Architecture and anatomy of executive processes: evidence from verbal fluency and Trail Making Test in 2009 stroke patients.

OBJECTIVES: The few voxel-wise lesion-symptom mapping (VLSM) studies aimed at identifying the anatomy of executive function are limited by the absence of a model and by small populations. Using Trail Making Test (TMT) and verbal fluency and a model of their architectures, our objective was to identify the key structures underlying two major executive processes, set-shifting and strategic word search. METHODS: We applied a validated VLSM analysis to harmonized cognitive and imaging data from 2009 ischemic stroke patients as a part of the Meta VCI Map consortium. All contrast analyses used an adjusted threshold with 2000 Freedman-Lane permutations (p ≤ 0.05). RESULTS: The TMT parts A and B were associated with structures involved in visual-spatial processing, the motor system, the frontal lobes, and their subcortical connections. Set-shifting depended on the left dorsomedial frontal region. Both semantic and phonemic fluency tests depended on verbal output abilities and processing speed with similar slopes in different languages. The strategic search process depended on Broca's area, F2 and related tracts, temporal and deep regions. Lastly, the lesion map of set-shifting did not overlap with those of strategic word search processes. INTERPRETATION: Our results identify the anatomical substrates of two main executive processes, revealing that they represent only a specific subpart of previously reported structures. Finally, our results indicate that executive functions depend on several specific, anatomically separable executive processes mainly operating in various parts of the frontal lobes.

Cognitive trajectory in the first year after first-ever ischaemic stroke in young adults: the ODYSSEY study.

BACKGROUND: Limited data exists on cognitive recovery in young stroke patients. We aimed to investigate the longitudinal course of cognitive performance during the first year after stroke at young age and identify predictors for cognitive recovery. METHODS: We conducted a multicentre prospective cohort study between 2013 and 2021, enrolling patients aged 18-49 years with first-ever ischaemic stroke. Cognitive assessments were performed within 6 months and after 1 year following the index event, covering seven cognitive domains. Composite Z-scores using normative data determined cognitive impairment (Z-score<-1.5). A Reliable Change Index (RCI) assessed cognitive recovery (RCI>1.96) or decline (RCI<-1.96). RESULTS: 393 patients (median age 44.3 years, IQR 38.4-47.2) completed cognitive assessments with a median time interval of 403 days (IQR 364-474) between assessments. Based on RCI, a similar proportion of patients showed improvement and decline in each cognitive domain, while the majority exhibited no cognitive change. Among cognitively impaired patients at baseline, improvements were observed in processing speed (23.1%), visuoconstruction (40.1%) and executive functioning (20.0%). Younger age was associated with better cognitive recovery in visuoconstruction, and larger lesion volume was related to cognitive recovery in processing speed. No other predictors for cognitive recovery were identified. CONCLUSIONS: Cognitive impairment remains prevalent in young stroke even 1 year after the event. Most patients showed no cognitive change, however, recovery may have occurred in the early weeks after stroke, which was not assessed in our study. Among initially cognitively impaired patients, cognitive recovery is observed in processing speed, visuoconstruction and executive functioning. It is still not possible to predict cognitive recovery in individual patients.

White Matter Hyperintensity Volume and Poststroke Cognition: An Individual Patient Data Pooled Analysis of 9 Ischemic Stroke Cohort Studies.

BACKGROUND: White matter hyperintensities (WMH) are associated with cognitive dysfunction after ischemic stroke. Yet, uncertainty remains about affected domains, the role of other preexisting brain injury, and infarct types in the relation between WMH burden and poststroke cognition. We aimed to disentangle these factors in a large sample of patients with ischemic stroke from different cohorts. METHODS: We pooled and harmonized individual patient data (n=1568) from 9 cohorts, through the Meta VCI Map consortium (www.metavcimap.org). Included cohorts comprised patients with available magnetic resonance imaging and multidomain cognitive assessment <15 months poststroke. In this individual patient data meta-analysis, linear mixed models were used to determine the association between WMH volume and domain-specific cognitive functioning (Z scores; attention and executive functioning, processing speed, language and verbal memory) for the total sample and stratified by infarct type. Preexisting brain injury was accounted for in the multivariable models and all analyses were corrected for the study site as a random effect. RESULTS: In the total sample (67 years [SD, 11.5], 40% female), we found a dose-dependent inverse relationship between WMH volume and poststroke cognitive functioning across all 4 cognitive domains (coefficients ranging from -0.09 [SE, 0.04, P=0.01] for verbal memory to -0.19 [SE, 0.03, P<0.001] for attention and executive functioning). This relation was independent of acute infarct volume and the presence of lacunes and old infarcts. In stratified analyses, the relation between WMH volume and domain-specific functioning was also largely independent of infarct type. CONCLUSIONS: In patients with ischemic stroke, increasing WMH volume is independently associated with worse cognitive functioning across all major domains, regardless of old ischemic lesions and infarct type.

Sex Differences in Poststroke Cognitive Impairment: A Multicenter Study in 2343 Patients With Acute Ischemic Stroke.

BACKGROUND: Poststroke cognitive impairment (PSCI) occurs in about half of stroke survivors. Cumulative evidence indicates that functional outcomes of stroke are worse in women than men. Yet it is unknown whether the occurrence and characteristics of PSCI differ between men and women. METHODS: Individual patient data from 9 cohorts of patients with ischemic stroke were harmonized and pooled through the Meta-VCI-Map consortium (n=2343, 38% women). We included patients with visible symptomatic infarcts on computed tomography/magnetic resonance imaging and cognitive assessment within 15 months after stroke. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment. Logistic regression analyses were performed to compare men to women, adjusted for study cohort, to obtain odds ratios for PSCI and individual cognitive domains. We also explored sensitivity and specificity of cognitive screening tools for detecting PSCI, according to sex (Mini-Mental State Examination, 4 cohorts, n=1814; Montreal Cognitive Assessment, 3 cohorts, n=278). RESULTS: PSCI was found in 51% of both women and men. Men had a lower risk of impairment of attention and executive functioning (men: odds ratio, 0.76 [95% CI, 0.61-0.96]), and language (men: odds ratio, 0.67 [95% CI, 0.45-0.85]), but a higher risk of verbal memory impairment (men: odds ratio, 1.43 [95% CI, 1.17-1.75]). The sensitivity of Mini-Mental State Examination (<25) for PSCI was higher for women (0.53) than for men (0.27; P=0.02), with a lower specificity for women (0.80) than men (0.96; P=0.01). Sensitivity and specificity of Montreal Cognitive Assessment (<26.) for PSCI was comparable between women and men (0.91 versus 0.86; P=0.62 and 0.29 versus 0.28; P=0.86, respectively). CONCLUSIONS: Sex was not associated with PSCI occurrence but affected domains differed between men and women. The latter may explain why sensitivity of the Mini-Mental State Examination for detecting PSCI was higher in women with a lower specificity compared with men. These sex differences need to be considered when screening for and diagnosing PSCI in clinical practice.

Trigger Factors for Stroke in Young Adults: A Case-Crossover Study.

BACKGROUND AND OBJECTIVES: Causes of stroke in young adults differ from those in the elderly individuals, and in a larger percentage, no cause can be determined. To gain more insight into the etiology of (cryptogenic) stroke in the young population, we investigated whether trigger factors, such as short-lasting exposure to toxins or infection, may play a role. METHODS: Patients aged 18-49 years with a first-ever ischemic stroke or intracerebral hemorrhage (ICH) in 17 participating centers in the Netherlands completed a questionnaire about exposure to 9 potential trigger factors in hazard periods and on a regular yearly basis. A case-crossover design was used to assess relative risks (RRs) with 95% confidence intervals (95% CIs) by the Mantel-Haenszel case-crossover method, for any stroke (ischemic stroke and ICH combined) and for different etiologic subgroups of ischemic stroke. RESULTS: One thousand one hundred forty-six patients completed the questionnaire (1,043 patients with an ischemic stroke and 103 with an ICH, median age 44.0 years, 52.6% men). For any stroke, an increased risk emerged within 1 hour of cola consumption (RR 2.0, 95% CI 1.5-2.8) and vigorous physical exercise (RR 2.6, 95% CI 2.2-3.0), within 2 hours after sexual activity (RR 2.4, 95% CI 1.6-3.5), within 4 hours after illicit drug use (RR 2.8, 95% CI 1.7-4.9), and within 24 hours after fever or flu-like disease (RR 14.1, 95% CI 10.5-31.2; RR 13.9, 95% CI 8.9-21.9). Four trigger factors increased the risk of other determined and cryptogenic ischemic stroke, 3 that of cardioembolic stroke, 2 that of large vessel atherosclerosis and likely atherothrombotic stroke combined and stroke with multiple causes, and none that of stroke due to small vessel disease. DISCUSSION: We identified cola consumption, vigorous physical exercise, sexual activity, illicit drug use, fever, and flu-like disease as potential trigger factors for stroke in the young population and found differences in the type and number of trigger factors associated with different etiologic subgroups of ischemic stroke. These findings might help in better understanding the pathophysiologic mechanisms of (cryptogenic) stroke in the young population.

Risk Factors and Causes of Ischemic Stroke in 1322 Young Adults.

BACKGROUND: Identification of risk factors and causes of stroke is key to optimize treatment and prevent recurrence. Up to one-third of young patients with stroke have a cryptogenic stroke according to current classification systems (Trial of ORG 10172 in Acute Stroke Treatment [TOAST] and atherosclerosis, small vessel disease, cardiac pathology, other causes, dissection [ASCOD]). The aim was to identify risk factors and leads for (new) causes of cryptogenic ischemic stroke in young adults, using the pediatric classification system from the IPSS study (International Pediatric Stroke Study). METHODS: This is a multicenter prospective cohort study conducted in 17 hospitals in the Netherlands, consisting of 1322 patients aged 18 to 49 years with first-ever, imaging confirmed, ischemic stroke between 2013 and 2021. The main outcome was distribution of risk factors according to IPSS classification in patients with cryptogenic and noncryptogenic stroke according to the TOAST and ASCOD classification. RESULTS: The median age was 44.2 years, and 697 (52.7%) were men. Of these 1322 patients, 333 (25.2%) had a cryptogenic stroke according to the TOAST classification. Additional classification using the ASCOD criteria reduced the number patients with cryptogenic stroke from 333 to 260 (19.7%). When risk factors according to the IPSS were taken into account, the number of patients with no potential cause or risk factor for stroke reduced to 10 (0.8%). CONCLUSIONS: Among young adults aged 18 to 49 years with a cryptogenic ischemic stroke according to the TOAST classification, risk factors for stroke are highly prevalent. Using a pediatric classification system provides new leads for the possible causes in cryptogenic stroke, and could potentially lead to more tailored treatment for young individuals with stroke.

Network impact score is an independent predictor of post-stroke cognitive impairment: A multicenter cohort study in 2341 patients with acute ischemic stroke.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. AIMS: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. METHODS: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3-12, 12-24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. RESULTS: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3-12 months, 243/853 (28%) at 12-24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34-1.68) and multivariable (OR 1.27, 95%CI 1.10-1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. CONCLUSIONS: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

Improvements in Endovascular Treatment for Acute Ischemic Stroke: A Longitudinal Study in the MR CLEAN Registry.

BACKGROUND: We evaluated data from all patients in the Netherlands who underwent endovascular treatment for acute ischemic stroke in the past 3.5 years, to identify nationwide trends in time to treatment and procedural success, and assess their effect on clinical outcomes. METHODS: We included patients with proximal occlusions of the anterior circulation from the second and first cohorts of the MR CLEAN (Multicenter Randomized Clinical trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry (March 2014 to June 2016; June 2016 to November 2017, respectively). We compared workflow times and rates of successful reperfusion (defined as an extended Thrombolysis in Cerebral Infarction score of 2B-3) between cohorts and chronological quartiles (all included patients stratified in chronological quartiles of intervention dates to create equally sized groups over the study period). Multivariable ordinal logistic regression was used to assess differences in the primary outcome (ordinal modified Rankin Scale at 90 days). RESULTS: Baseline characteristics were similar between cohorts (second cohort n=1692, first cohort n=1488) except for higher age, poorer collaterals, and less signs of early ischemia on computed tomography in the second cohort. Time from stroke onset to groin puncture and reperfusion were shorter in the second cohort (median 185 versus 210 minutes; P<0.001 and 236 versus 270 minutes; P<0.001, respectively). Successful reperfusion was achieved more often in the second than in the first cohort (72% versus 66%; P<0.001). Functional outcome significantly improved (adjusted common odds ratio 1.23 [95% CI, 1.07-1.40]). This effect was attenuated by adjustment for time from onset to reperfusion (adjusted common odds ratio, 1.12 [95% CI, 0.98-1.28]) and successful reperfusion (adjusted common odds ratio, 1.13 [95% CI, 0.99-1.30]). Outcomes were consistent in the analysis per chronological quartile. CONCLUSIONS: Clinical outcomes after endovascular treatment for acute ischemic stroke in routine clinical practice have improved over the past years, likely resulting from improved workflow times and higher successful reperfusion rates.

Temporal evolution and predictors of subjective cognitive complaints up to 4 years after stroke.

OBJECTIVE: To examine the temporal evolution of subjective cognitive complaints in the long-term after stroke, and to identify predictors of long-term subjective cognitive complaints. METHODS: Prospective cohort study including 395 stroke patients. Subjective cognitive complaints were assessed at 2 months, 6 months and 4 years post-stroke, using the Checklist for Cognitive and Emotional consequences following stroke (CLCE-24). The temporal evolution of subjective cognitive complaints was described using multilevel growth modelling. Associations between CLCE-24 cognition score at 4 years post-stroke and baseline characteristics, depression, anxiety, cognitive test performance, and adaptive and maladaptive psychological factors were examined. Significant predictors were entered in a multivariate multilevel model. RESULTS: A significant increase in subjective cognitive complaints from 2 months up to 4 years (mean 3.7 years, standard deviation (SD) 0.6 years) post-stroke was observed (p≤0.001). Two months post-stroke, 76% of patients reported at least one cognitive complaint, 72% at 6 months, and 89% at 4 years post-stroke. A higher level of subjective cognitive complaints at 2 months and lower scores on adaptive and maladaptive psychological factors were significant independent predictors of a higher level of subjective cognitive complaints at 4 years post-stroke. CONCLUSION: Post-stroke subjective cognitive complaints increase over time and can be predicted by the extent of subjective cognitive complaints and the presence of adaptive and maladaptive psychological factors in the early phases after stroke.

Prediction of Cognitive Recovery After Stroke: The Value of Diffusion-Weighted Imaging-Based Measures of Brain Connectivity.

BACKGROUND AND PURPOSE: Prediction of long-term recovery of a poststroke cognitive disorder (PSCD) is currently inaccurate. We assessed whether diffusion-weighted imaging (DWI)-based measures of brain connectivity predict cognitive recovery 1 year after stroke in patients with PSCD in addition to conventional clinical, neuropsychological, and imaging variables. METHODS: This prospective monocenter cohort study included 217 consecutive patients with a clinical diagnosis of ischemic stroke, aged ≥50 years, and Montreal Cognitive Assessment score below 26 during hospitalization. Five weeks after stroke, patients underwent DWI magnetic resonance imaging. Neuropsychological assessment was performed 5 weeks and 1 year after stroke and was used to classify PSCD as absent, modest, or marked. Cognitive recovery was operationalized as a shift to a better PSCD category over time. We evaluated 4 DWI-based measures of brain connectivity: global network efficiency and mean connectivity strength, both weighted for mean diffusivity and fractional anisotropy. Conventional predictors were age, sex, level of education, clinical stroke characteristics, neuropsychological variables, and magnetic resonance imaging findings (eg, infarct size). DWI-based measures of brain connectivity were added to a multivariable model to assess additive predictive value. RESULTS: Of 135 patients (mean age, 71 years; 95 men [70%]) with PSCD 5 weeks after ischemic stroke, 41 (30%) showed cognitive recovery. Three of 4 brain connectivity measures met the predefined threshold of P<0.1 in univariable regression analysis. There was no added value of these measures to a multivariable model that included level of education and infarct size as significant predictors of cognitive recovery. CONCLUSIONS: Current DWI-based measures of brain connectivity appear to predict recovery of PSCD but at present have no added value over conventional predictors.

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts.

BACKGROUND: Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model. METHODS: In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal-external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa. FINDINGS: In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88-0·92), inter-rater agreement (0·85-0·87), and intra-rater agreement (for a single rater, 0·95) were all high. INTERPRETATION: To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI. FUNDING: The Netherlands Organisation for Health Research and Development.

The relationship between social cognition and participation in the long term after stroke.

Social cognitive impairments may play a role in participation restrictions after stroke. Understanding their relationship could inform treatment approaches to improve participation. We investigated the relationship between social cognition and participation in the long term after stroke. Of 395 patients participating in a large prospective cohort study, cross-sectional data were available at 3-4 years post-stroke of 118 patients on tests for emotion recognition, theory of mind, empathy, and behaviour regulation. Participation was assessed with the Utrecht Scale for Evaluation of Rehabilitation - Participation (USER-P). Bivariate and multivariate regression analysis were used to examine the relationship between social cognitive domains and participation. The majority suffered from minor stroke (83.1% scored NIHSS 0-4). Only behaviour regulation was related to participation restrictions in bivariate analysis, but social cognitive impairments did not predict participation restrictions in multivariate regression in this group. To conclude, in a sample of minor stroke patients with mild impairments in theory of mind, emotion recognition and behavioural control, there were no associations with restrictions in participation. Research should examine whether a relationship is present in patients with more severe stroke. In addition, measuring social aspects of participation is necessary to further unravel this relationship, to determine treatment targets for improving participation.

Course and Predictors of Subjective Cognitive Complaints During the First 12 Months after Stroke.

BACKGROUND: Subjective Cognitive Complaints (SCC) are common after stroke. This study documents the prevalence and course of SCC in the first year after stroke and determines which patient characteristics in the first 3 months predict subsequent SCC at 1-year follow-up. METHODS: Using a longitudinal design, 155 patients (mean age 64.0 ± 11.9 years; 69.7% men) were assessed at 3 and 12 months after stroke. SCC were assessed using the Checklist for Cognitive and Emotional consequences following stroke (CLCE) inventory (content component [CLCE-c] and worry component [CLCE-w]). Potential predictors of 12 months SCC included demographics, stroke severity, objective cognitive impairment, psychological factors (depression, anxiety, perceived stress, fatigue, personality traits, coping style), and activities of daily life functioning assessed at 3 months poststroke. Multiple hierarchical linear regression analyses were used to determine predictors of SCC at 12 months poststroke. RESULTS: SCC remained stable from 3 to 12 months over time (CLCE-c from 3.3 ± 2.4 to 3.3 ± 2.6; CLCE-w: from 1.9 ± 2.2 to 2.1 2.5). Independent predictors of SCC at 12 months were baseline CLCE-c (ß = 0.54) and perceived stress (ß = 0.23) for content, and baseline CLCE-w (ß = 0.57) and depressive symptoms (ß = 0.23) for worry. CONCLUSIONS: Patients who report SCC at 3 months after stroke are likely to continue having these complaints at 1 year follow-up. Perceived stress and depressive symptoms additionally increase the likelihood of having SCC at 12 months, independent of SCC at 3 months poststroke. Rehabilitation programs that target reduction of stress and depression in the first months after stroke might reduce sustained SCC and improve well-being.

Extent to Which Network Hubs Are Affected by Ischemic Stroke Predicts Cognitive Recovery.

Background and Purpose- It is uncertain what determines the potential for cognitive recovery after ischemic stroke. The extent to which strategic areas of the brain network, so-called hubs, are affected by the infarct could be a key factor. We developed a lesion impact score, which estimates the damage to network hubs by integrating information on infarct size with healthy brain network topology. We verified whether the lesion impact score indeed reflects global network disturbances in patients and assessed if it could predict cognitive recovery. Methods- Seventy-five ischemic stroke patients without signs of a prestroke cognitive disorder were included, all with evidence of a cognitive disorder during hospitalization. A brain magnetic resonance imaging and neuropsychological assessment were performed 5 weeks (±1 week) after stroke. Neuropsychological testing was repeated after 1 year to assess cognitive recovery. Brain networks were reconstructed from diffusion-weighted data and consisted of 90 gray matter regions (ie, network nodes). A standard brain network map, indicating the hub-score of each node, was obtained from network data of 44 cognitively healthy adults. For each patient, we calculated the lesion impact score by multiplying the percentage of node volume affected by the infarct with the node's corresponding hub-score. The patients' maximum lesion impact score was used as outcome predictor. Results- A higher lesion impact score in patients, indicating an increasing infarct size in nodes with a higher hub-score, was related to lower global brain network efficiency (ß=-0.528 [-0.776 to -0.277]; P<0.001), independent of age, brain volume, infarct volume, and white matter hyperintensity severity. A lower lesion impact score, however, was an independent predictor of cognitive recovery 1 year after stroke (odds ratio=0.434 [0.193-0.978]; P=0.044). Conclusions- We introduced a lesion impact score that combines information on infarct size and network topology to predict long-term recovery after stroke. This score can potentially be used in a clinical setting, also without availability of high-resolution diffusion-weighted magnetic resonance imaging.

The Meta VCI Map consortium for meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping: Design and multicenter pilot study.

INTRODUCTION: The Meta VCI Map consortium performs meta-analyses on strategic lesion locations for vascular cognitive impairment using lesion-symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion-symptom mapping studies. METHODS: Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage. RESULTS: Forty-seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population-based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage. DISCUSSION: Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

Social cognition impairments are associated with behavioural changes in the long term after stroke.

BACKGROUND AND PURPOSE: Behavioural changes after stroke might be explained by social cognition impairments. The aim of the present study was to investigate whether performances on social cognition tests (including emotion recognition, Theory of Mind (ToM), empathy and behaviour regulation) were associated with behavioural deficits (as measured by proxy ratings) in a group of patients with relatively mild stroke. METHODS: Prospective cohort study in which 119 patients underwent neuropsychological assessment with tests for social cognition (emotion recognition, ToM, empathy, and behaviour regulation) 3-4 years post stroke. Test scores were compared with scores of 50 healthy controls. Behavioural problems were assessed with the Dysexecutive Questionnaire (DEX) self rating and proxy rating scales. Pearson correlations were used to determine the relationship between the social cognition measures and DEX scores. RESULTS: Patients performed significantly worse on emotion recognition, ToM and behaviour regulation tests than controls. Mean DEX-self score did not differ significantly from the mean DEX-proxy score. DEX-proxy ratings correlated with tests for emotion recognition, empathy, and behavioural regulation (lower scores on these items were associated with more problems on the DEX-proxy scale). CONCLUSIONS: Social cognition impairments are present in the long term after stroke, even in a group of mildly affected stroke patients. Most of these impairments also turned out to be associated with a broad range of behavioural problems as rated by proxies of the patients. This strengthens the proposal that social cognition impairments are part of the underlying mechanism of behavioural change. Since tests for social cognition can be administered in an early stage, this would allow for timely identification of patients at risk for behavioural problems in the long term.

Psychological factors and subjective cognitive complaints after stroke: Beyond depression and anxiety.

Subjective Cognitive Complaints (SCC) are common after stroke and adversely affect quality of life. In the present study, we determined the associations of depression, anxiety, perceived stress and fatigue with post-stroke SCC, and whether these associations were independent of objective cognitive functioning, stroke characteristics and individual differences in personality traits and coping styles. Using a cross-sectional design, SCC and psychological measures were obtained in 208 patients (mean 3.3 ± 0.5 months after stroke; 65.9% men; mean age 64.9 ± 12.4 years). SCC were assessed using the Checklist for Cognitive and Emotional consequences following stroke (CLCE) inventory. Validated questionnaires were used to measure depression and anxiety (Hospital Anxiety and Depression Scale), perceived stress (Perceived Stress Scale), fatigue (Fatigue Assessment Scale), personality traits (Eysenck Personality Questionnaire Revised Short Scale) and coping style (Utrecht Coping List). Multivariate hierarchical linear regression analyses were used to adjust for covariates. Depression (ß = 0.35), anxiety (ß = 0.38), perceived stress (ß = 0.39), and fatigue (ß = 0.39) were associated with CLCE scores, independent of demographic, cognitive performance and stroke-related covariates. After including personality traits and coping styles in the model, independent associations with CLCE scores were found for fatigue (ß = 0.26, p = .003) and neuroticism (ß = 0.21, p = .05). Interventions aimed at improving psychological resilience and increasing energy levels might be a worthwhile addition to stroke rehabilitation programmes by reducing SCC and improving quality of life.

Predicting the presence of macrovascular causes in non-traumatic intracerebral haemorrhage: the DIAGRAM prediction score.

OBJECTIVE: A substantial part of non-traumatic intracerebral haemorrhages (ICH) arises from a macrovascular cause, but there is little guidance on selection of patients for additional diagnostic work-up. We aimed to develop and externally validate a model for predicting the probability of a macrovascular cause in patients with non-traumatic ICH. METHODS: The DIagnostic AngioGRAphy to find vascular Malformations (DIAGRAM) study (n=298; 69 macrovascular cause; 23%) is a prospective, multicentre study assessing yield and accuracy of CT angiography (CTA), MRI/ magnetic resonance angiography (MRA) and intra-arterial catheter angiography in diagnosing macrovascular causes in patients with non-traumatic ICH. We considered prespecified patient and ICH characteristics in multivariable logistic regression analyses as predictors for a macrovascular cause. We combined independent predictors in a model, which we validated in an external cohort of 173 patients with ICH (78 macrovascular cause, 45%). RESULTS: Independent predictors were younger age, lobar or posterior fossa (vs deep) location of ICH, and absence of small vessel disease (SVD). A model that combined these predictors showed good performance in the development data (c-statistic 0.83; 95% CI 0.78 to 0.88) and moderate performance in external validation (c-statistic 0.66; 95% CI 0.58 to 0.74). When CTA results were added, the c-statistic was excellent (0.91; 95% CI 0.88 to 0.94) and good after external validation (0.88; 95% CI 0.83 to 0.94). Predicted probabilities varied from 1% in patients aged 51-70 years with deep ICH and SVD, to more than 50% in patients aged 18-50 years with lobar or posterior fossa ICH without SVD. CONCLUSION: The DIAGRAM scores help to predict the probability of a macrovascular cause in patients with non-traumatic ICH based on age, ICH location, SVD and CTA.

Advance directives, proxy opinions, and treatment restrictions in patients with severe stroke.

BACKGROUND: Patients with severe stroke often do not have the capacity to participate in discussions on treatment restrictions because of a reduced level of consciousness, aphasia, or another cognitive disorder. We assessed the role of advance directives and proxy opinions in the decision-making process of incapacitated patients. METHODS: Sixty patients with severe functional dependence (Barthel Index ≤6) at day four after ischemic stroke or intracerebral hemorrhage were included in a prospective two-center cohort study. The decision-making process with respect to treatment restrictions was assessed by means of a semi-structured questionnaire administered to the treating physician at the day of inclusion. RESULTS: Forty-nine patients (82%) did not have the capacity to participate in the decision-making process. In eight patients, there was no discussion on treatment restrictions and full care was installed. In 41 patients, the decision whether to install treatment restrictions was discussed with proxies. One patient had a written advance directive. In the remaining 40 patients, proxies based their opinion on previously expressed wishes of the patient (18 patients) or advised in the best interest of the patient (22 patients). In 36 of 41 patients, treatment restrictions were installed after agreement between physician and proxy. At six months, 23 of 49 patients had survived. In only three of them the decision on treatment restrictions was based on previously expressed wishes. Remarkably, two of these survivors could not recall any of their alleged previously expressed wishes. CONCLUSIONS: Treatment restrictions were installed in the majority of incapacitated patients after stroke. Proxy opinions frequently served as the best way to respect the patients' autonomy, but their accuracy remains unclear.

Predictive accuracy of physicians' estimates of outcome after severe stroke.

INTRODUCTION: End-of-life decisions after stroke should be guided by accurate estimates of the patient's prognosis. We assessed the accuracy of physicians' estimates regarding mortality, functional outcome, and quality of life in patients with severe stroke. METHODS: Treating physicians predicted mortality, functional outcome (modified Rankin scale (mRS)), and quality of life (visual analogue scale (VAS)) at six months in patients with major disabling stroke who had a Barthel Index ≤6 (of 20) at day four. Unfavorable functional outcome was defined as mRS >3, non-satisfactory quality of life as VAS <60. Patients were followed-up at six months after stroke. We compared physicians' estimates with actual outcomes. RESULTS: Sixty patients were included, with a mean age of 72 years. Of fifteen patients who were predicted to die, one actually survived at six months (positive predictive value (PPV), 0.93; 95% CI, 0.66-0.99). Of thirty patients who survived, one was predicted to die (false positive rate (FPR), 0.03; 95%CI 0.00-0.20). Of forty-six patients who were predicted to have an unfavorable outcome, four had a favorable outcome (PPV, 0.93; 95% CI, 0.81-0.98; FPR, 0.30; 95% CI; 0.08-0.65). Prediction of non-satisfactory quality of life was less accurate (PPV, 0.63; 95% CI, 0.26-0.90; FPR, 0.18; 95% CI 0.05-0.44). CONCLUSIONS: In patients with severe stroke, treating physicians' estimation of the risk of mortality or unfavorable functional outcome at six months is relatively inaccurate. Prediction of quality of life is even more imprecise.