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Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope ‘a’ of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control−stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke., Highlights • FreeSurfer–Subfields segmentation is favoured for hippocampal volume estimation in healthy and ischemic stroke participants • Subfields agreed most with manual tracing of the hippocampus performed in accordance with the EADC-ADNI harmonized protocol • Masking (removal) of stroke lesions in MRI images negatively impact on the performance of automated hippocampal segmentation • Manual and automated segmentation revealed a significantly compromised hippocampal state in ischemic stroke patients

Introduction Compared to healthy individuals, stroke patients have five times the rate of dementia diagnosis within three years. Aerobic exercise may induce neuroprotective mechanisms that help to preserve, and even increase, brain volume and cognition. We seek to determine whether aerobic fitness training helps to protect brain volume and cognitive function after stroke compared to an active, non-aerobic control. Methods In this Phase IIb, single blind, randomised controlled trial, 100 ischaemic stroke participants, recruited at two months post-stroke, will be randomly allocated to either the intervention (aerobic and strength exercise) or active control (stretching and balance training). Participants will attend one-hour, individualised exercise sessions, three days-per-week for eight weeks. Assessments at two months (baseline), four months (post-intervention), and one year (follow-up) post-stroke will measure brain volume, cognition, mood, cardiorespiratory fitness, physical activity, blood pressure and blood biomarkers. Study outcome: Our primary outcome measure is hippocampal volume at four months after stroke. We hypothesise that participants who undertake the prescribed intervention will have preserved hippocampal volume at four months compared to the control group. We also hypothesise that this group will have preserved total brain volume and cognition, better mood, fitness, and higher levels of physical activity, than those receiving stretching and balance training. Discussion The promise of exercise training to prevent, or slow, the accelerated rates of brain atrophy and cognitive decline experienced by stroke survivors needs to be tested. Post Ischaemic Stroke Cardiovascular Exercise Study has the potential, if proven efficacious, to identify a new treatment that could be readily translated to the clinic.

Cue combination occurs when two independent noisy perceptual estimates are merged together as a weighted average, creating a unified estimate that is more precise than either single estimate alone. Surprisingly, this effect has not been demonstrated compellingly in children under the age of 10 years, in contrast with the array of other multisensory skills that children show even in infancy. Instead, across a wide variety of studies, precision with both cues is no better than the best single cue – and sometimes worse. Here we provide the first consistent evidence of cue combination in children from 7–10 years old. Across three experiments, participants showed evidence of a bimodal precision advantage (Experiments 1a and 1b) and the majority were best-fit by a combining model (Experiment 2). The task was to localize a target horizontally with a binaural audio cue and a noisy visual cue in immersive virtual reality. Feedback was given as well, which could both (a) help participants judge how reliable each cue is and (b) help correct between-cue biases that might prevent cue combination. A feedback effect was found in Experiment 2, with children who were given single-cue feedback showing the highest rate of cue combination. Given this, we suggest that children at 7–10 years old are capable of cue combination in principle, but must have sufficient representations of reliabilities and biases in their own perceptual estimates as relevant to the task, which can be facilitated through task-specific feedback.