Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Abstract

Background and Purpose— Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods— We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results— Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions— These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

Interhemispheric Functional Connectivity in the Primary Motor Cortex Assessed by Resting-State Functional Magnetic Resonance Imaging Aids Long-Term Recovery Prediction among Subacute Stroke Patients with Severe Hand Weakness

“…In contrast to PSD, functional connectivity (FC) analysis is another approach to measure the resting-state intrinsic neuronal activity in the time domain using rsfMRI. Changes in FC value in the interhemispheric motor cortex (M1) after stroke are reportedly reflective of long-term recovery, and patients with good functional outcomes have greater FC values than patients with poor outcomes [7][8][9]. However, a recent study reported that differences in FC value in the interhemispheric M1 did not change over time with recovery [10].…”

Predicting response to motor therapy in chronic stroke patients using Machine Learning

“…One limitation of this study is that we had only the patient's structural T1 scans. It may be more informative to have access to stroke individual's diffusion or functional MRI, which have been shown to be important for extracting biomarkers that can predict impairment and recovery after stroke (Stinear et al, 2007;Siegel et al, 2016;Puig et al, 2018). One recent study showed increased baseline functional connectivity in certain regions in stroke patients who recovered better than those that did not (Puig et al, 2018).…”

“…It may be more informative to have access to stroke individual's diffusion or functional MRI, which have been shown to be important for extracting biomarkers that can predict impairment and recovery after stroke (Stinear et al, 2007;Siegel et al, 2016;Puig et al, 2018). One recent study showed increased baseline functional connectivity in certain regions in stroke patients who recovered better than those that did not (Puig et al, 2018). Other studies have shown biomarkers of structural white matter integrity, particularly in the motor tracts, are predictive of recovery (Stinear et al, 2007;Lindenberg et al, 2012;Rüber et al, 2012).…”

“…One study in particular showed baseline metrics of the functional connectome and changes in the functional connectome from the acute to subacute stage varied between groups with different severities of impairment (Lee et al, 2017). Another study showed increased baseline functional connectivity in certain regions in stroke patients who recovered better than those that did not (Puig et al, 2018). Measures of structural connectome disruption have also been found to be predictive of post-stroke impairment and motor recovery.…”

Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club

“…Dynamic (time-varying) functional connectivity has been explored across the lifespan (Nomi et al, 2017), across different states of consciousness (Deco et al, 2017b;Escrichs et al, 2019;Lord et al, 2019), in patients with brain disorders (Puig et al, 2018), and during healthy aging (Tian et al, 2018;Nobukawa et al, 2019). One study that evaluated resting-state fMRI data from 250 subjects to examine patterns of resting-state functional connectivity over time found that dynamic connectivity patterns are consistent across groups (Abrol et al, 2016).…”