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Gao L, Cao Y, Zhang Y, Liu J, Zhang T, Zhou R, Guo X. Sex differences in the flexibility of dynamic network reconfiguration of autism spectrum disorder based on multilayer network. Brain Imaging Behav 2024:10.1007/s11682-024-00907-5. [PMID: 39212890 DOI: 10.1007/s11682-024-00907-5] [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] [Accepted: 08/10/2024] [Indexed: 09/04/2024]
Abstract
Dynamic network reconfiguration alterations in the autism spectrum disorder (ASD) brain have been frequently reported. However, since the prevalence of ASD in males is approximately 3.8 times higher than that in females, and previous studies of dynamic network reconfiguration of ASD have predominantly used male samples, it is unclear whether sex differences exist in dynamic network reconfiguration in ASD. This study used resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database, which included balanced samples of 64 males and 64 females with ASD, along with 64 demographically-matched typically developing control (TC) males and 64 TC females. The multilayer network analysis was used to explore the flexibility of dynamic network reconfiguration. The two-way analysis of variance was further performed to examine the sex-related changes in ASD in flexibility of dynamic network reconfiguration. A diagnosis-by-sex interaction effect was identified in the cingulo-opercular network (CON), central executive network (CEN), salience network (SN), and subcortical network (SUB). Compared with TC females, females with ASD showed lower flexibility in CON, CEN, SN, and SUB. The flexibility of CEN and SUB in males with ASD was higher than that in females with ASD. In addition, the flexibility of CON, CEN, SN, and SUB predicted the severity of social communication impairments and stereotyped behaviors and restricted interests only in females with ASD. These findings highlight significant sex differences in the flexibility of dynamic network reconfiguration in ASD and emphasize the importance of further study of sex differences in future ASD research.
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Affiliation(s)
- Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China
| | - Yabo Cao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China
| | - Yigeng Zhang
- Department of Computer Science, University of Houston, Houston, TX, 77204-3010, USA
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, China
| | - Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China.
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2
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De Rosa AP, d'Ambrosio A, Bisecco A, Altieri M, Cirillo M, Gallo A, Esposito F. Functional gradients reveal cortical hierarchy changes in multiple sclerosis. Hum Brain Mapp 2024; 45:e26678. [PMID: 38647001 PMCID: PMC11033924 DOI: 10.1002/hbm.26678] [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: 11/17/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alessandro d'Ambrosio
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Alvino Bisecco
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Manuela Altieri
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Mario Cirillo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Antonio Gallo
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
| | - Fabrizio Esposito
- Advanced MRI Neuroimaging Centre, Department of Advanced Medical and Surgical SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
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Wang Y, Yang Z, Zheng X, Liang X, Chen J, He T, Zhu Y, Wu L, Huang M, Zhang N, Zhou F. Temporal and topological properties of dynamic networks reflect disability in patients with neuromyelitis optica spectrum disorders. Sci Rep 2024; 14:4199. [PMID: 38378887 PMCID: PMC10879085 DOI: 10.1038/s41598-024-54518-7] [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: 08/20/2023] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
Approximately 36% of patients with neuromyelitis optica spectrum disorders (NMOSD) suffer from severe visual and motor disability (blindness or light perception or unable to walk) with abnormalities of whole-brain functional networks. However, it remains unclear how whole-brain functional networks and their dynamic properties are related to clinical disability in patients with NMOSD. Our study recruited 30 NMOSD patients (37.70 ± 11.99 years) and 45 healthy controls (HC, 41.84 ± 11.23 years). The independent component analysis, sliding-window approach and graph theory analysis were used to explore the static strength, time-varying and topological properties of large-scale functional networks and their associations with disability in NMOSD. Compared to HC, NMOSD patients showed significant alterations in dynamic networks rather than static networks. Specifically, NMOSD patients showed increased occurrence (fractional occupancy; P < 0.001) and more dwell times of the low-connectivity state (P < 0.001) with fewer transitions (P = 0.028) between states than HC, and higher fractional occupancy, increased dwell times of the low-connectivity state and lower transitions were related to more severe disability. Moreover, NMOSD patients exhibited altered small-worldness, decreased degree centrality and reduced clustering coefficients of hub nodes in dynamic networks, related to clinical disability. NMOSD patients exhibited higher occurrence and more dwell time in low-connectivity states, along with fewer transitions between states and decreased topological organizations, revealing the disrupted communication and coordination among brain networks over time. Our findings could provide new perspective to help us better understand the neuropathological mechanism of the clinical disability in NMOSD.
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Affiliation(s)
- Yao Wang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Ziwei Yang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Xiumei Zheng
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Xiao Liang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Jin Chen
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Ting He
- Department of Radiology, Pingxiang People's Hospital, Pingxiang, 337055, Jiangxi Province, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Lin Wu
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi Province, China.
- Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, 330006, Jiangxi Province, China.
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Baldini S, Morelli ME, Sartori A, Pasquin F, Dinoto A, Bratina A, Bosco A, Manganotti P. Microstates in multiple sclerosis: an electrophysiological signature of altered large-scale networks functioning? Brain Commun 2022; 5:fcac255. [PMID: 36601622 PMCID: PMC9806850 DOI: 10.1093/braincomms/fcac255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/07/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
Multiple sclerosis has a highly variable course and disabling symptoms even in absence of associated imaging data. This clinical-radiological paradox has motivated functional studies with particular attention to the resting-state networks by functional MRI. The EEG microstates analysis might offer advantages to study the spontaneous fluctuations of brain activity. This analysis investigates configurations of voltage maps that remain stable for 80-120 ms, termed microstates. The aim of our study was to investigate the temporal dynamic of microstates in patients with multiple sclerosis, without reported cognitive difficulties, and their possible correlations with clinical and neuropsychological parameters. We enrolled fifty relapsing-remitting multiple sclerosis patients and 24 healthy subjects, matched for age and sex. Demographic and clinical data were collected. All participants underwent to high-density EEG in resting-state and analyzed 15 min free artefact segments. Microstates analysis consisted in two processes: segmentation, to identify specific templates, and back-fitting, to quantify their temporal dynamic. A neuropsychological assessment was performed by the Brief International Cognitive Assessment for Multiple Sclerosis. Repeated measures two-way ANOVA was run to compare microstates parameters of patients versus controls. To evaluate association between clinical, neuropsychological and microstates data, we performed Pearsons' correlation and stepwise multiple linear regression to estimate possible predictions. The alpha value was set to 0.05. We found six templates computed across all subjects. Significant differences were found in most of the parameters (global explained variance, time coverage, occurrence) for the microstate Class A (P < 0.001), B (P < 0.001), D (P < 0.001), E (P < 0.001) and F (P < 0.001). In particular, an increase of temporal dynamic of Class A, B and E and a decrease of Class D and F were observed. A significant positive association of disease duration with the mean duration of Class A was found. Eight percent of patients with multiple sclerosis were found cognitive impaired, and the multiple linear regression analysis showed a strong prediction of Symbol Digit Modalities Test score by global explained variance of Class A. The EEG microstate analysis in patients with multiple sclerosis, without overt cognitive impairment, showed an increased temporal dynamic of the sensory-related microstates (Class A and B), a reduced presence of the cognitive-related microstates (Class D and F), and a higher activation of a microstate (Class E) associated to the default mode network. These findings might represent an electrophysiological signature of brain reorganization in multiple sclerosis. Moreover, the association between Symbol Digit Modalities Test and Class A may suggest a possible marker of overt cognitive dysfunctions.
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Affiliation(s)
- Sara Baldini
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Maria Elisa Morelli
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Arianna Sartori
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Fulvio Pasquin
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Alessandro Dinoto
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Alessio Bratina
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Antonio Bosco
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
| | - Paolo Manganotti
- Neurology Unit, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, 34149 Trieste, Italy
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Masina F, Pezzetta R, Lago S, Mantini D, Scarpazza C, Arcara G. Disconnection from prediction: A systematic review on the role of right temporoparietal junction in aberrant predictive processing. Neurosci Biobehav Rev 2022; 138:104713. [PMID: 35636560 DOI: 10.1016/j.neubiorev.2022.104713] [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: 01/21/2022] [Revised: 05/06/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
Abstract
The right temporoparietal junction (rTPJ) is a brain area that plays a critical role in a variety of cognitive functions. Although different theoretical proposals tried to explain the ubiquitous role of rTPJ, recent evidence suggests that rTPJ may be a fundamental cortical region involved in different kinds of predictions. This systematic review aims to better investigate the potential role of rTPJ under a predictive processing perspective, providing an overview of cognitive impairments in neurological patients as the consequence of structural or functional disconnections or damage of rTPJ. Results confirm the involvement of rTPJ across several tasks and neurological pathologies. RTPJ, via its connections with other brain networks, would integrate diverse information and update internal models of the world. Against traditional views, which tend to focus on distinct domains, we argue that the role of rTPJ can be parsimoniously interpreted as a key hub involved in domain-general predictions. This alternative account of rTPJ role in aberrant predictive processing opens different perspectives, stimulating new hypotheses in basic research and clinical contexts.
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Affiliation(s)
| | | | - Sara Lago
- IRCCS San Camillo Hospital, Venice, Italy.
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven 3001, Belgium.
| | - Cristina Scarpazza
- IRCCS San Camillo Hospital, Venice, Italy; Department of General Psychology, University of Padua, Padua, Italy.
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Rocca MA, Schoonheim MM, Valsasina P, Geurts JJG, Filippi M. Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective. Neuroimage Clin 2022; 35:103076. [PMID: 35691253 PMCID: PMC9194954 DOI: 10.1016/j.nicl.2022.103076] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 01/12/2023]
Abstract
Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages. Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease. Collapse of long-range connections and impaired hub integration characterize MS network reorganization. Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity. New perspectives might be the use of multimodal MRI and artificial intelligence.
Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.
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Affiliation(s)
- Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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Pervaiz U, Vidaurre D, Gohil C, Smith SM, Woolrich MW. Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations. Med Image Anal 2022; 77:102366. [PMID: 35131700 PMCID: PMC8907871 DOI: 10.1016/j.media.2022.102366] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/29/2021] [Accepted: 01/10/2022] [Indexed: 11/23/2022]
Abstract
The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability.
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Affiliation(s)
- Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom.
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom; Department of Clinical Medicine, Aarhus University, Denmark
| | - Chetan Gohil
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
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Bao J, Tu H, Li Y, Sun J, Hu Z, Zhang F, Li J. Diffusion Tensor Imaging Revealed Microstructural Changes in Normal-Appearing White Matter Regions in Relapsing–Remitting Multiple Sclerosis. Front Neurosci 2022; 16:837452. [PMID: 35310094 PMCID: PMC8924457 DOI: 10.3389/fnins.2022.837452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAxons and myelin sheaths are the physical foundation for white matter (WM) to perform normal functions. Our previous study found the metabolite abnormalities in frontal, parietal, and occipital normal-appearing white matter (NAWM) regions in relapsing–remitting multiple sclerosis (RRMS) patients by applying a 2D 1H magnetic resonance spectroscopic imaging method. Since the metabolite changes may associate with the microstructure changes, we used the diffusion tensor imaging (DTI) method to assess the integrity of NAWM in this study.MethodDiffusion tensor imaging scan was performed on 17 clinically definite RRMS patients and 21 age-matched healthy controls on a 3.0-T scanner. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were extracted from 19 predefined regions of interest (ROIs), which were generated by removing a mask of manually drawn probabilistic lesion map from the Johns Hopkins University white-matter atlas. The mean values of FA, MD, AD, and RD were compared between different groups in the same ROIs.ResultsA probabilistic lesion map was successfully generated, and the lesion regions were eliminated from the WM atlas. We found that the RRMS patients had significantly lower FA in the entire corpus callosum (CC), bilateral of anterior corona radiata, and right posterior thalamic radiation (PTR). At the same time, RRMS patients showed significantly higher MD in the bilateral anterior corona radiata and superior corona radiata. Moreover, all AD values increased, and the bilateral external capsule, PTR, and left tapetum NAWM show statistical significance. What is more, all NAWM tracts showed increasing RD values in RRMS patients, and the bilateral superior corona radiata, the anterior corona radiata, right PTR, and the genu CC reach statistical significance.ConclusionOur study revealed widespread microstructure changes in NAWM in RRMS patients through a ready-made WM atlas and probabilistic lesion map. These findings support the hypothesis of demyelination, accumulation of inflammatory cells, and axonal injury in NAWM for RRMS. The DTI-based metrics could be considered as potential non-invasive biomarkers of disease severity.
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Affiliation(s)
- Jianfeng Bao
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Hui Tu
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Yijia Li
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jubao Sun
- MRI Center, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
| | - Zhigang Hu
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Fengshou Zhang
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
- *Correspondence: Fengshou Zhang,
| | - Jinghua Li
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
- Jinghua Li,
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Tozlu C, Jamison K, Gauthier SA, Kuceyeski A. Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis. Front Neurosci 2021; 15:763966. [PMID: 34966255 PMCID: PMC8710545 DOI: 10.3389/fnins.2021.763966] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/17/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status. Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks. Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC). Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability. Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.
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Affiliation(s)
- Ceren Tozlu
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Susan A. Gauthier
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York, NY, United States
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
- Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
- *Correspondence: Amy Kuceyeski
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10
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Huiskamp M, Eijlers AJC, Broeders TAA, Pasteuning J, Dekker I, Uitdehaag BMJ, Barkhof F, Wink AM, Geurts JJG, Hulst HE, Schoonheim MM. Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis. Neurology 2021; 97:e794-e802. [PMID: 34099528 PMCID: PMC8397585 DOI: 10.1212/wnl.0000000000012341] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To characterize functional network changes related to conversion to cognitive impairment in a large sample of patients with multiple sclerosis (MS) over a period of 5 years. METHODS Two hundred twenty-seven patients with MS and 59 healthy controls of the Amsterdam MS cohort underwent neuropsychological testing and resting-state fMRI at 2 time points (time interval 4.9 ± 0.9 years). At both baseline and follow-up, patients were categorized as cognitively preserved (CP; n = 123), mildly impaired (MCI; z < -1.5 on ≥2 cognitive tests, n = 32), or impaired (CI; z < -2 on ≥2 tests, n = 72), and longitudinal conversion between groups was determined. Network function was quantified with eigenvector centrality, a measure of regional network importance, which was computed for individual resting-state networks at both time points. RESULTS Over time, 18.9% of patients converted to a worse phenotype; 22 of 123 patients who were CP (17.9%) converted from CP to MCI, 10 of 123 from CP to CI (8.1%), and 12 of 32 patients with MCI converted to CI (37.5%). At baseline, default-mode network (DMN) centrality was higher in CI individuals compared to controls (p = 0.05). Longitudinally, ventral attention network (VAN) importance increased in CP, driven by stable CP and CP-to-MCI converters (p < 0.05). CONCLUSIONS Of all patients, 19% worsened in their cognitive status over 5 years. Conversion from intact cognition to impairment is related to an initial disturbed functioning of the VAN, then shifting toward DMN dysfunction in CI. Because the VAN normally relays information to the DMN, these results could indicate that in MS normal processes crucial for maintaining overall network stability are progressively disrupted as patients clinically progress.
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Affiliation(s)
- Marijn Huiskamp
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK.
| | - Anand J C Eijlers
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Tommy A A Broeders
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Jasmin Pasteuning
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Iris Dekker
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Bernard M J Uitdehaag
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Frederik Barkhof
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Alle-Meije Wink
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Jeroen J G Geurts
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Hanneke E Hulst
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
| | - Menno M Schoonheim
- From the Department of Anatomy and Neurosciences (M.H., A.J.C.E., T.A.A.B., J.P., J.J.G.G., H.E.H., M.M.S.), Department of Neurology (I.D., B.M.J.U.), and Department of Radiology and Nuclear Medicine (I.D., F.B., A.-M.W.), MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; and UCL Institutes of Neurology and Healthcare Engineering (F.B.), London, UK
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11
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Hidalgo de la Cruz M, Valsasina P, Sangalli F, Esposito F, Rocca MA, Filippi M. Dynamic Functional Connectivity in the Main Clinical Phenotypes of Multiple Sclerosis. Brain Connect 2021; 11:678-690. [PMID: 33813839 DOI: 10.1089/brain.2020.0920] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Dynamic functional connectivity (dFC) allows capturing recurring patterns (states) of interaction among functional networks. In this study, we investigated resting state (RS) dFC abnormalities across the different clinical phenotypes of multiple sclerosis (MS) and assessed their correlation with motor and cognitive performances. Methods: RS functional magnetic resonance imaging (fMRI) and 3D T1-weighted MRI data were acquired from 128 MS patients (69 relapsing-remitting [RR] MS, 34 secondary progressive [SP] MS, and 25 primary progressive [PP] MS) and 40 healthy controls (HC). RS fMRI data underwent independent component analysis and sliding-window correlations, to identify recurring dFC states and between-group dFC differences in the main networks. Results: dFC identified three recurring connectivity states: State 1 (frequency of appearance during fMRI acquisition = 57%, low dFC strength), State 2 (frequency = 19%, middle-high dFC strength), and State 3 (frequency = 24%, high sensorimotor and visual dFC strength). Compared to HC, MS (as a whole), RRMS, and PPMS patients exhibited lower State1/State 3 dFC (p = 0.0001, corrected) between sensorimotor, cerebellar, and cognitive networks, and some dFC increments (p = 0.001-0.05, uncorrected) in sensorimotor, visual, default-mode, and frontal/attention networks in States 2 and 3. Similar results were observed in SPMS versus RRMS patients. In MS, dFC decrease in sensorimotor, default-mode, and frontal/attention networks was correlated with worse motor and cognitive performances. Conclusions: MS patients exhibited overall lower dFC, and marginally higher dFC in sensorimotor/cognitive networks in the less-frequent middle/high-connected States. dFC abnormalities became more severe in progressive MS and correlated with motor and cognitive impairment, suggesting the presence of maladaptive mechanisms concomitant with accumulation of damage.
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Affiliation(s)
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy
| | | | - Federica Esposito
- Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service and IRCSS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCSS San Raffaele Scientific Institute, Milan, Italy
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12
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Dynamic functional connectivity as a neural correlate of fatigue in multiple sclerosis. NEUROIMAGE-CLINICAL 2021; 29:102556. [PMID: 33472144 PMCID: PMC7815811 DOI: 10.1016/j.nicl.2020.102556] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/14/2020] [Accepted: 12/30/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND More than 80% of multiple sclerosis (MS) patients experience symptoms of fatigue. MS-related fatigue is only partly explained by structural (lesions and atrophy) and functional (brain activation and conventional static functional connectivity) brain properties. OBJECTIVES To investigate the relationship of dynamic functional connectivity (dFC) with fatigue in MS patients and to compare dFC with commonly used clinical and MRI parameters. METHODS In 35 relapsing-remitting MS patients (age: 42.83 years, female/male: 20/15, disease duration: 11 years) and 19 healthy controls (HCs) (age: 41.38 years, female/male: 11/8), fatigue was measured using the CIS-20r questionnaire at baseline and at 6-month follow-up. All subjects underwent structural and resting-state functional MRI at baseline. Global static functional connectivity (sFC) and dynamic functional connectivity (dFC) were calculated. dFC was assessed using a sliding-window approach by calculating the summed difference (diff) and coefficient of variation (cv) across windows. Moreover, regional connectivity between regions previously associated with fatigue in MS was estimated (i.e. basal ganglia and regions of the Default Mode Network (DMN): medial prefrontal, posterior cingulate and precuneal cortices). Hierarchical regression analyses were performed with forward selection to identify the most important correlates of fatigue at baseline. Results were not corrected for multiple testing due to the exploratory nature of the study. RESULTS Patients were more fatigued than HCs at baseline (p = 0.001) and follow-up (p = 0.002) and fatigue in patients was stable over time (p = 0.213). Patients had significantly higher baseline global dFC than HCs, but no difference in basal ganglia-DMN dFC. In the regression model for baseline fatigue in patients, basal ganglia-DMN dFC-cv (standardized β = -0.353) explained 12.5% additional variance on top of EDSS (p = 0.032). Post-hoc analysis revealed higher basal ganglia-DMN dFC-cv in non-fatigued patients compared to healthy controls (p = 0.013), whereas fatigued patients and healthy controls showed similar basal ganglia-DMN dFC. CONCLUSIONS Less dynamic connectivity between the basal ganglia and the cortex is associated with greater fatigue in MS patients, independent of disability status. Within patients, lower dynamics of these connections could relate to lower efficiency and increased fatigue. Increased dynamics in non-fatigued patients compared to healthy controls might represent a network organization that protects against fatigue or signal early network dysfunction.
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13
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Wu L, Huang M, Zhou F, Zeng X, Gong H. Distributed causality in resting-state network connectivity in the acute and remitting phases of RRMS. BMC Neurosci 2020; 21:37. [PMID: 32933478 PMCID: PMC7493168 DOI: 10.1186/s12868-020-00590-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 09/09/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Although previous studies have shown that intra-network abnormalities in brain functional networks are correlated with clinical/cognitive impairment in multiple sclerosis (MS), there is little information regarding the pattern of causal interactions among cognition-related resting-state networks (RSNs) in different disease stages of relapsing-remitting MS (RRMS) patients. We hypothesized that abnormalities of causal interactions among RSNs occurred in RRMS patients in the acute and remitting phases. METHODS Seventeen patients in the acute phases of RRMS, 24 patients in the remitting phases of RRMS, and 23 appropriately matched healthy controls participated in this study. First, we used group independent component analysis to extract the time courses of the spatially independent components from all the subjects. Then, the Granger causality analysis was used to investigate the causal relationships among RSNs in the spectral domain and to identify correlations with clinical indices. RESULTS Compared with the patients in the acute phase of RRMS, patients in the remitting phase of RRMS showed a significantly lower expanded disability status scale, modified fatigue impact scale scores, and significantly higher paced auditory serial addition test (PASAT) scores. Compared with healthy subjects, during the acute phase, RRMS patients had significantly increased driving connectivity from the right executive control network (rECN) to the anterior salience network (aSN), and the causal coefficient was negatively correlated with the PASAT score. During the remitting phase, RRMS patients had significantly increased driving connectivity from the rECN to the aSN and from the rECN to the visuospatial network. CONCLUSIONS Together with the disease duration (mean disease duration < 5 years) and relatively better clinical scores than those in the acute phase, abnormal connections, such as the information flow from the rECN to the aSN and the rECN to the visuospatial network, might provide adaptive compensation in the remitting phase of RRMS.
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Affiliation(s)
- Lin Wu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Muhua Huang
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China. .,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China.
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
| | - Honghan Gong
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, People's Republic of China.,Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China
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14
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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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15
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Yang J, Gohel S, Vachha B. Current methods and new directions in resting state fMRI. Clin Imaging 2020; 65:47-53. [PMID: 32353718 DOI: 10.1016/j.clinimag.2020.04.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 03/24/2020] [Accepted: 04/08/2020] [Indexed: 12/12/2022]
Abstract
Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.
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Affiliation(s)
- Jackie Yang
- NYU Grossman School of Medicine, 550 1(st) Avenue, New York, NY 10016, USA
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA
| | - Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
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16
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Valsasina P, Hidalgo de la Cruz M, Filippi M, Rocca MA. Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis. Front Neurosci 2019; 13:618. [PMID: 31354402 PMCID: PMC6636554 DOI: 10.3389/fnins.2019.00618] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 05/29/2019] [Indexed: 01/27/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.
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Affiliation(s)
- Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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