The aim of the current study was to explore the whole‐brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long‐term stroke severity. We investigated resting‐state functional MRI‐based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re‐occurring dynamic connectivity configurations were obtained using a sliding window approach and k‐means clustering. We evaluated differences in dynamic patterns between three NIHSS‐stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90‐day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post‐stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three‐level ANOVA: p < .05, post hoc t tests: p < .05, FDR‐corrected). Configuration‐specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = −.68, p = .003, FDR‐corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Background : The ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function. Methods : In a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain's pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length ( L ) was calculated for each connectome. The number of RC regions (N RC ) affected were manually determined using each patient's diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days; poor: mRS>2) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including N RC and L in 'outcome' models, using linear regression and assessing the explained variance (R 2 ). Results : Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences in L between patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R 2 increased between 1.3-and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusion : In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

Background and Purpose: The ability to model long-term functional outcomes after acute ischemic stroke (AIS) represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club (RC) organization, a high capacity backbone system of brain function. Methods: In a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain's pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length (L) was calculated for each connectome. The number of RC regions (NRC) affected were manually determined using each patient's diffusion weighted image (DWI). We investigated differences in L with respect to outcome (modified Rankin Scale score (mRS); 90-days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including NRC and L in 'outcome' models, using linear regression and assessing the explained variance (R 2). Results: Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. Lower L was generally associated with better outcome. Including NRC in the backward selection models of outcome, R 2 increased between 1.3-and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusions: In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.

Our study suggests that chronic BP shows similar WM changes to early SZ, suggesting that extracellular FW increases could be a transient indication of recent psychotic episodes. Since FW increase in SZ has been suggested to be related to neuroinflammation, we theorize that neuroinflammation might be a shared pathology between chronic BP and early SZ.

Background and Purpose To explore the whole-brain dynamic functional network connectivity patterns in acute ischemic stroke (AIS) patients and their relation to stroke severity in the short and long term. Methods We investigated large-scale dynamic functional network connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we established correlation analyses between dynamic connectivity estimates and AIS severity as well as neurological recovery within the first 90 days after stroke. Finally, we built Bayesian hierarchical models to predict acute ischemic stroke severity and examine the inter-relation of dynamic connectivity and clinical measures, with an emphasis on white matter hyperintensity lesion load. Results We identified three distinct dynamic connectivity configurations in the early post-acute stroke phase. More severely affected patients (NIHSS 10-21) spent significantly more time in a highly segregated dynamic connectivity configuration that was characterized by particularly strong connectivity (three-level ANOVA: p<0.05, post hoc t-tests: p<0.05, FDR-corrected for multiple comparisons). Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the acute dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r=-0.68, p<0.05, FDR-corrected). Increasing dwell times, particularly those in a very segregated connectivity configuration, predicted higher acute stroke severity in our Bayesian modelling framework. Conclusions Our findings demonstrate transiently increased segregation between multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first three months post-stroke.

Background: Ischemic stroke (IS) is a leading cause of long-term disability with sex-specific differences in outcomes. Identifying the influential factors that contribute to sex-specific disparities in stroke outcomes, therefore, holds potential to develop individualized interventions for reducing long-term disability. Further, investigating the association between sex and Patient-Reported Outcome Measures (PROMs) provides additional information on the individual impact and heterogeneity of IS. We aimed to identify sex-specific differences in stroke outcomes and relationship with PROMs in IS patients with 3-month follow-up. Methods: Between February 2017 and February 2020, a total of 410 patients admitted with IS to the Massachusetts General Hospital, in Boston, were enrolled in this prospective cohort. At 3-month poststroke, patients were assessed for Barthel Index, modified Rankin Scale, and PROM-10 questionnaires. T scores for physical and mental health were determined from the summing of PROM-10 responses in each domain. Regression analysis was performed to identify sex-specific determinants of functional and patient-reported outcomes. Results: At baseline, 242 participants were male (mean age, 65 years) and 168 were female (mean age, 70 years). Groups had similar rates of cardiovascular risk factors, admission National Institutes of Health Stroke Scale, and discharge modified Rankin Scale. At follow-up, male participants were more likely to have better rates of T Physical and Barthel Index. In regression analysis, PROMs T Physical (odds ratio, 1.06; P =0.01), Barthel Index (odds ratio, 1.06; P =0.01), and modified Rankin Scale score of ≥2 (odds ratio, 2.60; P =0.01) were associated with female sex. Female sex was also associated with lower scores for PROMs Physical subcomponents and with patient-reported general health and emotional problems. Conclusions: Women have worse outcomes after ischemic stroke, including objective measures of functional disability and patient-reported outcomes. Incorporating PROMs into IS outcome measures may offer additional insight into sex-specific differences in stroke recovery and outcomes.

Introduction: Post-stroke cognitive impairment/dementia (PSCID) is highly prevalent and associated with poor long-term outcomes after acute ischemic stroke (AIS). Recognition of early determinants of PSCID allows for individualized interventions to reduce long-term disability. Furthermore, investigating the relation between PSCID and patient-reported outcomes provides insight into personalized impact of post-stroke cognitive dysfunction. We aimed to identify clinical determinants of PSCID and association with patient-reported outcome measures (PROMs) at 3- and 12-month after AIS. Methods: 138 AIS patients with no previous history of dementia were included. Clinical variables were acquired on hospital admission. Patients underwent a telephone interview at 3- and 12-months post-stroke including the Telephone Interview for Cognitive Status (TICS), modified Rankin scale (mRS), Barthel Index (BI) and PROM-10 questionnaires. PSCID was defined as TICS < 36 at 12-month post-stroke. Linear regression analyses were performed to identify predictors of 12-month TICS score and association with 3- and 12-month PROMs. Results: At 12 months post-stroke, 113 participants (82%) had PSCID. AIS patients with PSCID had higher rates of mRS ≥ 2 at 3-months (61.8% vs. 32%, p = 0.01), worse BI at 12-months (93.12 vs. 102, p < 0.01) and lower 12-month PROMs T Mental scores (48.22 vs. 52.41, p < 0.05). In linear regression analysis, worse functional and patient-reported outcomes at 3- and 12-months were independently associated with lower 12-month TICS (Table) . Conclusions: Large proportion of AIS patients experience PSCID at 12-months after stroke. 3-month PROMs, especially in the physical domain, are independently associated with worse cognitive measures at 12-months post-stroke. Moreover, PROMs at 12-months after AIS are strongly associated with PSCID. Incorporating PROMs in the ambulatory setting may offer additional insight into cognitive impairment after stroke.