Network Mapping of Connectivity Alterations in Disorder of Consciousness: Towards Targeted Neuromodulation

Disorder of consciousness (DoC) refers to a group of clinical conditions that may emerge after brain injury, characterized by a varying decrease in the level of consciousness that can last from days to years. An understanding of its neural correlates is crucial for the conceptualization and application of effective therapeutic interventions. Here we propose a quantitative meta-analysis of the neural substrate of DoC emerging from functional magnetic resonance (fMRI) and positron emission tomography (PET) studies. We also map the relevant networks of resulting areas to highlight similarities with Resting State Networks (RSNs) and hypothesize potential therapeutic solutions leveraging network-targeted noninvasive brain stimulation. Available literature was reviewed and analyzed through the activation likelihood estimate (ALE) statistical framework to describe resting-state or task-dependent brain activation patterns in DoC patients. Results show that task-related activity is limited to temporal regions resembling the auditory cortex, whereas resting-state fMRI data reveal a diffuse decreased activation affecting two subgroups of cortical (angular gyrus, middle frontal gyrus) and subcortical (thalamus, cingulate cortex, caudate nucleus) regions. Clustering of their cortical functional connectivity projections identify two main altered functional networks, related to decreased activity of (i) the default mode and frontoparietal networks, as well as (ii) the anterior salience and visual/auditory networks. Based on the strength and topography of their connectivity profile, biophysical modeling of potential brain stimulation solutions suggests the first network as the most feasible target for tES, tDCS neuromodulation in DoC patients.

Download Full-text

Noninvasive brain stimulation for patients with a disorder of consciousness: a systematic review and meta-analysis

Reviews in the Neurosciences ◽

10.1515/revneuro-2020-0033 ◽

2020 ◽

Vol 31 (8) ◽

pp. 905-914 ◽

Cited By ~ 1

Author(s):

Yali Feng ◽

Jiaqi Zhang ◽

Yi Zhou ◽

Zhongfei Bai ◽

Ying Yin

Keyword(s):

Brain Stimulation ◽

Meta Analysis ◽

Minimally Conscious State ◽

Conscious State ◽

Primary Outcome Measure ◽

Noninvasive Brain Stimulation ◽

Disorder Of Consciousness ◽

Left Dlpfc ◽

Dorsolateral Prefrontal ◽

Minimally Conscious

AbstractNoninvasive brain stimulation (NIBS) techniques have been used to facilitate the recovery from prolonged unconsciousness as a result of brain injury. The aim of this study is to systematically assess the effects of NIBS in patients with a disorder of consciousness (DOC). We searched four databases for any randomized controlled trials on the effect of NIBS in patients with a DOC, which used the JFK Coma Recovery Scale-Revised (CRS-R) as the primary outcome measure. A random-effects meta-analysis was conducted to pool effect sizes. Fourteen studies with 273 participants were included in this review, of which 12 studies with sufficient data were included in the meta-analysis. Our meta-analysis showed a significant effect on increasing CRS-R scores in favor of real stimulation as compared to sham (Hedges’ g = 0.522; 95% confidence interval [CI], 0.318–0.726; P < 0.0001, I2 = 0.00%). Subgroup analysis demonstrated that only anodal transcranial direct current stimulation (tDCS) of the left dorsolateral prefrontal cortex (DLPFC) significantly enhances the CRS-R scores in patients with a DOC, as compared to sham (Hedges’ g = 0.703; 95% CI, 0.419–0.986; P < 0.001), and this effect was predominant in patients in a minimally conscious state (MCS) (Hedges’ g = 0.815; 95% CI, 0.429–1.200; P < 0.001). Anodal tDCS of the left DLPFC appears to be an effective approach for patients with MCS.

Download Full-text

Resting State fMRI: A Valuable Tool for Studying Cognitive Dysfunction in PD

Parkinson s Disease ◽

10.1155/2018/6278649 ◽

2018 ◽

Vol 2018 ◽

pp. 1-5 ◽

Cited By ~ 1

Author(s):

Kai Li ◽

Wen Su ◽

Shu-Hua Li ◽

Ying Jin ◽

Hai-Bo Chen

Keyword(s):

Cognitive Impairment ◽

Cognitive Dysfunction ◽

Cognitive Deficits ◽

Resting State ◽

Lewy Bodies ◽

Resting State Fmri ◽

Underlying Mechanism ◽

Research Field ◽

Functional Magnetic Resonance ◽

Positron Emission

Cognitive impairment is a common disabling symptom in PD. Unlike motor symptoms, the mechanism underlying cognitive dysfunction in Parkinson’s disease (PD) remains unclear and may involve multiple pathophysiological processes. Resting state functional magnetic resonance imaging (rs-fMRI) is a fast-developing research field, and its application in cognitive impairments in PD is rapidly growing. In this review, we summarize rs-fMRI studies on cognitive function in PD and discuss the strong potential of rs-fMRI in this area. rs-fMRI can help reveal the pathophysiology of cognitive symptoms in PD, facilitate early identification of PD patients with cognitive impairment, distinguish PD dementia from dementia with Lewy bodies, and monitor and guide treatment for cognitive impairment in PD. In particular, ongoing and future longitudinal studies would enhance the ability of rs-fMRI in predicting PD dementia. In combination with other modalities such as positron emission tomography, rs-fMRI could give us more information on the underlying mechanism of cognitive deficits in PD.

Download Full-text

“Resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis”: Answer to Wang and colleagues

Parkinsonism & Related Disorders ◽

10.1016/j.parkreldis.2019.07.014 ◽

2019 ◽

Vol 66 ◽

pp. 253-254

Author(s):

Amée F. Wolters ◽

Sjors C.F. van de Weijer ◽

Albert F.G. Leentjens ◽

Annelien A. Duits ◽

Heidi I.L. Jacobs ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Resting State ◽

Meta Analysis ◽

Resting State Fmri

Download Full-text

Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

10.1101/2021.11.28.470231 ◽

2021 ◽

Author(s):

Pavithra Elumalai ◽

Yasharth Yadav ◽

Nitin Williams ◽

Emil Saucan ◽

Jürgen Jost ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Resting State ◽

Neurodevelopmental Disorders ◽

Data Exchange ◽

Meta Analysis ◽

Resting State Fmri ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

Download Full-text

Music reduces pain and increases resting state fMRI BOLD signal amplitude in the left angular gyrus in fibromyalgia patients

Frontiers in Psychology ◽

10.3389/fpsyg.2015.01051 ◽

2015 ◽

Vol 6 ◽

Cited By ~ 33

Author(s):

Eduardo A. Garza-Villarreal ◽

Zhiguo Jiang ◽

Peter Vuust ◽

Sarael Alcauter ◽

Lene Vase ◽

...

Keyword(s):

Resting State ◽

Signal Amplitude ◽

Resting State Fmri ◽

Angular Gyrus ◽

Bold Signal ◽

Left Angular Gyrus

Download Full-text

Functional alterations of fronto-limbic circuit and default mode network systems in first-episode, drug-naïve patients with major depressive disorder: A meta-analysis of resting-state fMRI data

Journal of Affective Disorders ◽

10.1016/j.jad.2016.09.005 ◽

2016 ◽

Vol 206 ◽

pp. 280-286 ◽

Cited By ~ 53

Author(s):

Xue Zhong ◽

Weidan Pu ◽

Shuqiao Yao

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Resting State ◽

Meta Analysis ◽

Resting State Fmri ◽

Fmri Data ◽

First Episode ◽

Network Systems ◽

Major Depressive ◽

Drug Naïve

Download Full-text

Additive and interaction effects of working memory and motor sequence training on brain functional connectivity

Scientific Reports ◽

10.1038/s41598-021-02492-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Priska Zuber ◽

Laura Gaetano ◽

Alessandra Griffa ◽

Manuel Huerbin ◽

Ludovico Pedullà ◽

...

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Processing Speed ◽

Resting State ◽

Resting State Fmri ◽

Interactive Effects ◽

Angular Gyrus ◽

Precentral Gyrus ◽

Motor Sequence ◽

Brain Functional Connectivity

AbstractAlthough shared behavioral and neural mechanisms between working memory (WM) and motor sequence learning (MSL) have been suggested, the additive and interactive effects of training have not been studied. This study aimed at investigating changes in brain functional connectivity (FC) induced by sequential (WM + MSL and MSL + WM) and combined (WM × MSL) training programs. 54 healthy subjects (27 women; mean age: 30.2 ± 8.6 years) allocated to three training groups underwent twenty-four 40-min training sessions over 6 weeks and four cognitive assessments including functional MRI. A double-baseline approach was applied to account for practice effects. Test performances were compared using linear mixed-effects models and t-tests. Resting state fMRI data were analysed using FSL. Processing speed, verbal WM and manual dexterity increased following training in all groups. MSL + WM training led to additive effects in processing speed and verbal WM. Increased FC was found after training in a network including the right angular gyrus, left superior temporal sulcus, right superior parietal gyrus, bilateral middle temporal gyri and left precentral gyrus. No difference in FC was found between double baselines. Results indicate distinct patterns of resting state FC modulation related to sequential and combined WM and MSL training suggesting a relevance of the order of training performance. These observations could provide new insight for the planning of effective training/rehabilitation.

Download Full-text

Deep learning-based classification of resting-state fMRI independent-component analysis

10.1101/2020.07.02.183772 ◽

2020 ◽

Author(s):

Victor Nozais ◽

Philippe Boutinaud ◽

Violaine Verrecchia ◽

Marie-Fateye Gueye ◽

Pierre Yves Hervé ◽

...

Keyword(s):

Deep Learning ◽

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Independent Component ◽

Angular Gyrus ◽

Automated Classification ◽

Dimensional Parameter ◽

Spatial Level

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively. We trained a multi-layer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions on the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

Download Full-text

Meta-analysis of resting-state fMRI in depression: generating spatial hypotheses for potential clinical applications

10.7287/peerj.preprints.412v1 ◽

2014 ◽

Cited By ~ 1

Author(s):

Benedikt Sundermann ◽

Mona Olde lütke Beverborg ◽

Bettina Pfleiderer

Keyword(s):

Resting State ◽

Spatial Information ◽

Brain Activity ◽

Meta Analysis ◽

Likelihood Estimation ◽

Resting State Fmri ◽

Brain Regions ◽

Anterior Cingulate ◽

Diagnostic Classification Models ◽

Spontaneous Brain Activity

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features, for example for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD to serve such feature selection and as a secondary aim to improve understanding of disease mechanisms. 32 studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Results were compared with established resting state networks (RSNs) and spatial representations of recently introduced temporally independent functional modes (TFMs) of spontaneous brain activity. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/ hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components associated with self-referential processing and the subgenual anterior cingulate cortex) with lateral frontal areas related to externally-directed cognition. One particular TFM seems to better comprehend the findings than classical RSNs. Alterations that can be captured by resting state fMRI show considerable overlap with those identifiable with other neuroimaging modalities though differing in some aspects.

Download Full-text