Long-term effects of two psychological interventions on physical exercise and self-regulation following coronary rehabilitation

In cardiac rehabilitation programs, patients learn how to adopt a healthier lifestyle, including regular, strenuous physical activity. Long-term success is only modest despite good intentions. To improve exercise adherence, a 3-group experiment was designed that included innovative psychological interventions. All 3 groups underwent a standard care rehabilitation program. Patients in the 2 treatment groups were instructed not only to produce detailed action plans but also to develop barrier-focused mental strategies. On top of this, in 1 of these groups a weekly diary was kept for 6 weeks to increase a sense of action control. At the end of a standard cardiac rehabilitation program, 240 patients were randomly assigned to these treatment groups plus a standard care control group. Treatments resulted in more physical activity at follow-up and better adherence to recommended levels of exercise intensity. Moreover, self-regulatory skills such as planning and action control were improved by the treatments. Follow-up analyses demonstrated the mediating mechanisms of self-regulatory skills in the process of physical exercise maintenance. Findings imply that interventions targeting self-regulatory skills can enable post-rehabilitation patients to reduce behavioral risk factors and facilitate intended lifestyle changes

Identifying behavioral structure from deep variational embeddings of animal motion

Quantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose estimation enable the visualization of high-dimensional spatiotemporal behavioral dynamics of animal motion. However, robust and reliable technical approaches are needed to uncover underlying structure in these data and to segment behavior into discrete hierarchically organized motifs. Here, we present an unsupervised probabilistic deep learning framework that identifies behavioral structure from deep variational embeddings of animal motion (VAME). By using a mouse model of beta amyloidosis as a use case, we show that VAME not only identifies discrete behavioral motifs, but also captures a hierarchical representation of the motif's usage. The approach allows for the grouping of motifs into communities and the detection of differences in community-specific motif usage of individual mouse cohorts that were undetectable by human visual observation. Thus, we present a robust approach for the segmentation of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference

Hippocampal hyperactivity in a rat model of Alzheimer's disease

Neuronal network dysfunction is a hallmark of Alzheimer's disease (AD). However, the underlying pathomechanisms remain unknown. We analyzed the hippocampal micronetwork in transgenic McGill-R-Thy1-APP rats (APPtg) at the beginning of extracellular amyloid beta (Aβ) deposition. We established two-photon Ca2+ -imaging in vivo in the hippocampus of rats and found hyperactivity of CA1 neurons. Patch-clamp recordings in brain slices in vitro revealed increased neuronal input resistance and prolonged action potential width in CA1 pyramidal neurons. We did neither observe changes in synaptic inhibition, nor in excitation. Our data support the view that increased intrinsic excitability of CA1 neurons may precede inhibitory dysfunction at an early stage of Aβ-deposition and disease progression

A septal-ventral tegmental area circuit drives exploratory behavior

To survive, animals need to balance their exploratory drive with their need for safety. Subcortical circuits play an important role in initiating and modulating movement based on external demands and the internal state of the animal; however, how motivation and onset of locomotion are regulated remain largely unresolved. Here, we show that a glutamatergic pathway from the medial septum and diagonal band of Broca (MSDB) to the ventral tegmental area (VTA) controls exploratory locomotor behavior in mice. Using a self-supervised machine learning approach, we found an overrepresentation of exploratory actions, such as sniffing, whisking, and rearing, when this projection is optogenetically activated. Mechanistically, this role relies on glutamatergic MSDB projections that monosynaptically target a subset of both glutamatergic and dopaminergic VTA neurons. Taken together, we identified a glutamatergic basal forebrain to midbrain circuit that initiates locomotor activity and contributes to the expression of exploration-associated behavior

Departement Informatik, ETH Zürich

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