Large scale neural model of the human cortex functional connectivity based on empirically derived resting state networks

Schizophrenia may involve an elevated excitation/inhibition (E/I) ratio in cortical microcircuits. It remains unknown how this regulatory disturbance maps onto neuroimaging findings. To address this issue, we implemented E/I perturbations within a neural model of large-scale functional connectivity, which predicted hyperconnectivity following E/I elevation. To test predictions, we examined resting-state functional MRI in 161 schizophrenia patients and 164 healthy subjects. As predicted, patients exhibited elevated functional connectivity that correlated with symptom levels, and was most prominent in association cortices, such as the fronto-parietal control network. This pattern was absent in patients with bipolar disorder (n = 73). To account for the pattern observed in schizophrenia, we integrated neurobiologically plausible, hierarchical differences in association vs. sensory recurrent neuronal dynamics into our model. This in silico architecture revealed preferential vulnerability of association networks to E/I imbalance, which we verified empirically. Reported effects implicate widespread microcircuit E/I imbalance as a parsimonious mechanism for emergent inhomogeneous dysconnectivity in schizophrenia.

Download Full-text

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Scientific Reports ◽

10.1038/s41598-021-03349-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Katrin M. Beckmann ◽

Adriano Wang-Leandro ◽

Henning Richter ◽

Rima N. Bektas ◽

Frank Steffen ◽

...

Keyword(s):

Functional Connectivity ◽

Default Mode Network ◽

Resting State ◽

Large Scale ◽

Group Level ◽

Resting State Networks ◽

Default Mode ◽

Starting Point ◽

Healthy Control ◽

Large Scale Networks

AbstractEpilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

Download Full-text

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.

Download Full-text

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

Download Full-text

Graph slepians to probe into large-scale network organization of resting-state functional connectivity

2017 51st Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/acssc.2017.8335615 ◽

2017 ◽

Author(s):

Maria Giulia Preti ◽

Dimitri Van De Ville

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Network Organization ◽

Resting State Functional Connectivity ◽

Large Scale Network ◽

Scale Network

Download Full-text

Stroke-related alterations in inter-areal communication revealed via Granger causality analysis

10.1101/2021.01.04.425190 ◽

2021 ◽

Author(s):

Michele Allegra ◽

Chiara Favaretto ◽

Nicholas Metcalf ◽

Maurizio Corbetta ◽

Andrea Brovelli

Keyword(s):

Functional Connectivity ◽

Granger Causality ◽

Resting State ◽

Information Transfer ◽

Large Scale ◽

Functional Organization ◽

Information Flows ◽

Causality Analysis ◽

Stroke Patients ◽

Granger Causality Analysis

ABSTRACTNeuroimaging and neurological studies suggest that stroke is a brain network syndrome. While causing local ischemia and cell damage at the site of injury, stroke strongly perturbs the functional organization of brain networks at large. Critically, functional connectivity abnormalities parallel both behavioral deficits and functional recovery across different cognitive domains. However, the reasons for such relations remain poorly understood. Here, we tested the hypothesis that alterations in inter-areal communication underlie stroke-related modulations in functional connectivity (FC). To this aim, we used resting-state fMRI and Granger causality analysis to quantify information transfer between brain areas and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was strongly decreased with respect to healthy controls. Second, information transfer within the affected hemisphere, and from the affected to the intact hemisphere was reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they were correlated with impaired performance in several behavioral domains. Overall, our results support the hypothesis that stroke perturbs inter-areal communication within and across hemispheres, and suggest novel therapeutic approaches aimed at restoring normal information flow.SIGNIFICANCE STATEMENTA thorough understanding of how stroke perturbs brain function is needed to improve recovery from the severe neurological syndromes affecting stroke patients. Previous resting-state neuroimaging studies suggested that interaction between hemispheres decreases after stroke, while interaction between areas of the same hemisphere increases. Here, we used Granger causality to reconstruct information flows in the brain at rest, and analyze how stroke perturbs them. We showed that stroke causes a global reduction of inter-hemispheric communication, and an imbalance between the intact and the affected hemisphere: information flows within and from the latter are impaired. Our results may inform the design of stimulation therapies to restore the functional balance lost after stroke.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Attention network modulation via tRNS correlates with attention gain

eLife ◽

10.7554/elife.63782 ◽

2021 ◽

Vol 10 ◽

Author(s):

Federica Contò ◽

Grace Edwards ◽

Sarah Tyler ◽

Danielle Parrott ◽

Emily Grossman ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Random Noise ◽

Cognitive Capacity ◽

Resting State Functional Connectivity ◽

Attention Network ◽

Transcranial Random Noise Stimulation ◽

The Impact ◽

Behavioral Improvement

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

Download Full-text