Alteration of brain resting‐state networks and functional connectivity in prelingual deafness

2021 ◽  
Author(s):  
Uttam Kumar ◽  
Amit Keshri ◽  
Mrutyunjaya Mishra
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Networks ◽  
Prelingual Deafness

scholarly journals Resting-State Functional Connectivity and Cognitive Impairment in Children with Perinatal Stroke

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Nigul Ilves ◽  
Pilvi Ilves ◽  
Rael Laugesaar ◽  
Julius Juurmaa ◽  
Mairi Männamaa ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Functions ◽  
Resting State ◽  
Outcome Measurement ◽  
Pediatric Stroke ◽  
Resting State Networks ◽  
Neurocognitive Deficits ◽  
Resting State Functional Connectivity ◽  
Perinatal Stroke ◽  
Brain Functions

Perinatal stroke is a leading cause of congenital hemiparesis and neurocognitive deficits in children. Dysfunctions in the large-scale resting-state functional networks may underlie cognitive and behavioral disability in these children. We studied resting-state functional connectivity in patients with perinatal stroke collected from the Estonian Pediatric Stroke Database. Neurodevelopment of children was assessed by the Pediatric Stroke Outcome Measurement and the Kaufman Assessment Battery. The study included 36 children (age range 7.6–17.9 years): 10 with periventricular venous infarction (PVI), 7 with arterial ischemic stroke (AIS), and 19 controls. There were no differences in severity of hemiparesis between the PVI and AIS groups. A significant increase in default mode network connectivity (FDR 0.1) and lower cognitive functions (p<0.05) were found in children with AIS compared to the controls and the PVI group. The children with PVI had no significant differences in the resting-state networks compared to the controls and their cognitive functions were normal. Our findings demonstrate impairment in cognitive functions and neural network profile in hemiparetic children with AIS compared to children with PVI and controls. Changes in the resting-state networks found in children with AIS could possibly serve as the underlying derangements of cognitive brain functions in these children.

scholarly journals Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

2015 ◽  
Vol 72 (8) ◽  
pp. 767 ◽  
Author(s):  
Leonardo Cerliani ◽  
Maarten Mennes ◽  
Rajat M. Thomas ◽  
Adriana Di Martino ◽  
Marc Thioux ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Networks

scholarly journals Local GABA concentration is related to network-level resting functional connectivity

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Charlotte J Stagg ◽  
Velicia Bachtiar ◽  
Ugwechi Amadi ◽  
Christel A Gudberg ◽  
Andrei S Ilie ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Empirical Studies ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Resonance Spectroscopy ◽  
Resting State Networks ◽  
Inhibitory Neurotransmitter ◽  
Motor Network

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

scholarly journals The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Measurement Noise ◽  
Resting State Networks ◽  
Cross Sectional ◽  
Global Signal ◽  
Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

scholarly journals Alteration in the Local and Global Functional Connectivity of Resting State Networks in Parkinson’s Disease

2018 ◽  
Vol 11 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Maryam Ghahremani ◽  
Jaejun Yoo ◽  
Sun Ju Chung ◽  
Kwangsun Yoo ◽  
Jong C. Ye ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Networks

scholarly journals Frequency‐dependent functional connectivity in resting state networks

2020 ◽  
Vol 41 (18) ◽  
pp. 5187-5198
Author(s):  
Jessica Samogin ◽  
Marco Marino ◽  
Camillo Porcaro ◽  
Nicole Wenderoth ◽  
Patrick Dupont ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Networks ◽  
Frequency Dependent

Abnormal functional connectivity within resting-state networks is related to rTMS-based therapy effects of treatment resistant depression: A pilot study

2017 ◽  
Vol 218 ◽  
pp. 75-81 ◽  
Author(s):  
Ruiyang Ge ◽  
Daniel M. Blumberger ◽  
Jonathan Downar ◽  
Zafiris J. Daskalakis ◽  
Adam A. Dipinto ◽  
...  
Keyword(s):  
Pilot Study ◽  
Functional Connectivity ◽  
Resting State ◽  
Treatment Resistant Depression ◽  
Resting State Networks ◽  
Treatment Resistant ◽  
Resistant Depression

scholarly journals Aberrant functional connectivity of resting state networks related to misperceptions and intra-individual variability in Parkinson‘s disease

2020 ◽  
Vol 25 ◽  
pp. 102076 ◽  
Author(s):  
Kristina Miloserdov ◽  
Carsten Schmidt-Samoa ◽  
Kathleen Williams ◽  
Christiane Anne Weinrich ◽  
Igor Kagan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Individual Variability ◽  
Resting State Networks

scholarly journals Atypical functional connectivity in resting-state networks of individuals with 22q11.2 deletion syndrome: associations with neurocognitive and psychiatric functioning

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Leah M. Mattiaccio ◽  
Ioana L. Coman ◽  
Matthew J. Schreiner ◽  
Kevin M. Antshel ◽  
Wanda P. Fremont ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
22Q11.2 Deletion Syndrome ◽  
Deletion Syndrome ◽  
Resting State Networks ◽  
22Q11.2 Deletion ◽  
Psychiatric Functioning
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