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

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <div class="section"> <a class="named-anchor" id="S1"> <!-- named anchor --> </a> <h5 class="section-title" id="d11035854e295">Background and Purpose—</h5> <p id="P2">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. </p> </div><div class="section"> <a class="named-anchor" id="S2"> <!-- named anchor --> </a> <h5 class="section-title" id="d11035854e300">Methods—</h5> <p id="P3">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 &gt;2). </p> </div><div class="section"> <a class="named-anchor" id="S3"> <!-- named anchor --> </a> <h5 class="section-title" id="d11035854e305">Results—</h5> <p id="P4">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. </p> </div><div class="section"> <a class="named-anchor" id="S4"> <!-- named anchor --> </a> <h5 class="section-title" id="d11035854e310">Conclusions—</h5> <p id="P5">These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome. </p> </div>

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          Most cited references23

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          Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect.

          Spatial neglect is a syndrome following stroke manifesting attentional deficits in perceiving and responding to stimuli in the contralesional field. We examined brain network integrity in patients with neglect by measuring coherent fluctuations of fMRI signals (functional connectivity). Connectivity in two largely separate attention networks located in dorsal and ventral frontoparietal areas was assessed at both acute and chronic stages of recovery. Connectivity in the ventral network, part of which directly lesioned, was diffusely disrupted and showed no recovery. In the structurally intact dorsal network, interhemispheric connectivity in posterior parietal cortex was acutely disrupted but fully recovered. This acute disruption, and disrupted connectivity in specific pathways in the ventral network, strongly correlated with impaired attentional processing across subjects. Lastly, disconnection of the white matter tracts connecting frontal and parietal cortices was associated with more severe neglect and more disrupted functional connectivity. These findings support a network view in understanding neglect.
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            Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke.

            Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain-behavior relationships in stroke.
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              Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior.

              The resting brain is not silent, but exhibits organized fluctuations in neuronal activity even in the absence of tasks or stimuli. This intrinsic brain activity persists during task performance and contributes to variability in evoked brain responses. What is unknown is if this intrinsic activity also contributes to variability in behavior. In the current fMRI study, we identify a relationship between human brain activity in the left somatomotor cortex and spontaneous trial-to-trial variability in button press force. We then demonstrate that 74% of this brain-behavior relationship is attributable to ongoing fluctuations in intrinsic activity similar to those observed during resting fixation. In addition to establishing a functional and behavioral significance of intrinsic brain activity, these results lend new insight into the origins of variability in human behavior.
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                Author and article information

                Journal
                Stroke
                Stroke
                Ovid Technologies (Wolters Kluwer Health)
                0039-2499
                1524-4628
                October 2018
                October 2018
                : 49
                : 10
                : 2353-2360
                Affiliations
                [1 ]From the Imaging Research Unit, Department of Radiology (Girona Biomedical Research Institute) Girona Biomedical Research Institute, Diagnostic Imaging Institute (IDI) (J.P., G.B., C.B., M.N.-M., C.L.O.-R., S.P.), Dr Josep Trueta University Hospital, Girona, Spain
                [2 ]Quantitative Imaging Biomarkers In Medicine, La Fe Health Research Institute, La Fe Polytechnics and University Hospital, Valencia, Spain (A.A.-B.)
                [3 ]Neuroimaging and Stroke Recovery Laboratory, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA (G.S.)
                [4 ]Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain (G.D.)
                [5 ]ICREA Institut Catalan de Recerca i Estudis Avançats, Barcelona, Spain (G.D.)
                [6 ]Department of Neurology, Girona Biomedical Research Institute (M.R., J.G., J.S.), Dr Josep Trueta University Hospital, Girona, Spain
                [7 ]Department of Rehabilitation (J.F., C.T.), Dr Josep Trueta University Hospital, Girona, Spain
                [8 ]Department of Computer Science, Applied Mathematics, and Statistics, University of Girona, Spain (P.D.-i.-E.)
                [9 ]Department of Medicine, Centre for Brain Research, University of Auckland, New Zealand (C.M.S.)
                [10 ]Department of Radiology, Weill Cornell Medical College, NY (A.K.)
                [11 ]Department of Psychiatry, Bellvitge University Hospital-Instituto de Investigación Biomédica de Bellvitge, Hospitalet del Llobregat, Barcelona, Spain (C.S.-M.)
                [12 ]Centro de Investigación en Salud Mental, Barcelona, Spain (C.S.-M.)
                [13 ]Department of Psychobiology and Methodology in Health Sciences, Universitat Autonoma de Barcelona, Spain (C.S.-M.)
                [14 ]Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany (G.T.)
                [15 ]Department of Radiology, University of Manitoba, Winnipeg, Canada (M.E., C.R.F.)
                [16 ]Departments of Clinical Neurosciences and Radiology, Hotchkiss Brain Institute, University of Calgary, Alberta, Canada (B.M., A.D.)
                [17 ]Department of Radiology, Icahn School of Medicine at Mount Sinai, NY (K.N.)
                [18 ]Neuroradiology Division, Department of Radiology, Stanford University, Palo Alto, CA (M.W.)
                [19 ]Neurovascular Imaging Research Core and University of California Los Angeles Stroke Center, Los Angeles, CA (D.S.L.).
                Article
                10.1161/STROKEAHA.118.021319
                6645916
                30355087
                44cc337b-255c-41c1-9240-051cdb545567
                © 2018
                History

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