochemistry with accuracy in a small subset of neurons. Thus, unbiased cluster analysis using electrophysiological properties is a tool that can enhance current interneuronal subclassifications and can complement groupings based on transcription factor and molecular expression.
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Affiliation(s)
| | | | | | | | - Kimberly J. Dougherty
- Department of Neurobiology and Anatomy, Marion Murray Spinal Cord Research Center, Drexel University College of Medicine, Philadelphia, PA, United States
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15
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Jiang F, Jin H, Gao Y, Xie X, Cummings J, Raj A, Nagarajan S. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging. Neuroimage 2022; 254:119131. [PMID: 35337963 PMCID: PMC9942947 DOI: 10.1016/j.neuroimage.2022.119131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/04/2022] [Accepted: 03/21/2022] [Indexed: 01/26/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.
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Affiliation(s)
- Fei Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
| | - Huaqing Jin
- Department of Statistics and Actuarial Science, the University of Hong Kong, CN, Hong Kong
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Jennifer Cummings
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
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16
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Wu Y, Zheng Y, Li J, Liu Y, Liang X, Chen Y, Zhang H, Wang N, Weng X, Qiu S, Wang J. Subregion-specific, modality-dependent and timescale-sensitive hippocampal connectivity alterations in patients with first-episode, drug-naïve major depression disorder. J Affect Disord 2022; 305:159-172. [PMID: 35218862 DOI: 10.1016/j.jad.2022.02.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/11/2022] [Accepted: 02/18/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Despite accumulating evidence for the hippocampus as a key dysfunctional node in major depressive disorder (MDD), previous findings are controversial possibly due to heterogeneous and small clinical samples, complicated hippocampal structure, and different imaging modalities and analytical methods. METHODS We collected structural and resting-state functional MRI data from 100 first-episode, drug-naïve MDD patients and 99 healthy controls. A subset of the participants (34 patients and 33 controls) also completed a battery of neuropsychological tests and childhood trauma questionnaires. Seed-based morphological and functional (static and dynamic) connectivity were calculated for ten hippocampal subregions, followed by analyses of dynamic functional connectivity states (k-means clustering), connectivity cross-modality relationships (cosine similarity), and connectivity associations with clinical and neuropsychological variables (Spearman correlation). RESULTS Between-group comparisons revealed abnormal hippocampal connectivity in the patients that depended on 1) hippocampal subdivisions: the cornu ammonis (CA) was the most seriously affected subregion, in particular the right CA1 for functional connectivity alterations; 2) imaging modality: morphological connectivity revealed seldom and sporadic alterations with different lobes, while functional connectivity identified numerous and convergent alterations with prefrontal regions; and 3) time scale: dynamic functional connectivity was more sensitive than static functional connectivity, in particular in revealing alterations between the right CA1 and contralateral prefrontal cortex. Among the 34 patients, functional connectivity alterations of the CA1 were related to the history of childhood trauma in the patients. LIMITATIONS Only a subset of the patients completed the neuropsychological tests, which may cause underestimation of cognitive relevance of hippocampal connectivity alterations. CONCLUSIONS Disrupted hippocampal CA1 functional connectivity plays key roles in the pathophysiology of MDD and may act as a potential diagnostic biomarker for the disease.
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Affiliation(s)
- Yujie Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China
| | - Yanting Zheng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China; Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Yujie Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China; Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Xinyu Liang
- The First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Yaoping Chen
- The Third Affliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510630, China
| | - Hanyue Zhang
- Department of Radiology, Guangzhou First People's Hospital, Guangdong 510180, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Xuchu Weng
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong 510405, China.
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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17
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Wang J, Wang K, Liu T, Wang L, Suo D, Xie Y, Funahashi S, Wu J, Pei G. Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease. Front Comput Neurosci 2022; 16:885126. [PMID: 35586480 PMCID: PMC9108158 DOI: 10.3389/fncom.2022.885126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.
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Affiliation(s)
- Jue Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shintaro Funahashi
- Kokoro Research Center, Kyoto University, Kyoto, Japan
- Laboratory of Cognitive Brain Science, Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China
- *Correspondence: Jinglong Wu
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Guangying Pei
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18
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Hein DM, Deng W, Bleile M, Kazmi SA, Rhead B, De La Vega FM, Jones AL, Kainthla R, Jiang W, Cantarel B, Sanford NN. Racial and Ethnic Differences in Genomic Profiling of Early Onset Colorectal Cancer. J Natl Cancer Inst 2022; 114:775-778. [PMID: 35134211 PMCID: PMC9086761 DOI: 10.1093/jnci/djac014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/19/2021] [Accepted: 01/12/2022] [Indexed: 02/04/2023] Open
Abstract
The incidence and mortality of early onset colorectal cancer (EOCRC) is rising; outcomes appear to differ by race and ethnicity. We aimed to assess differences in mutational landscape and gene expression of EOCRC by racial and ethnic groups (non-Hispanic Asian, non-Hispanic Black, non-Hispanic White, White Hispanic) using data from the American Association for Cancer Research Project GENIE (10.2) and University of Texas Southwestern, the latter enriched in Hispanic patients. All statistical tests were 2-sided. Of 1752 EOCRC patients, non-Hispanic Black patients had higher rates of KRAS mutations (60.9%; P = .001, q = 0.015), and non-Hispanic White and non-Hispanic Black patients had higher rates of APC mutations (77.1% and 76.6% among non-Hispanic White and non-Hispanic Black patients, respectively; P = .001, q = 0.015) via the Fisher exact test with Benjamini-Hochberg correction. Using R packages DESeq2 and clusterProfiler, we found that White Hispanic patients had increased expression of genes involved in oxidative phosphorylation (P < .001, q = 0.025). Genomic profiling has the potential to identify novel diagnostics and influence individualized treatment options to address the currently limited prognosis of EOCRC.
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Affiliation(s)
- David M Hein
- Department of Radiation Oncology, University of Texas Southwestern, Dallas, TX, USA
| | - Weiye Deng
- Department of Radiation Oncology, University of Texas Southwestern, Dallas, TX, USA,Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - MaryLena Bleile
- Department of Statistical Science, Southern Methodist University, Dallas, TX, USA
| | - Syed Ali Kazmi
- Department of Medical Oncology, University of Texas Southwestern, Dallas, TX, USA
| | | | | | - Amy L Jones
- Department of Medical Oncology, University of Texas Southwestern, Dallas, TX, USA
| | - Radhika Kainthla
- Department of Medical Oncology, University of Texas Southwestern, Dallas, TX, USA
| | - Wen Jiang
- Department of Radiation Oncology, University of Texas Southwestern, Dallas, TX, USA,Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Brandi Cantarel
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern, Dallas, TX, USA
| | - Nina N Sanford
- Correspondence to: Nina N. Sanford, MD, Department of Radiation Oncology, University of Texas Southwestern, 2280 Inwood Road, Dallas, TX 75390-9303, USA (e-mail: )
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19
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Vergara VM, Rafiei F, Wokke ME, Lau H, Rahnev D, Calhoun VD. Evidence for Transcranial Magnetic Stimulation Induced Functional Connectivity Oscillations in the Brain. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1407-1411. [PMID: 34891548 DOI: 10.1109/embc46164.2021.9629899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transcranial magnetic stimulation (TMS) is an effective research tool to elucidate mechanisms of function in the brain. Despite its widespread use, very few studies have looked at dynamic functional connectivity responses to TMS. This work performs an exploratory analysis of dynamic functional network connectivity (dynFNC) to evaluate evidence of brain response to TMS. Results show clear functional dynamic patterns categorized by frequency. Some patterns appear to be more directly linked to TMS, but there is one pattern that might be a TMS-independent response to the excitation. This first look presents an analysis methodology and important results to consider in future research.
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20
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Ahmadi M, Kazemi K, Kuc K, Cybulska-Klosowicz A, Helfroush MS, Aarabi A. Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder. J Neural Eng 2021; 18. [PMID: 34289458 DOI: 10.1088/1741-2552/ac16b3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity (dFC) between 113 children with inattentive (46 ADHDI) and combined (67 ADHDC) ADHD and 76 typically developing (TD) children using resting-state functional MRI data. For dynamic connectivity analysis, the data were first decomposed into 100 independent components, among which 88 were classified into eight well-known resting-state networks (RSNs). Three discrete FC states were then identified using k-means clustering and used to estimate transition probabilities between states in both patient and control groups using a hidden Markov model. Our results showed state-dependent alterations in intra and inter-network connectivity in both ADHD subtypes in comparison with TD. Spending less time than healthy controls in state 1, both ADHDIand ADHDCwere characterized with weaker intra-hemispheric connectivity with functional asymmetries. In this state, ADHDIfurther showed weaker inter-hemispheric connectivity. The patients spent more time in state 2, exhibiting characteristic abnormalities in corticosubcortical and corticocerebellar connectivity. In state 3, a less frequently state observed across the ADHD and TD children, ADHDCwas differentiated from ADHDIby significant alterations in FC between bilateral temporal regions and other brain areas in comparison with TD. Across all three states, several strategic brain regions, mostly bilateral, exhibited significant alterations in both static functional connectivity (sFC) and dFC in the ADHD groups compared to TD, including inferior, middle and superior temporal gyri, middle frontal gyri, insula, anterior cingulum cortex, precuneus, calcarine, fusiform, superior motor area, and cerebellum. Our results show distributed abnormalities in sFC and dFC between different large-scale RSNs including cortical and subcortical regions in both ADHD subtypes compared to TD. Our findings show that the dynamic changes in brain FC can better explain the underlying pathophysiology of ADHD such as deficits in visual cognition, attention, memory and emotion processing, and cognitive and motor control.
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Affiliation(s)
- Maliheh Ahmadi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Kamran Kazemi
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| | - Katarzyna Kuc
- SWPS University of Social Sciences and Humanities, Warsaw, Poland
| | - Anita Cybulska-Klosowicz
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Ardalan Aarabi
- Laboratory of Functional Neuroscience and Pathologies (LNFP EA4559), University Research Center (CURS), University Hospital, Amiens, France.,Faculty of Medicine, University of Picardy Jules Verne, Amiens, France
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21
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [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: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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22
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Teng G, Wang Q, Cui X, Chen G, Wei K, Xu X, Idrees BS, Nouman Khan M. Predictive data clustering of laser-induced breakdown spectroscopy for brain tumor analysis. BIOMEDICAL OPTICS EXPRESS 2021; 12:4438-4451. [PMID: 34457424 PMCID: PMC8367271 DOI: 10.1364/boe.431356] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 05/25/2023]
Abstract
Limited by the lack of training spectral data in different kinds of tissues, the diagnostic accuracy of laser-induced breakdown spectroscopy (LIBS) is hard to reach the desired level with normal supervised learning identification methods. In this paper, we proposed to apply the predictive data clustering methods with supervised learning methods together to identify tissue information accurately. The meanshift clustering method is introduced to compare with three other clustering methods which have been used in LIBS field. We proposed the cluster precision (CP) score as a new criterion to work with Calinski-Harabasz (CH) score together for the evaluation of the clustering effect. The influences of principal component analysis (PCA) on all four kinds of clustering methods are also analyzed. PCA-meanshift shows the best clustering effect based on the comprehensive evaluation combined CH and CP scores. Based on the spatial location and feature similarity information provided by the predictive clustering, the PCA-Meanshift can improve diagnosis accuracy from less than 95% to 100% for all classifiers including support vector machine (SVM), k nearest neighbor (k-NN), soft independent modeling of class analogy (Simca) and random forests (RF) models.
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Affiliation(s)
| | | | | | | | - Kai Wei
- Beijing Institute of Technology
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23
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Liu X, Chen Y, Cheng AS, Zeng Y, Ullah S, Feuerstein M. Conceptualizing problems with symptoms, function, health behavior, health-seeking skills, and financial strain in breast cancer survivors using hierarchical clustering. J Cancer Surviv 2021; 16:751-759. [PMID: 34109506 PMCID: PMC9300498 DOI: 10.1007/s11764-021-01068-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 05/28/2021] [Indexed: 11/27/2022]
Abstract
Purpose Determine whether a diverse set of problems experienced by breast cancer survivors (BCS) following curative treatment can be formulated into a reduced number of clusters, potentially simplifying the conceptualization of these problems. Method Female BCS were recruited from four cancer hospitals in China. The Chinese translation of the Cancer Survivor Profile (CSPro) was used to measure 18 common problem areas, as supported by epidemiological and phenomenological research. The Functional Assessment of Cancer Therapy–Breast (FACT-B) was used to measure quality of life, as a validation of any observed groupings. Hierarchical clustering using multiple distance criteria and aggregation methods to detect patterns of problems was used. Results A total of 1008 BCS (mean 46.51 years old) living in both urban and rural areas were investigated. Hierarchical cluster analysis identified two major clusters of problems. One set was classified as “functional limitations,” while the other cluster was labeled “multi-problems.” Those who fell into the multi-problem cluster experienced poorer quality of life. Conclusion Eighteen non-medical problems were broken down into two major clusters: (1) limitations in higher level functions required of daily life and (2) limitations in health care–seeking skills, problems with certain symptoms, unhealthy behaviors, and financial problems related to cancer. The breakdown of problem areas into these two clusters may help identify common mechanisms. Implications for Cancer Survivors In the future, the search for common clusters and the mechanisms for the many problems that breast cancer survivors and other cancer survivors can experience following primary treatment may improve how we help manage these problems in the future.
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Affiliation(s)
- Xiangyu Liu
- Department of Health Service Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
| | - Yongyi Chen
- Department of Institute Office, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.
| | - Andy Sk Cheng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yingchun Zeng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Shahid Ullah
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
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24
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Chen S, Wu Y, Qi H, Shen L, Ma W, Cao F, Diao Y, Wang T, Ou S, Fan W. Single-nodule hepatitis B virus-associated hepatocellular carcinoma smaller than 3 cm: two phenotypes defined by cluster analysis and their association with the outcome of ablation as the first-line therapy. Int J Hyperthermia 2021; 38:120-129. [PMID: 33541160 DOI: 10.1080/02656736.2021.1876930] [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: 10/22/2022] Open
Abstract
OBJECTIVES Hepatocellular carcinoma (HCC) is a heterogeneous disease. This study aimed to identify the heterogeneity related to the prognosis of ablation in patients with single-nodule hepatitis B virus (HBV)-associated HCC ≤3 cm. METHODS A total of 359 patients with single-nodule HBV-associated HCC ≤3 cm treated with curative thermal ablation were retrospectively investigated. Hierarchical cluster analysis was applied to obtain more homogeneous patient clusters concerning demographic and physiological characteristics. Discriminant analysis was performed to identify the relatively important variables for cluster analysis. Multiple correspondence analysis (MCA) was used to clarify the relationship between clusters and categorical variables. Overall survival (OS) was compared among clusters using the Kaplan-Meier model. RESULTS A two-cluster model was identified. Cluster 1 (n = 85) showed a higher percentage of female and older patients, higher inflammation response (higher prognostic nutritional index [PNI] and Glasgow prognostic score [GPS]), worse liver function (higher albumin-bilirubin grade and Child-Pugh grade), and relatively poorer immune status (higher neutrophil-to-lymphocyte ratio [NLR]) than cluster 2 (n = 274). NLR and GPS were the two most influential variables for cluster analysis (p < .0001). Cluster 2 had a significantly better prognosis than cluster 1. MCA revealed a clear negative correlation between inflammation status and liver function. Compared with cluster 1, the hazard ratios for OS of cluster 2 were 0.47 and 0.52 before and after adjusting for age, respectively (p < .05). CONCLUSIONS This study identified two sub-phenotypes of patients with single-nodule HBV-associated HCC ≤3 cm and their association with the outcome of thermal ablation alone as the first-line therapy. Key points Thermal ablation alone as the first-line therapy is not suitable for all patients with single-nodule hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) ≤3 cm. Patients with single-nodule HBV-associated HCC ≤3 cm can be identified as two sub-phenotypes associated with the outcome of thermal ablation alone as the first-line therapy, based on key preoperative clinical characteristics, especially inflammatory response and immune status. Patients with single-nodule HBV-associated HCC ≤3 cm characterized by late-onset disease, worse liver function, poorer immune status, and higher inflammatory response (with higher inflammatory response being the most important factor) are not suitable for thermal ablation alone as the first-line therapy. In contrast, patients with single-nodule HBV-associated HCC ≤3 cm characterized by early-onset disease, better liver function, lower inflammatory response, and good immune status (with lower inflammatory response being the most important factor) are particularly suitable for thermal ablation alone. Implications for patient care In the treatment of patients with single-nodule HBV-associated HCC ≤3 cm, thermal ablation alone as the first-line therapy should be carefully considered after recognizing the key clinical characteristics, among which inflammatory response and immune status are the two most important factors involved in clinical heterogeneity, and inflammatory response is closely related to the prognosis of thermal ablation alone as the first-line therapy for these patients.
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Affiliation(s)
- Shuanggang Chen
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Ying Wu
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Han Qi
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lujun Shen
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weimei Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China.,Department of Radiology, The Eighth Affiliated Hospotal, Sun Yat-sen University, Shenzhen, People's Republic of China
| | - Fei Cao
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yuhong Diao
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Ting Wang
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Shunling Ou
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weijun Fan
- Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University, Guangzhou, People's Republic of China
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25
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Kuang D, Michoski C. Dual stream neural networks for brain signal classification. J Neural Eng 2021; 18. [DOI: 10.1088/1741-2552/abc903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/10/2020] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. The primary objective of this work is to develop a neural nework classifier for arbitrary collections of functional neuroimaging signals to be used in brain–computer interfaces (BCIs). Approach. We propose a dual stream neural network (DSNN) for the classification problem. The first stream is an end-to-end classifier taking raw time-dependent signals as input and generating feature identification signatures from them. The second stream enhances the identified features from the first stream by adjoining a dynamic functional connectivity matrix aimed at incorporating nuanced multi-channel information during specified BCI tasks. Main results. The proposed DSNN classifier is benchmarked against three publicly available datasets, where the classifier demonstrates performance comparable to, or better than the state-of-art in each instance. An information theoretic examination of the trained network is also performed, utilizing various tools, to demonstrate how to glean interpretive insight into how the hidden layers of the network parse the underlying biological signals. Significance
. The resulting DSNN is a subject-independent classifier that works for any collection of 1D functional neuroimaging signals, with the option of integrating domain specific information in the design.
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26
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Long Q, Bhinge S, Calhoun VD, Adali T. Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia. J Neurosci Methods 2020; 350:109039. [PMID: 33370561 DOI: 10.1016/j.jneumeth.2020.109039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Dynamic functional network connectivity (dFNC) summarizes associations among time-varying brain networks and is widely used for studying dynamics. However, most previous studies compute dFNC using temporal variability while spatial variability started receiving increasing attention. It is hence desirable to investigate spatial variability and the interaction between temporal and spatial variability. NEW METHOD We propose to use an adaptive variant of constrained independent vector analysis to simultaneously capture temporal and spatial variability, and introduce a goal-driven scheme for addressing a key challenge in dFNC analysis---determining the number of transient states. We apply our methods to resting-state functional magnetic resonance imaging data of schizophrenia patients (SZs) and healthy controls (HCs). RESULTS The results show spatial variability provides more features discriminative between groups than temporal variability. A comprehensive study of graph-theoretical (GT) metrics determines the optimal number of spatial states and suggests centrality as a key metric. Four networks yield significantly different levels of involvement in SZs and HCs. The high involvement of a component that relates to multiple distributed brain regions highlights dysconnectivity in SZ. One frontoparietal component and one frontal component demonstrate higher involvement in HCs, suggesting a more efficient cognitive control system relative to SZs. COMPARISON WITH EXISTING METHODS Spatial variability is more informative than temporal variability. The proposed goal-driven scheme determines the optimal number of states in a more interpretable way by making use of discriminative features. CONCLUSION GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.
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Affiliation(s)
- Qunfang Long
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA.
| | - Suchita Bhinge
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA
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27
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Li C, Dong M, Womer FY, Han S, Yin Y, Jiang X, Wei Y, Duan J, Feng R, Zhang L, Zhang X, Wang F, Tang Y, Xu K. Transdiagnostic time-varying dysconnectivity across major psychiatric disorders. Hum Brain Mapp 2020; 42:1182-1196. [PMID: 33210798 PMCID: PMC7856647 DOI: 10.1002/hbm.25285] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/23/2020] [Accepted: 11/03/2020] [Indexed: 12/21/2022] Open
Abstract
Dynamic functional connectivity (DFC) analysis can capture time‐varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting‐state functional magnetic resonance imaging and a sliding‐window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k‐means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4‐group differences (SZ, BD, MDD, and HC groups; q < .05, false‐discovery rate [FDR]‐corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR‐corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state‐dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.
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Affiliation(s)
- Chao Li
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Mengshi Dong
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Shaoqiang Han
- Department of MRI, The First Affiliated Hospital of ZhengZhou University, ZhengZhou, China
| | - Yi Yin
- Guangdong Second Provincial General Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xiaowei Jiang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yange Wei
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jia Duan
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ruiqi Feng
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Luheng Zhang
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Fei Wang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yanqing Tang
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China.,Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.,Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
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28
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Bolton TA, Morgenroth E, Preti MG, Van De Ville D. Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics. Trends Neurosci 2020; 43:667-680. [DOI: 10.1016/j.tins.2020.06.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/24/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022]
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29
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Marapin RS, van der Stouwe AMM, de Jong BM, Gelauff JM, Vergara VM, Calhoun VD, Dalenberg JR, Dreissen YEM, Koelman JHTM, Tijssen MAJ, van der Horn HJ. The chronnectome as a model for Charcot's 'dynamic lesion' in functional movement disorders. Neuroimage Clin 2020; 28:102381. [PMID: 32927233 PMCID: PMC7495110 DOI: 10.1016/j.nicl.2020.102381] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 08/07/2020] [Accepted: 08/09/2020] [Indexed: 01/14/2023]
Abstract
This exploratory study set out to investigate dynamic functional connectivity (dFC) in patients with jerky and tremulous functional movement disorders (JT-FMD). The focus in this work is on dynamic brain states, which represent distinct dFC patterns that reoccur in time and across subjects. Resting-state fMRI data were collected from 17 patients with JT-FMD and 17 healthy controls (HC). Symptom severity was measured using the Clinical Global Impression-Severity scale. Depression and anxiety were measured using the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI), respectively. Independent component analysis was used to extract functional brain components. After computing dFC, dynamic brain states were determined for every subject using k-means clustering. Compared to HC, patients with JT-FMD spent more time in a state that was characterized predominantly by increasing medial prefrontal, and decreasing posterior midline connectivity over time. They also tended to visit this state more frequently. In addition, patients with JT-FMD transitioned significantly more often between different states compared to HC, and incorporated a state with decreasing medial prefrontal, and increasing posterior midline connectivity in their attractor, i.e., the cyclic patterns of state transitions. Altogether, this is the first study that demonstrates altered functional brain network dynamics in JT-FMD that may support concepts of increased self-reflective processes and impaired sense of agency as driving factors in FMD.
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Affiliation(s)
- Ramesh S Marapin
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
| | - A M Madelein van der Stouwe
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
| | - Bauke M de Jong
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands.
| | - Jeannette M Gelauff
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, 55 Park Pl NE, Atlanta, GA 30303, United States
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, 55 Park Pl NE, Atlanta, GA 30303, United States.
| | - Jelle R Dalenberg
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
| | - Yasmine E M Dreissen
- Neurology and Clinical Neurophysiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands.
| | - Johannes H T M Koelman
- Neurology and Clinical Neurophysiology, Amsterdam University Medical Center, location AMC, Amsterdam, The Netherlands.
| | - Marina A J Tijssen
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; Expertise Center Movement Disorders Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
| | - Harm J van der Horn
- Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands.
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30
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Iraji A, Faghiri A, Lewis N, Fu Z, Rachakonda S, Calhoun VD. Tools of the trade: estimating time-varying connectivity patterns from fMRI data. Soc Cogn Affect Neurosci 2020; 16:849-874. [PMID: 32785604 PMCID: PMC8343585 DOI: 10.1093/scan/nsaa114] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/24/2020] [Accepted: 08/05/2020] [Indexed: 01/04/2023] Open
Abstract
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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