Strategic white matter hyperintensity locations associated with post-stroke cognitive impairment: a multicenter study in 1568 stroke patients.

BACKGROUND: Post-stroke cognitive impairment (PSCI) occurs in up to 50% of stroke survivors. Presence of pre-existing vascular brain injury, in particular the extent of white matter hyperintensities (WMH), is associated with worse cognitive outcome after stroke, but the role of WMH location in this association is unclear. AIM: We determined if WMH in strategic white matter tracts explain cognitive performance after stroke. METHODS: Individual patient data from 9 ischemic stroke cohorts with MRI were harmonized through the Meta VCI Map consortium. The association between WMH volumes in strategic tracts and domain-specific cognitive functioning (attention and executive functioning, information processing speed, language and verbal memory) was assessed using linear mixed models and lasso regression. We used a hypothesis-driven design, primarily addressing four white matter tracts known to be strategic in memory clinic patients: the left and right anterior thalamic radiation, forceps major and left inferior fronto-occipital fasciculus. RESULTS: The total study sample consisted of 1568 patients (39.9% female, mean age: 67.3 years). Total WMH volume was strongly related to cognitive performance on all four cognitive domains. WMH volume in the left anterior thalamic radiation was significantly associated with cognitive performance on attention and executive functioning and information processing speed, and WMH volume in the forceps major with information processing speed. The multivariable lasso regression showed that these associations were independent of age, sex, education, and total infarct volume and had larger coefficients than total WMH volume. CONCLUSIONS: These results show tract-specific relations between WMH volume and cognitive performance after ischemic stroke, independent of total WMH volume. This implies that the concept of strategic lesions in PSCI extends beyond acute infarcts and also involves pre-existing WMH. DATA AVAILABILITY: The Meta VCI Map consortium is dedicated to data sharing, following our guidelines.

Could psychostimulant drug use among university students be related to ADHD symptoms? A preliminary study.

We aimed to explore if psychostimulant use among student could be linked to attention deficit-hyperactivity disorder (ADHD) symptoms using a self-administered questionnaire sent by email to French students in 2021. Participants were asked about their psychostimulant use and the presence of ADHD symptoms using the Wender Utah Rating Scale and the Adult Self-Report Scale. Among the 4431 respondents, the prevalence of psychostimulant use was concerning and significantly associated with ADHD symptoms. This association could be related to undiagnosed ADHD or to psychobehavioral impairments induced by psychostimulant use underlining the need of ADHD screening and targeted prevention measures.

An overview of the use of psychoactive substances among students at the University of Lille during the COVID-19 health crisis: Results of the PETRA study.

OBJECTIVES: Students represent a population at risk for substance abuse. That risk may have been exacerbated by the COVID-19 pandemic. We aimed to describe substance abuse among students and to compare consumption according to the university field. METHODS: A self-administered questionnaire was sent by email to all students at the University of Lille, France, between March and July 2021. This anonymous questionnaire included questions about sociodemographic characteristics, university courses and the use of psychoactive substances (frequency, reasons, routes of administration) since the first university year. RESULTS: Among the 4431 students who responded (response rate 6.1%), eighty percent declared having used alcohol since the first university year, 34% cannabis, 15.4% benzodiazepines, 14.7% opioid drugs, 7.5% cocaine, 6.8% nitrous oxide and 6.5% MDMA. More than 20% of the users of cannabis, benzodiazepines, amphetamines and cocaine reported having already felt dependent. Recreational use was described by more than 10% of benzodiazepine or opioid drug users. Nitrous oxide use was significantly more frequent in the health and sport field (p < 0.001). Tobacco, benzodiazepine, cannabis and MDMA uses were significantly more frequent in the humanities and social sciences/art, language and literature fields (p < 0.001). CONCLUSION: Prevention measures focusing on alcohol, cannabis, illicit psychostimulants, nitrous oxide and prescription drugs are required in the student population.

Transcranial Magnetic Stimulation (rTMS) on the Precuneus in Alzheimer's Disease: A Literature Review.

The current literature review aimed to evaluate the effectiveness of rTMS on the precuneus as a potential treatment for Alzheimer's disease (AD). Although the number of studies specifically targeting the precuneus is limited, the results from this review suggest the potential benefits of this approach. Future studies should focus on exploring the long-term effects of rTMS on the precuneus in Alzheimer's disease patients, as well as determining the optimal stimulation parameters and protocols for this population. Additionally, investigating the effects of rTMS on the precuneus in combination with other brain regions implicated in AD may provide valuable insights into the development of effective treatment for this debilitating neurodegenerative disorder.

Short-term Trajectories of Poststroke Cognitive Function: A STROKOG Collaboration Study.

BACKGROUND AND OBJECTIVES: Past studies on poststroke cognitive function have focused on the average performance or change over time, but few have investigated patterns of cognitive trajectories after stroke. This project used latent class growth analysis (LCGA) to identify clusters of patients with similar patterns of cognition scores over the first-year poststroke and the extent to which long-term cognitive outcome is predicted by the clusters ("trajectory groups"). METHODS: Data were sought from the Stroke and Cognition consortium. LCGA was used to identify clusters of trajectories based on standardized global cognition scores at baseline (T1) and at the 1-year follow-up (T2). One-step individual participant data meta-analysis was used to examine risk factors for trajectory groups and association of trajectory groups with cognition at the long-term follow-up (T3). RESULTS: Nine hospital-based stroke cohorts with 1,149 patients (63% male; mean age 66.4 years [SD 11.0]) were included. The median time assessed at T1 was 3.6 months poststroke, 1.0 year at T2, and 3.2 years at T3. LCGA identified 3 trajectory groups, which were characterized by different mean levels of cognition scores at T1 (low-performance, -3.27 SD [0.94], 17%; medium-performance, -1.23 SD [0.68], 48%; and high-performance, 0.71 SD [0.77], 35%). There was significant improvement in cognition for the high-performance group (0.22 SD per year, 95% CI 0.07-0.36), but changes for the low-performance and medium-performance groups were not significant (-0.10 SD per year, 95% CI -0.33 to 0.13; 0.11 SD per year, 95% CI -0.08 to 0.24, respectively). Factors associated with the low- (vs high-) performance group include age (relative risk ratio [RRR] 1.18, 95% CI 1.14-1.23), years of education (RRR 0.61, 95% CI 0.56-0.67), diabetes (RRR 3.78, 95% CI 2.08-6.88), large artery vs small vessel strokes (RRR 2.77, 95% CI 1.32-5.83), and moderate/severe strokes (RRR 3.17, 95% CI 1.42-7.08). Trajectory groups were predictive of global cognition at T3, but its predictive power was comparable with scores at T1. DISCUSSION: The trajectory of cognitive function over the first-year poststroke is heterogenous. Baseline cognitive function ∼3.6 months poststroke is a good predictor of long-term cognitive outcome. Older age, lower levels of education, diabetes, large artery strokes, and greater stroke severity are risk factors for lower cognitive performance over the first year.

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/.

Long-term cognitive impairments following COVID-19: a possible impact of hypoxia.

BACKGROUND: Cognitive and emotional disorders frequently persist after recovery from the acute symptoms of COVID-19; possible explanations include pneumonia-induced hypoxia, infection of the central nervous system, and microstrokes. The objective of the present study was to characterize the impact of hypoxia on the cognitive and psychological profile following COVID-19. METHODS: Sixty-two patients with COVID-19 were enrolled in a cross-sectional study and divided into two groups based on disease severity: outpatients with no pulmonary complications vs. inpatients with hypoxemic pneumonia having received oxygen therapy. All the participants underwent a comprehensive neuropsychological evaluation that included depression, anxiety, fatigue, sleepiness, attentional, memory and executive processes, and social cognition. For the inpatients, we also collected laboratory data (blood gas, blood glucose, fibrin, fibrinogen, D-dimer, and C-reactive protein). RESULTS: Cognitive disorders was found in patients with COVID-19: at least 18% had an impairment of memory and 11% had attentional dysfunctions. A high level of fatigue (90% of the patients), anxiety (52%), and depression (50%) was also observed. The impairments in attentional (p < 0.001 for omission and commission in CPT 3) and memory (p < 0.003 for Index Cue Efficiency from free and cue selected reminding test) functions were greater in COVID-19 inpatients that in COVID-19 outpatients. In contrast, levels of fatigue, depression, and anxiety were similarly high in both groups. CONCLUSIONS: These findings might help to improve the management of COVID-19 patients as a function of the disease severity in particular for patients with hypoxia.

Long-Term Cognitive Decline After Stroke: An Individual Participant Data Meta-Analysis.

BACKGROUND: Poststroke cognitive impairment is common, but the trajectory and magnitude of cognitive decline after stroke is unclear. We examined the course and determinants of cognitive change after stroke using individual participant data from the Stroke and Cognition Consortium. METHODS: Nine longitudinal hospital-based cohorts from 7 countries were included. Neuropsychological test scores and normative data were used to calculate standardized scores for global cognition and 5 cognitive domains. One-step individual participant data meta-analysis was used to examine the rate of change in cognitive function and risk factors for cognitive decline after stroke. Stroke-free controls were included to examine rate differences. Based on the literature and our own data that showed short-term improvement in cognitive function after stroke, key analyses were restricted to the period beginning 1-year poststroke to focus on its long-term effects. RESULTS: A total of 1488 patients (mean age, 66.3 years; SD, 11.1; 98% ischemic stroke) were followed for a median of 2.68 years (25th-75th percentile: 1.21-4.14 years). After an initial period of improvement through up to 1-year poststroke, decline was seen in global cognition and all domains except executive function after adjusting for age, sex, education, vascular risk factors, and stroke characteristics (-0.053 SD/year [95% CI, -0.073 to -0.033]; P<0.001 for global cognition). Recurrent stroke and older age were associated with faster decline. Decline was significantly faster in patients with stroke compared with controls (difference=-0.078 SD/year [95% CI, -0.11 to -0.045]; P<0.001 for global cognition in a subgroup analysis). CONCLUSIONS: Patients with stroke experience cognitive decline that is faster than that of stroke-free controls from 1 to 3 years after onset. An increased rate of decline is associated with older age and recurrent stroke.

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.

Multimodal brain imaging connectivity analyses of emotional and motivational deficits in depression among women.

BACKGROUND: Major depressive disorder (MDD) is characterized by impaired cortical-subcortical functional connectivity. Apathy adds to functional impairment, but its cerebral basis in MDD remains unknown. Our objective was to describe impairments in functional connectivity during emotional processing in MDD (with varying levels of congruency and attention), and to determine their correlation with apathy. METHODS: We used the Variable Attention Affective Task during functional MRI, followed by diffusion-weighted MRI, to assess 55 right-handed women (30 with MDD and 25 healthy controls) between September 2012 and February 2015. We estimated functional connectivity using generalized psychophysiologic interaction and anatomic connectivity with tract-based spatial statistics. We measured apathy using the Apathy Evaluation Scale. RESULTS: We found decreased functional connectivity between the left amygdala and the left anterior cingulate cortex (ACC) during negative stimuli in participants with MDD (t54 = 4.2; p = 0.035, family-wise error [FWE]-corrected). During high-attention stimuli, participants with MDD showed reduced functional connectivity between the right dorsolateral prefrontal cortex (dlPFC) and the right ACC (t54 = 4.06, pFWE = 0.02), but greater functional connectivity between the right dlPFC and the right amygdala (t54 = 3.35, p = 0.048). Apathy was associated with increased functional connectivity between the right dlPFC and the right ACC during high-attention stimuli (t28 = 5.2, p = 0.01) and increased fractional anisotropy in the right posterior cerebellum, the anterior and posterior cingulum and the bilateral internal capsule (all pFWE < 0.05). LIMITATIONS: Limitations included a moderate sample size, concomitant antidepressant therapy and no directed connectivity. CONCLUSION: We found that MDD was associated with impairments in cortical-subcortical functional connectivity during negative stimuli that might alter the recruitment of networks engaged in attention. Apathy-related features suggested networks similar to those observed in degenerative disorders, but possible different mechanisms.

Functional Role of the Cerebellum in Parkinson Disease: A PET Study.

OBJECTIVES: To test for cerebellar involvement in motor and nonmotor impairments in Parkinson disease (PD) and to determine patterns of metabolic correlations with supratentorial brain structures, we correlated clinical motor, cognitive, and psychiatric scales with cerebellar metabolism. METHODS: We included 90 patients with PD. Motor, cognitive, and psychiatric domains were assessed, and resting-state 18FDG-PET metabolic imaging was performed. The motor, cognitive, and psychiatric scores were entered separately into a principal component analysis. We looked for correlations between these 3 principal components and cerebellar metabolism. Furthermore, we extracted the mean glucose metabolism value for each significant cerebellar cluster and looked for patterns of cerebrum-cerebellum metabolic correlations. RESULTS: Severity of impairment was correlated with increased metabolism in the anterior lobes and vermis (motor domain); the right crus I, crus II, and declive (cognitive domain); and the right crus I and crus II (psychiatric domain). No results survived multiple testing corrections regarding the psychiatric domain. Moreover, we found distributed and overlapping, but not identical, patterns of metabolic correlations for motor and cognitive domains. Specific supratentorial structures (cortical structures, basal ganglia, and thalamus) were strongly correlated with each of the cerebellar clusters. CONCLUSIONS: These results confirm the role of the cerebellum in nonmotor domains of PD, with differential but overlapping patterns of metabolic correlations suggesting the involvement of cerebello-thalamo-striatal-cortical loops.

Prediction of Long-term Cognitive Functions after Minor Stroke, Using Functional Connectivity.

OBJECTIVE: To determine whether functional MRI connectivity can predict the long-term cognitive functions 36 months after minor stroke. METHODS: Seventy-two participants with first-ever stroke were included at baseline and followed up for 36 months. A ridge regression machine learning algorithm was developed and used to predict cognitive scores 36 months post-stroke on the basis of the functional networks measured using MRI at 6 months (referred to here as the post-stroke cognitive impairment (PSCI) network). The prediction accuracy was evaluated in four domains (memory, attention/executive, language and visuospatial functions) and compared with clinical data and other functional networks. The models' statistical significance was probed with permutation tests. The potential involvement of cortical atrophy was assessed 6 months post-stroke. A second, independent dataset (n=40) was used to validate the results and assess their generalizability. RESULTS: Based on the PSCI network, a machine learning model was able to predict memory, attention, visuospatial functions and language functions 36 months post-stroke (r2: 0.67, 0.73, 0.55 and 0.48, respectively). The PSCI-based model was at least as accurate as models based on other functional networks or clinical data. Specific patterns were demonstrated for the four cognitive domains, with involvement of the left superior frontal cortex for memory, attention and visuospatial functions. The cortical thickness 6 months post-stroke was not correlated with cognitive function 36 months post-stroke. The independent validation dataset gave similar results. CONCLUSIONS: A machine learning model based on the PSCI network can predict the long-term cognitive outcome after stroke.

Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts.

BACKGROUND AND PURPOSE: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. METHODS: Data were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. RESULTS: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). CONCLUSIONS: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Association of Prediabetes and Type 2 Diabetes With Cognitive Function After Stroke: A STROKOG Collaboration Study.

Background and Purpose- Type 2 diabetes mellitus (T2D) is associated with cognitive impairment and an increased risk of dementia, but the association between prediabetes and cognitive impairment is less clear, particularly in a setting of major cerebrovascular events. This article examines the impact of impaired fasting glucose and T2D on cognitive performance in a stroke population. Methods- Seven international observational studies from the STROKOG (Stroke and Cognition) consortium (n=1601; mean age, 66.0 years; 70% Asian, 26% white, and 2.6% African American) were included. Fasting glucose level (FGL) during hospitalization was used to define 3 groups, T2D (FGL ≥7.0 mmol/L), impaired fasting glucose (FGL 6.1-6.9 mmol/L), and normal (FGL <6.1 mmol/L), and a history of diabetes mellitus and the use of a diabetes mellitus medication were also used to support a diagnosis of T2D. Domain and global cognition Z scores were derived from standardized neuropsychological test scores. The cross-sectional association between glucose status and cognitive performance at 3 to 6 months poststroke was examined using linear mixed models, adjusting for age, sex, education, stroke type, ethnicity, and vascular risk factors. Results- Patients with T2D had significantly poorer performance in global cognition (SD, -0.59 [95% CI, -0.82 to -0.36]; P<0.001) and in all domains compared with patients with normal FGL. There was no significant difference between impaired fasting glucose patients and those with normal FGL in global cognition (SD, -0.10 [95% CI, -0.45 to 0.24]; P=0.55) or in any cognitive domain. Conclusions- Diabetes mellitus, but not prediabetes, is associated with poorer cognitive performance in patients 3 to 6 months after stroke.

Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment.

Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.

Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson's disease.

Cognitive action control depends on cortical-subcortical circuits, involving notably the subthalamic nucleus (STN), as evidenced by local field potentials recordings (LFPs) studies. The STN consistently shows an increase in theta oscillations power during conflict resolution. Some studies have shown that cognitive action control in Parkinson's disease (PD) could be influenced by the occurrence of monetary reward. In this study, we investigated whether incentive motivation could modulate STN activity, and notably STN theta activity, during response conflict resolution. To achieve this objective, we recorded STN LFPs during a motivated Simon task in PD patients who had undergone deep brain stimulation surgery. Behavioral results revealed that promised rewards increased the difficulty in resolving conflict situations, thus replicating previous findings. Signal analyses locked on the imperative stimulus onset revealed the typical pattern of increased theta power in a conflict situation. However, this conflict-related modulation of theta power was not influenced by the size of the reward cued. We nonetheless identified a significant effect of the reward size on local functional organization (indexed by inter-trial phase clustering) of theta oscillations, with higher organization associated with high rewards while resolving conflict. When focusing on the period following the onset of the reward cue, we unveiled a stronger beta power decrease in higher reward conditions. However, these LFPs results were not correlated to behavioral results. Our study suggests that the STN is involved in how reward information can influence computations during conflict resolution. However, considering recent studies as well as the present results, we suspect that these effects are subtle.

Apathy alters emotional arousal in chronic schizophrenia

Background: Within the heterogeneity of schizophrenia, apathy constitutes an independent cluster of negative symptoms associated with poor outcomes. Attempts to identify an emotional deficit in patients who have schizophrenia with negative symptoms have yielded mixed results, and studies that focus on the relationship between apathy and emotional disorders are lacking. Methods: We set out to remedy this shortcoming using a validated battery of film excerpts to induce positive and negative emotions in patients with chronic schizophrenia with (n = 20) or without (n = 20) apathy, and in controls (n = 20) comparable for age, sex and socioeconomic status. We assessed emotions using an innovative but validated technique to evaluate tonic and phasic electrodermal activity and subjective feelings using a standardized visual analogue scale. Results: Using a qualitative measure of apathy, we did not find a specific decrease in tonic activity during the induction of positive emotions. However, we did observe that patients with apathy showed reduced tonic activity independent of valence (i.e., for both positive and negative emotions) compared with controls and patients without apathy. Moreover, the quantitative measure of apathy (Apathy Evaluation Scale) was the only significant factor, explaining 24% of the variance in tonic activity during induction of positive emotions after controlling for confounding factors. Limitations: Electrodermal activity was the only physiologic measure we acquired. We induced several emotions sequentially that might have overlapped with each other, but we added an emotional "washout" period and randomized the order of each film excerpt to limit this possibility. Conclusion: Taken together, these results suggest that apathy in schizophrenia could impair tonic activity during positive emotions. Treatments aimed at enhancing positive emotions may help alleviate apathy in schizophrenia.

Early MoCA predicts long-term cognitive and functional outcome and mortality after stroke.

OBJECTIVE: To examine whether the Montreal Cognitive Assessment (MoCA) administered within 7 days after stroke predicts long-term cognitive impairment, functional impairment, and mortality. METHODS: MoCA was administered to 274 patients from 2 prospective hospital-based cohort studies in Germany (n = 125) and France (n = 149). Cognitive and functional outcomes were assessed at 6, 12, and 36 months after stroke by comprehensive neuropsychological testing, the Clinical Dementia Rating (CDR) scale, the modified Rankin Scale (mRS), and Instrumental Activities of Daily Living (IADL) and analyzed with generalized estimating equations. All-cause mortality was investigated by Cox proportional hazard models. Analyses were adjusted for demographic variables, education, vascular risk factors, premorbid cognitive status, and NIH Stroke Scale scores. The additive predictive value of MoCA was examined with receiver operating characteristic curves. RESULTS: In pooled analyses, a baseline MoCA score <26 was associated with cognitive impairment, defined by neuropsychological testing (odds ratio [OR] 5.30, 95% confidence interval [CI] 2.75-10.22) and by CDR score ≥0.5 (OR 2.53, 95% CI 1.53-4.18); functional impairment, defined by mRS score >2 (OR 5.03, 95% CI 2.20-11.51) and by IADL score <8 (OR 2.48, 95% CI 1.40-4.38); and mortality (hazard ratio 7.24, 95% CI 1.99-26.35) across the 3-year follow-up. Patients with MoCA score <26 performed worse across all prespecified cognitive domains (executive function/attention, memory, language, visuospatial ability). MoCA increased the area under the curve for predicting cognitive impairment (neuropsychological testing 0.81 vs 0.72, p = 0.01) and functional impairment (mRS score >2, 0.88 vs 0.84, p = 0.047). CONCLUSION: Early cognitive testing by MoCA predicts long-term cognitive outcome, functional outcome, and mortality after stroke. Our results support routine use of the MoCA in stroke patients.

Identification of a specific functional network altered in poststroke cognitive impairment.

OBJECTIVE: To study the association between poststroke cognitive impairment and defining a specific resting functional marker. METHODS: The resting-state functional connectivity 6 months after an ischemic stroke in 56 patients was investigated. Twenty-nine of the patients who had an impairment of one or several cognitive domains were compared to 27 without any cognitive deficit. We studied the whole-brain connectivity using 2 complementary approaches: graph theory to study the functional network organization and network-based statistics to explore connectivity between brain regions. We assessed the potential cortical atrophy using voxel-based morphometry analysis. RESULTS: The overall topological organization of the functional network was not altered in cognitively impaired stroke patients, who had the same mean node degree, average clustering coefficient, and global efficiency as cognitively healthy stroke patients. Network-based statistics analysis showed that poststroke cognitive impairment was associated with dysfunction of a whole-brain network composed of 167 regions and 178 connections, and functional disconnections between superior, middle, and inferior frontal gyri and the superior and inferior temporal gyri. These regions had connections that were specifically and positively correlated with cognitive domain scores. No intergroup differences in overall gray matter thickness and ischemic infarct topography were observed. To assess the effect of prestroke white matter hyperintensities on connectivity, we included the initial Fazekas scale in the regression model for a second network-based analysis. The resulting network was associated with the same key alterations but had fewer connections. CONCLUSIONS: The observed functional network alterations suggest that the appearance of a cognitive impairment following stroke may be associated with a particular functional alteration, shared specifically between cognitive domains.

Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson's disease.

Using intracranial local field potential (LFP) recordings in patients with Parkinson's disease (PD) undergoing deep brain stimulation (DBS), we explored the electrophysiological activity of the subthalamic nucleus (STN) in response to emotional stimuli in the auditory modality. Previous studies focused on the influence of visual stimuli. To this end, we recorded LFPs within the STN in response to angry, happy, and neutral prosodies in 13 patients with PD who had just undergone implantation of DBS electrodes. We observed specific modulation of the right STN in response to anger and happiness, as opposed to neutral prosody, occurring at around 200-300ms post-onset, and later at around 850-950ms post-onset for anger and at around 3250-3350ms post-onset for happiness. Taken together with previous reports of modulated STN activity in response to emotional visual stimuli, the present results appear to confirm that the STN is involved in emotion processing irrespective of stimulus valence and sensory modality.