Longitudinal Effects of Lesions on Functional Networks after Stroke

While ischemic stroke reflects focal damage determined by the affected vascular territory, clinical symptoms are often more complex and may be better explained by additional indirect effects of the focal lesion. Assumed to be structurally underpinned by anatomical connections, supporting evidence has been found using alterations in the functional connectivity of resting-state functional magnetic resonance imaging (fMRI) data in both sensorimotor and attention networks. To assess the generalizability of this phenomenon in a stroke population with heterogeneous lesions, we investigated the distal effects of lesions on a global level. Longitudinal resting-state fMRI scans were acquired at three consecutive time points, beginning during the acute phase (days 1, 7, and 90 post-stroke) in 12 patients after ischemic stroke. We found a preferential functional change in affected networks (i.e., networks containing lesions changed more during recovery when compared with unaffected networks). This change in connectivity was significantly correlated with clinical changes assessed with the National Institute of Health Stroke Scale. Our results provide evidence that the functional architecture of large-scale networks is critical to understanding the clinical effect and trajectory of post-stroke recovery.

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Post-stroke fatigue is linked to resting state posterior hypoactivity and prefrontal hyperactivity

10.1101/2021.03.17.21253777 ◽

2021 ◽

Author(s):

Georgia Mary Cotter ◽

Mohamed Salah Khlif ◽

Laura Bird ◽

Mark E Howard ◽

Amy Brodtmann ◽

...

Keyword(s):

Ischaemic Stroke ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Low Frequency ◽

Resting State Fmri ◽

Stroke Severity ◽

Stroke Survivors ◽

Whole Brain ◽

Post Stroke

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.

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LncRNAs a New Target for Post-Stroke Recovery

Current Pharmaceutical Design ◽

10.2174/1381612826666200225141414 ◽

2020 ◽

Vol 26 (26) ◽

pp. 3115-3121

Author(s):

Jun Yang ◽

Jingjing Zhao ◽

Xu Liu ◽

Ruixia Zhu

Keyword(s):

Ischemic Stroke ◽

Microglial Activation ◽

Vital Role ◽

Stroke Recovery ◽

Biological Processes ◽

Current Development ◽

Post Stroke ◽

Cascade Processes ◽

Non Coding Rnas ◽

Neuron Injury

LncRNAs (long non-coding RNAs) are endogenous molecules, involved in complicated biological processes. Increasing evidence has shown that lncRNAs play a vital role in the post-stroke pathophysiology. Furthermore, several lncRNAs were reported to mediate ischemia cascade processes include apoptosis, bloodbrain barier breakdown, angiogenesis, microglial activation induced neuroinflammation which can cause neuron injury and influence neuron recovery after ischemic stroke. In our study, we first summarize current development about lncRNAs and post-stroke, focus on the regulatory roles of lncRNAs on pathophysiology after stroke. We also reviewed genetic variation in lncRNA associated with functional outcome after ischemic stroke. Additionally, lncRNA-based therapeutics offer promising strategies to decrease brain damage and promote neurological recovery following ischemic stroke. We believe that lncRNAs will become promising for the frontier strategies for IS and can open up a new path for the treatment of IS in the future.

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Abstract W P29: Tissue Plasminogen Activator Mediated Reperfusion, and Subsequent Improvement in Resting State Connectivity Correlates with Functional Outcome in Acute Ischemic Stroke Patients

Stroke ◽

10.1161/str.46.suppl_1.wp29 ◽

2015 ◽

Vol 46 (suppl_1) ◽

Author(s):

Souvik Sen ◽

Johann Fridriksson ◽

Taylor Hanayik ◽

Christopher Rorden ◽

Isabel Hubbard ◽

...

Keyword(s):

Ischemic Stroke ◽

Functional Connectivity ◽

Functional Outcome ◽

Resting State ◽

Network Connectivity ◽

Stroke Patients ◽

Post Stroke ◽

Resting State Network ◽

Tissue Plasminogen

Background: Intravenous Tissue Plasminogen Activator (TPA) is the only FDA approved medical therapy for acute ischemic stroke (AIS). Prior study suggests that early recanalization is associated with better stroke outcome. Our aim was to correlate task-negative and task-positive (TN/TP) resting state network activity with tissue perfusion and functional outcome, in stroke patients who received TPA. Method: AIS patients were consented and underwent NIH stroke scale (NIHSS) assessment and magnetic resonance imaging (MRI) scans during TPA infusion (baseline) and six hours post stroke. The MRI sequences include contrast-enhanced perfusion weighted image (PWI) and resting state Blood Oxygen Level-Dependent or BOLD (RSB) images acquired using a Siemens Treo 3T MRI scanner. Additionally, the RSB scan and the NIHSS were obtained at a 30-day follow up visit. Results: Fourteen patients (mean age ± SD=63 ±14, 50% male, 50% white, 43% black and 7% others) who qualified for TPA completed the study at baseline and 6 hours post stroke. Of these, 6 patients had valid follow up data at 30 days. Three patients without cerebral ischemia were excluded. A paired samples t-test comparing baseline and 6h post stroke showed a significantly improved TP network t(10)= -4.24 p< 0.05. The resting network connectivity improved from 6 hours post stroke to 30-days follow up, t(5)= -5.35 p< 0.01. Similarly, NIHSS, at 6h post stroke t(10)= 3.62 p< 0.01 and at 30-days follow up t(5)= -3.4 p< 0.01 were significantly better than the NIHSS at baseline. The 6-hours post-stroke perfusion correlated with the resting network connectivity in both the damaged (r=-0.56 p= 0.07) and intact hemispheres (r= -0.57 p= 0.06). Differences in functional connectivity and NIHSS scores from baseline to 6 h were positively correlated (r= 0.56 p=0.07). Conclusion: In this pilot study we found that TPA led to changes in MRI based resting state networks and associated functional outcome. Correlations were found between perfusion, functional connectivity and NIHSS. This suggests that the improvement of resting state network means improved efficiency of brain activity indicated by functional outcome and may be a potential predictive MRI biomarker for TPA response. A larger study is needed to verify this finding.

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Organization of Propagated Intrinsic Brain Activity in Individual Humans

Cerebral Cortex ◽

10.1093/cercor/bhz198 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1716-1734 ◽

Cited By ~ 10

Author(s):

Ryan V Raut ◽

Anish Mitra ◽

Scott Marek ◽

Mario Ortega ◽

Abraham Z Snyder ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Cortical Excitability ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Organization ◽

Individual Level ◽

Slow Activity ◽

Lag Structure

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

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Infarct topography and functional outcomes

Journal of Cerebral Blood Flow & Metabolism ◽

10.1177/0271678x17700666 ◽

2017 ◽

Vol 38 (9) ◽

pp. 1517-1532 ◽

Cited By ~ 7

Author(s):

Mark R Etherton ◽

Natalia S Rost ◽

Ona Wu

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Functional Outcomes ◽

Brain Regions ◽

Stroke Recovery ◽

Acute Ischemia ◽

Post Stroke ◽

Neurologic Outcomes ◽

Recovery Potential

Acute ischemic stroke represents a major cause of long-term adult disability. Accurate prognostication of post-stroke functional outcomes is invaluable in guiding patient care, targeting early rehabilitation efforts, selecting patients for clinical research, and conveying realistic expectations to families. The involvement of specific brain regions by acute ischemia can alter post-stroke recovery potential. Understanding the influences of infarct topography on neurologic outcomes holds significant promise in prognosis of functional recovery. In this review, we discuss the recent evidence of the contribution of infarct location to patient management decisions and functional outcomes after acute ischemic stroke.

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Large-scale DCMs for resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00015 ◽

2017 ◽

Vol 1 (3) ◽

pp. 222-241 ◽

Cited By ~ 63

Author(s):

Adeel Razi ◽

Mohamed L. Seghier ◽

Yuan Zhou ◽

Peter McColgan ◽

Peter Zeidman ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Effective Connectivity ◽

Directed Graphs ◽

Resting State Fmri ◽

Causal Modeling ◽

Large Graphs ◽

Model Inversion ◽

Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

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Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease

Psychological Medicine ◽

10.1017/s0033291714000579 ◽

2014 ◽

Vol 44 (15) ◽

pp. 3341-3356 ◽

Cited By ~ 30

Author(s):

R. C. Wolf ◽

F. Sambataro ◽

N. Vasic ◽

M. S. Depping ◽

P. A. Thomann ◽

...

Keyword(s):

Huntington's Disease ◽

Huntington’S Disease ◽

Motor Function ◽

Neural Activity ◽

Resting State ◽

Brain Atrophy ◽

Clinical Symptoms ◽

Resting State Fmri ◽

Cognitive Networks ◽

The Impact

Background.Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's ‘resting state’ could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients.Method.Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and ‘biological parametric mapping’ analyses to investigate the impact of atrophy on neural activity.Results.Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition.Conclusions.This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

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Homotopic contralesional excitation suppresses spontaneous circuit repair and global network reconnections following ischemic stroke

10.1101/2021.05.02.442355 ◽

2021 ◽

Author(s):

Annie R Bice ◽

Qingli Xiao ◽

Justin Kong ◽

Ping Yan ◽

Zachary Pollack Rosenthal ◽

...

Keyword(s):

Ischemic Stroke ◽

Resting State ◽

Stroke Recovery ◽

Global Network ◽

Resting State Functional Connectivity ◽

Targeted Interventions ◽

Global Circuit ◽

Excitatory Activity ◽

Transcriptional Changes ◽

Evoked Activity

Understanding circuit-level changes that affect the brain's capacity for plasticity will inform the design of targeted interventions for treating stroke recovery. We combine optogenetic photostimulation with optical neuroimaging to examine how contralesional excitatory activity affects cortical remodeling after stroke in mice. Following photothrombosis of left primary somatosensory forepaw (S1FP) cortex, mice received chronic excitation of right S1FP, a maneuver mimicking the use of the unaffected limb during recovery. Contralesional excitation suppressed perilesional S1FP remapping and was associated with abnormal patterns of evoked activity in the unaffected limb. Contralesional stimulation prevented the restoration of resting-state functional connectivity (RSFC) within the S1FP network, RSFC in several networks functionally-distinct from somatomotor regions, and resulted in persistent limb-use asymmetry. In stimulated mice, perilesional tissue exhibited suppressed transcriptional changes in several genes important for recovery. These results suggest that contralesional excitation impedes local and global circuit reconnection through suppression of several neuroplasticity-related genes after stroke.

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No evidence for motor recovery-related cortical reorganization after stroke using resting-state fMRI

10.1101/681320 ◽

2019 ◽

Author(s):

Meret Branscheidt ◽

Naveed Ejaz ◽

Jing Xu ◽

Mario Widmer ◽

Michelle D. Harran ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Motor Recovery ◽

Cortical Reorganization ◽

Behavioral Recovery ◽

Brain Areas ◽

Data Set ◽

Post Stroke ◽

Cortical Areas ◽

One Year

AbstractCortical reorganization has been suggested as mechanism for recovery after stroke. It has been proposed that a form of cortical reorganization (changes in functional connectivity between brain areas) can be assessed with resting-state fMRI. Here we report the largest longitudinal data-set in terms of overall sessions in 19 patients with subcortical stroke and 11 controls. Patients were imaged up to 5 times over one year. We found no evidence for post-stroke cortical reorganization despite substantial behavioral recovery. These results could be construed as questioning the value of resting-state imaging. Here we argue instead that they are consistent with other emerging reasons to challenge the idea of motor recovery-related cortical reorganization post-stroke when conceived as changes in connectivity between cortical areas.

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Individual Variation in Brain Network Topology Predicts Emotional Intelligence

10.1101/275768 ◽

2018 ◽

Author(s):

Ling George ◽

Lee Ivy ◽

Guimond Synthia ◽

Lutz Olivia ◽

Tandon Neeraj ◽

...

Keyword(s):

Emotional Intelligence ◽

Social Cognition ◽

Network Topology ◽

Resting State ◽

Large Scale ◽

Psychotic Disorders ◽

Brain Connectivity ◽

Distance Matrix ◽

Resting State Fmri ◽

Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

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