The hippocampal formation represents a key region in the pathophysiology of schizophrenia. Aerobic exercise poses a promising add-on treatment to potentially counteract structural impairments of the hippocampal formation and associated symptomatic burden. However, current evidence regarding exercise effects on the hippocampal formation in schizophrenia is largely heterogeneous. Therefore, we conducted a systematic review and meta-analysis to assess the impact of aerobic exercise on total hippocampal formation volume. Additionally, we used data from a recent multicenter randomized-controlled trial to examine the effects of aerobic exercise on hippocampal formation subfield volumes and their respective clinical implications.

The meta-analysis comprised six studies that investigated the influence of aerobic exercise on total hippocampal formation volume compared to a control condition with a total of 186 people with schizophrenia (100 male, 86 female), while original data from 29 patients (20 male, 9 female) was considered to explore effects of six months of aerobic exercise on hippocampal formation subfield volumes.

Our meta-analysis did not demonstrate a significant effect of aerobic exercise on total hippocampal formation volume in people with schizophrenia (g = 0.33 [−0.12 to 0.77]), p = 0.15), but our original data suggested significant volume increases in certain hippocampal subfields, namely the cornu ammonis and dentate gyrus.

Driven by the necessity of better understanding the pathophysiology of schizophrenia, the present work underlines the importance to focus on hippocampal formation subfields and to characterize subgroups of patients that show neuroplastic responses to aerobic exercise accompanied by corresponding clinical improvements.

Unsupervised remote digital cognitive assessment makes frequent testing feasible and allows for measurement of learning across days on participants’ own devices. More rapid detection of diminished learning may provide a potentially valuable metric that is sensitive to cognitive change over short intervals. In this study we examine feasibility and predictive validity of a novel digital assessment that measures learning of the same material over 7 days in older adults.

The Boston Remote Assessment for Neurocognitive Health (BRANCH) (Papp et al., 2021) is a web-based assessment administered over 7 consecutive days repeating the same stimuli each day to capture multi-day-learning slopes. The assessment includes Face-Name (verbal-visual associative memory), Groceries-Prices (numeric-visual associative memory), and Digits-Signs (speeded processing of numeric-visual associations). Our sample consisted of200 cognitively unimpaired older adults enrolled in ongoing observational studies (mean age=74.5, 63% female, 87% Caucasian, mean education=16.6) who completed the tasks daily, at home, on their own digital devices. Participants had previously completed in-clinic paper-and-pencil tests to compute a Preclinical Alzheimer’s Cognitive Composite (PACC-5). Mixed-effects models controlling for age, sex, and education were used to observe the associations between PACC-5 scores and both initial performance and multi-day learning on the three BRANCH measures.

Adherence was high with 96% of participants completing all seven days of consecutive assessment; demographic factors were not associated with differences in adherence. Younger participants had higher Day 1 scores all three measures, and learning slopes on Digit-Sign. Female participants performed better on Face-Name (T=3.35, p<.001) and Groceries-Prices (T=2.00, p=0.04) on Day 1 but no sex differences were seen in learning slopes; there were no sex differences on Digit-Sign. Black participants had lower Day 1 scores on Face-Name (T=-3.34, p=0.003) and Digit Sign (T=3.44, p=0.002), but no racial differences were seen on learning slopes for any measure. Education was not associated with any measure. First day performance on Face-Name (B=0.39, p<.001), but not learning slope B=0.008, p=0.302) was associated with the PACC5. For Groceries-Prices, both Day 1 (B=0.27, p<.001) and learning slope (B=0.02, p=0.03) were associated with PACC-5. The Digit-Sign scores at Day 1 (B=0.31, p<.001) and learning slope (B=0.06, p<.001) were also both associated with PACC-5.

Seven days of remote, brief cognitive assessment was feasible in a sample of cognitively unimpaired older adults. Although various demographic factors were associated with initial performance on the tests, multi-day-learning slopes were largely unrelated to demographics, signaling the possibility of its utility in diverse samples. Both initial performance and learning scores on an associative memory and processing speed test were independently related to baseline cognition indicating that these tests’ initial performance and learning metrics are convergent but unique in their contributions. The findings signal the value of measuring differences in learning across days as a means towards sensitively identifying differences in cognitive function before signs of frank impairment are observed. Next steps will involve identifying the optimal way to model multi-day learning on these subtests to evaluate their potential associations with Alzheimer’s disease biomarkers.

Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups.

Ninety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery–Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) < 15) and 25 sex- and age-matched HCs were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HCs. Mean values and time variations of a set of standard actigraphy features were analyzed and further validated using the random forest classifier.

Using all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen’s d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified.

A machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.