Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization

2017 ◽  
Vol 24 (13) ◽  
pp. 1696-1705 ◽  
Author(s):  
Alvino Bisecco ◽  
Federica Di Nardo ◽  
Renato Docimo ◽  
Giuseppina Caiazzo ◽  
Alessandro d’Ambrosio ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Regression Analysis ◽  
Functional Connectivity ◽  
Motor Cortex ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Linear Regression Analysis ◽  
Posterior Cingulate Cortex ◽  
Resting State Functional Connectivity ◽  
Sensorimotor Network

Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks.

scholarly journals Primary motor cortex mapping in brain-lesioned patients using MEG resting-state functional connectivity

2014 ◽  
Vol 8 ◽  
Author(s):  
Coquelet Nicolas ◽  
Wens Vincent ◽  
Bourguignon Mathieu ◽  
Carrette Evelien ◽  
Op De Beeck Marc ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Motor Cortex ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Resting State Functional Connectivity ◽  
Motor Cortex Mapping

scholarly journals Neural basis underlying the trait of attachment anxiety and avoidance revealed by the amplitude of low-frequency fluctuations and resting-state functional connectivity

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Min Deng ◽  
Xing Zhang ◽  
Xiaoyan Bi ◽  
Chunhai Gao
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Low Frequency ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Attachment Anxiety ◽  
Frequency Fluctuation ◽  
Resting State Functional Connectivity ◽  
Attachment Avoidance ◽  
Posterior Cingulate

Abstract Background Attachment theory demonstrates that early attachment experience shapes internal working models with mental representations of self and close relationships, which affects personality traits and interpersonal relationships in adulthood. Although research has focused on brain structural and functional underpinnings to disentangle attachment styles in healthy individuals, little is known about the spontaneous brain activity associated with self-reported attachment anxiety and avoidance during the resting state. Methods One hundred and nineteen individuals participated in the study, completing the Experience in Close Relationship scale immediately after an 8-min fMRI scanning. We used the resting-state functional magnetic resonance imaging (rs-fMRI) signal of the amplitude of low-frequency fluctuation and resting-state functional connectivity to identify attachment-related regions and networks. Results Consequently, attachment anxiety is closely associated with the amplitude of low-frequency fluctuations in the right posterior cingulate cortex, over-estimating emotional intensity and exaggerating outcomes. Moreover, the functional connectivity between the posterior cingulate cortex and fusiform gyrus increases detection ability for potential threat or separation information, facilitating behavior motivation. The attachment avoidance is positively correlated with the amplitude of low-frequency fluctuation in the bilateral lingual gyrus and right postcentral and negatively correlated with the bilateral orbital frontal cortex and inferior temporal gyrus. Functional connection with attachment avoidance contains critical nodes in the medial temporal lobe memory system, frontal-parietal network, social cognition, and default mode network necessary to deactivate the attachment system and inhibit attachment-related behavior. Conclusion and implications These findings clarify the amplitude of low-frequency fluctuation and resting-state functional connectivity neural signature of attachment style, associated with attachment strategies in attachment anxiety and attachment avoidance individuals. These findings may improve our understanding of the pathophysiology of the attachment-related disorder.

scholarly journals Motor Sequences - Separating The Sequence From The Motor. A longitudinal rsfMRI Study

2021 ◽  
Author(s):  
ATP Jäger ◽  
JM Huntenburg ◽  
SA Tremblay ◽  
U Schneider ◽  
S Grahl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Motor Learning ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Control Group ◽  
Motor Execution ◽  
Motor Sequence ◽  
Resting State Functional Connectivity ◽  
Fast Learning ◽  
Slow Learning

AbstractIn motor learning, sequence-specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific increases in functional connectivity during fast learning in the sensorimotor territory of the internal segment of right globus pallidus (GPi), and sequence-specific decreases in right supplementary motor area (SMA) in overall learning. We found that connectivity changes in key regions of the motor network including the superior parietal cortex (SPC) and primary motor cortex (M1) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA and GPi that has previously been identified in online task-based learning studies in humans and primates, and extends it to resting state network changes after sequence-specific MSL. Finally, our results shed light on a timing-specific plasticity mechanism between GPi and SMA following MSL.

Resting-state functional connectivity networks associated with fatigue in multiple sclerosis with early age onset

2019 ◽  
Vol 31 ◽  
pp. 101-105 ◽  
Author(s):  
Patricia Stefancin ◽  
Sindhuja T Govindarajan ◽  
Lauren Krupp ◽  
Leigh Charvet ◽  
Timothy Q Duong
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Early Age ◽  
Resting State Functional Connectivity

Identifying Resting-State Functional Connectivity Changes in the Motor Cortex Using fNIRS During Recovery from Stroke

2020 ◽  
Vol 33 (6) ◽  
pp. 710-719
Author(s):  
K. M. Arun ◽  
K. A. Smitha ◽  
P. N. Sylaja ◽  
Chandrasekharan Kesavadas
Keyword(s):  
Functional Connectivity ◽  
Motor Cortex ◽  
Resting State ◽  
Resting State Functional Connectivity

scholarly journals Sex Differences in Resting-State Functional Connectivity in Multiple Sclerosis

2013 ◽  
Vol 34 (12) ◽  
pp. 2304-2311 ◽  
Author(s):  
K.A. Koenig ◽  
M.J. Lowe ◽  
J. Lin ◽  
K.E. Sakaie ◽  
L. Stone ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Sex Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity

Resting-state functional connectivity in multiple sclerosis: An examination of group differences and individual differences

2013 ◽  
Vol 51 (13) ◽  
pp. 2918-2929 ◽  
Author(s):  
Alisha L. Janssen ◽  
Aaron Boster ◽  
Beth A. Patterson ◽  
Amir Abduljalil ◽  
Ruchika Shaurya Prakash
Keyword(s):  
Multiple Sclerosis ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Group Differences ◽  
Resting State Functional Connectivity

scholarly journals Anterior Insular Resting‐State Functional Connectivity is Related to Cognitive Reserve in Multiple Sclerosis

2020 ◽  
Author(s):  
Bernardo Canedo Bizzo ◽  
Tiago Arruda‐Sanchez ◽  
Sean M Tobyne ◽  
John Daniel Bireley ◽  
Michael Howard Lev ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Cognitive Reserve ◽  
Resting State Functional Connectivity

scholarly journals M158. ASSOCIATIONS OF NEUROLOGICAL SOFT SIGNS AND CEREBELLAR-CEREBRAL FUNCTIONAL CONNECTIVITY IN PATIENTS WITH FIRST-EPISODE SCHIZOPHRENIA AND THEIR UNAFFECTED SIBLINGS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S196-S196
Author(s):  
Xinlu Cai ◽  
Yongming Wang ◽  
Hanyu Zhou ◽  
Jia Huang ◽  
Simon S Y Lui ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Sensory Integration ◽  
Motor Coordination ◽  
Linear Regression Analysis ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Sensorimotor Network ◽  
Potential Biomarker ◽  
First Episode Schizophrenia

Abstract Background Neurological softs signs (NSS) are defined as subtle neurological abnormalities with manifestations of motor coordination, sensory integration and disinhibition. Evidence has suggested NSS as one of the most promising endophenotypes for schizophrenia spectrum disorders. Moreover, accumulating evidence also suggest that NSS may be associated with specific functional connectivity. The present study aimed to examine the cerebellar-cerebral resting-state functional connectivity (rsFC) of NSS in patients with first-episode schizophrenia (FES) and their unaffected siblings (SB). Methods We administered the abridge version of the Cambridge Neurological Inventory (CNI) to 51 FES patients, 20 unaffected SB, and 50 healthy controls (HC) to assess the severity of NSS. All the participants also underwent a resting-state functional magnetic resonance imaging (MRI) scan. Ten regions of interest (ROIs) in the cerebellum were selected to represent cerebellar motor network (MN) and cerebellar executive control network (EN), which corresponded to the “sensorimotor-cognitive” dichotomy of NSS. rsFC between each ROI and the whole brain voxels were constructed, and the linear regression analysis was conducted to examine the cerebellar-cerebral rsFC patterns of NSS in each group. Results Regarding the cerebellar MN, there were positive correlations observed between the rsFC of the cerebellar MN with the default mode network (DMN) and NSS in FES patients group (CNI total score and the motor coordination subscale) and the SB group (CNI total score and the motor coordination and sensory integration subscales). The rsFC of the cerebellar MN and the sensorimotor network were significantly and positively correlated with NSS (CNI total score and the motor coordination and sensory integration subscales) in the SB group. Regarding the cerebellar EN, we found that both the FES and the SB groups exhibited significantly negative correlations between NSS (CNI total score and the motor coordination subscale) and the rsFC of the cerebellar EN with the DMN. Moreover, the rsFC between the cerebellar EN and the sensorimotor network was positively correlated with NSS (CNI total score and the motor coordination and disinhibition subscales) in the SB group. Discussion We found inverse correlations between NSS and the rsFC of the cerebellar EN/MN and the DMN in both FES patients and their unaffected SB, suggesting that altered cerebellar-cerebral rsFC between these networks is correlated with the NSS. Moreover, the SB group exhibited a unique correlational pattern that NSS were correlated with the cerebellar-sensorimotor network rsFC, suggesting that such a network connectivity may serve as a potential biomarker for schizophrenia.

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