Early stages of sensorimotor map acquisition: neurochemical signature in primary motor cortex and its relation to functional connectivity

Learning the mapping between movements and their sensory effects is a necessary step in the early stages of sensorimotor learning. There is evidence showing which brain areas are involved in early motor learning, but their role remains uncertain. Here, we show that GABA, a neurotransmitter linked to inhibitory processing, rises during and after learning and is involved in ongoing changes in resting-state networks.

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

eLife ◽

10.7554/elife.01465 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 86

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.

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

Neural Plasticity ◽

10.1155/2016/2306406 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 7

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.

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

JAMA Psychiatry ◽

10.1001/jamapsychiatry.2015.0101 ◽

2015 ◽

Vol 72 (8) ◽

pp. 767 ◽

Cited By ~ 138

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

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Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

10.1101/2021.09.01.458609 ◽

2021 ◽

Author(s):

Yusi Chen ◽

Qasim Bukhari ◽

Tiger Wutu Lin ◽

Terrence J Sejnowski

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Connectivity ◽

Resting State ◽

Structural Connectivity ◽

Reaction Times ◽

Resonance Imaging ◽

Brain Areas ◽

Human Connectome Project ◽

Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

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

10.1101/2021.02.09.430495 ◽

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.

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

10.1101/634063 ◽

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.

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

Journal of Movement Disorders ◽

10.14802/jmd.17061 ◽

2018 ◽

Vol 11 (1) ◽

pp. 13-23 ◽

Cited By ~ 1

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

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Sex Difference in Language Processing in Patients With Malignant Brain Tumors

Neurosurgery ◽

10.1093/neuros/nyz310_507 ◽

2019 ◽

Vol 66 (Supplement_1) ◽

Author(s):

Shun Yao ◽

Einat Liebenthal ◽

Parikshit Juvekar ◽

Adomas Bunevicius ◽

Matthew Vera ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Tumors ◽

Language Processing ◽

Resting State ◽

Precentral Gyrus ◽

Language Function ◽

Brain Areas ◽

Brain Organization ◽

Malignant Brain Tumors ◽

Parietal Lobule

Abstract INTRODUCTION Numerous differences between males and females in brain organization have been described including in the development, performance, and lateralization of language function. However, there is very limited knowledge of whether language processing differs across sex in patients with brain lesions. In particular, malignant brain tumors (MBT) demonstrate significant sex differences in incidence and long-term survival. Given the importance of brain organization and planning surgical treatment for patients with brain tumors, we investigated the effect of sex on the organization of language in a cohort of patients with MBT. METHODS In the current study, we carried out a retrospective analysis in 47 patients with MBT (22 females, 25 males), retrieving their clinical characteristics and task-based and resting-state functional magnetic resonance image (fMRI) data from our clinical database. General Linear Model (GLM) and region-of-interest (ROI) based resting-state functional connectivity (RSFC) analyses were applied to explore the effect of sex on language tasks associated activations and functional connectivity. RESULTS Across the Sentence Completion task and Antonym Generation task, female patients showed greater activation volumes in the left inferior frontal gyrus, right precuneus, and left superior parietal lobule, while male patients showed larger clusters of activation of the left supplemental motor area (SMA), left inferior parietal lobule (IPL), left precuneus, bilateral precentral gyrus, and right supramarginal gyrus (SMG). Furthermore, the left SMA was a highly sex-specific brain area during the language performance, and it showed stronger resting-state correlations with brain areas within the intrinsic language network in females, while it showed stronger resting-state connections with brain areas involving the visuomotor/higher level cognitive functions in males. CONCLUSION These findings enhance our understanding of the role of sex in language organization in patients with MBT, helping neurosurgeons assess surgical risk and plan surgery in patients with MBT to best preserve language function.

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