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Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations. To investigate the association between post-stroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n=82). Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains. These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting post stroke cognitive sequalae.

Fatigue and emotional distress rank high among self-reported unmet needs in stroke survivors. Currently, few treatment options exist for post stroke fatigue, a condition frequently associated with depression. Non-invasive brain stimulation techniques such as transcranial direct current stimulation (tDCS) have shown promise in alleviating fatigue and depression in other patient groups, but the acceptability and effects for chronic phase stroke survivors are not established. Here, we used a randomized sham-controlled design to evaluate the added effect of tDCS combined with computerized cognitive training to alleviate symptoms of fatigue and depression. 74 patients were enrolled at baseline (mean time since stroke = 26 months) and 54 patients completed the intervention. Self-report measures of fatigue and depression were collected at five consecutive timepoints, spanning a period of two months. While fatigue and depression severity were reduced during the course of the intervention, Bayesian analyses provided evidence for no added effect of tDCS. Less severe symptoms of fatigue and depression were associated with higher improvement rate in select tasks, and study withdrawal was higher in patients with more severe fatigue and younger age. Time-resolved analyses of individual symptoms by a network-approach suggested overall higher centrality of fatigue symptoms (except item 1 and 2) than depression symptoms. In conclusion, the results support the notion of fatigue as a significant stroke sequela with possible implications for treatment adherence and response, but reveal no effect of tDCS on fatigue or depression.

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropomorphic features fail to describe regional differences in body fat distribution and composition, and to determine visceral adiposity, which is related to a range of metabolic conditions. In this mixed cross-sectional and longitudinal design (interval mean and standard deviation = 19.7 ± 0.5 months), including 790 healthy individuals (mean (range) age = 46.7 (18-94) years, 53% women), we investigated cross-sectional body magnetic resonance imaging (MRI, n = 286) measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remain unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual brain disconnectome maps in 84 stroke survivors in the chronic phase (≥ 3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue are not simply explained by lesion characteristics or brain disconnectome measures in stroke patients in a chronic phase, and are discussed in light of methodological considerations.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalised on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine the age-related morphometric trajectories of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum early in life; the volume of the basal ganglia showed a gradual monotonic decline thereafter while the volumes of the thalamus, amygdala and the hippocampus remained largely stable (with some degree of decline in thalamus) until the sixth decade of life followed by a steep decline thereafter. The lateral ventricles showed a trajectory of continuous enlargement throughout the lifespan. Significant age-related increase in inter-individual variability was found for the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to derive risk predictions for the early identification of diverse clinical phenotypes.

Attentional deficits following stroke are common and pervasive, and are important predictors for functional recovery. Attentional functions comprise a set of specific cognitive processes allowing to attend, filter and select among a continuous stream of stimuli. These mechanisms are fundamental for more complex cognitive functions such as learning, planning and cognitive control, all crucial for daily functioning, including social interactions. The distributed functional neuroanatomy of these processes is a likely explanation for the high prevalence of attentional impairments following stroke, and underscores the importance of a clinical implementation of computational approaches allowing for sensitive and specific modeling of attentional sub-processes. The Theory of Visual Attention (TVA) offers a theoretical, computational, neuronal and practical framework to assess the efficiency of visual selection performance and parallel processing of multiple objects. Here, using a whole-report experimental paradigm in a cross-sectional case-control comparison and in six repeated assessments over the course of a three-week intensive computerized cognitive training (CCT) intervention in chronic stroke patients (> 6 months since hospital admission, NIHSS < 7 at hospital discharge), we assessed the sensitivity and reliability of TVA parameters reflecting short-term memory capacity (K), processing speed (C) and perceptual threshold (t0). Cross-sectional group comparisons documented lower short-term memory capacity, slower processing speed and higher perceptual threshold in patients compared to age-matched healthy controls. Further, longitudinal analyses revealed high reliability of the TVA parameters in stroke patients, and higher processing speed at baseline was associated with larger cognitive benefits of the CCT. The results support the feasibility, reliability and sensitivity of TVA-based assessment of attentional functions in chronic stroke patients.

Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS

Post-stroke fatigue (PSF) is a prevalent symptom among stroke patients. Its symptom burden is pervasive, persistent and associated with poor rehabilitation outcomes, though its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. In contrast to conventional neuropsychological assessment, computational modeling of behavioral data allows for a dissection of specific cognitive processes associated with group or individual differences in fatigue. With the aim to zero in on the cognitive phenotype of PSF, we fitted a hierarchical drift diffusion model (hDDM) to response time data from Attention Network Test (ANT) obtained from 53 chronic stroke patients. The computational model accurately reconstructed the individual level response time distributions in the different ANT conditions, and hDDM regressions identified an interaction between trial number and fatigue symptoms on non-decision time, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained attentional effort. These novel results demonstrate the significance of considering the sustained nature of cognitive effort when defining the cognitive phenotype of post-stroke fatigue, and suggest that the use of computational approaches offers a further characterization of the specific processes underlying observed behavioral differences.

Background The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are known to be differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often found to be impaired in individuals with brain disorders associated with reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given these structural and functional differences, we sought to characterize the subfields’ shared and specific genetic architecture. Methods T1-images (n= 17418, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We calculated the SNP-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. We further ran a genome-wide association analysis on each subfield, correcting for total hippocampal volume. All analyses included age, age2, sex, and intracranial volume as covariates. Results Volumes of all subfields were heritable (h2 ranging from .15 to .29, all p< 2.2 * 10-9) and clustered together (genetic correlations Rg>.41), compared to other brain features. The subiculum and the hippocampal-amygdalar transition area (HATA) showed significant genetic correlation with AD and schizophrenia, respectively. We found 14 independent whole-genome significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were annotated to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. Conclusions Hippocampal subfields have partly distinct genetic determinants, associated with specific biological processes and traits. Taking into account this specificity may aid in furthering our understanding of hippocampal neurobiology and associated disorders.