In 1949, Donald Hebb proposed that neuronal assemblies with temporally specific patterns of activity form the building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging due to a lack of large-scale structure-activity maps. Here, we combine in vivo optical physiology with postmortem electron microscopy (EM) in the same tissue volume. Using higher-order correlations in fluorescence traces, we extract neuronal assemblies. Physiologically, we show that these assemblies respond more reliably to repeated natural movies than size-matched control ensembles and decode such stimuli more accurately. Structurally, we find that over a quarter of the pyramidal neurons do not participate in any assembly and are significantly less integrated into the connectome than those that do. We do not observe a marked increase in the strength of monosynaptic excitatory connections between neurons sharing assembly assignment, but instead find significantly stronger indirect inhibitory connections targeting cells in other assemblies. These results show that assemblies can serve as functional units of perception and suggest they may be structurally delineated by mutual inhibition.
J. Wagner-Carena, Sai Kate, Trevor Riordan et al.· Cell Reports· 0 citations
Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with motor symptoms and cognitive changes. Its increasing global disease burden highlights the need for scalable approaches to support remote assessment and digital biomarker discovery. Digital health platforms offer scalable opportunities to assess motor and cognitive performance metrics relevant to PD diagnosis remotely, minimizing logistical and geographic barriers. We introduce RobustPDx, a dataset comprising structured motor and cognitive tasks performed remotely via an accessible web-based assessment completed by 261 participants, including 73 individuals with self-reported PD, 33 individuals reporting suspected PD, and 155 non-PD controls, each completing standardized tasks remotely using their own computer. The dataset includes raw task-level interaction data and a feature-engineered analytic dataset containing 79 derived features spanning five task categories related to fine motor control and working memory. Each record in the resulting dataset is paired with demographic data, device type, and handedness annotations. Notably, including device type and handedness metadata enables analyses along traditionally underexplored but important axes of variation in remote consumer digital health. Together, these data support the development and benchmarking of AI-driven models for remote PD detection and robust digital biomarker discovery.
Zerin Nasrin Tumpa, Md Rahat Shahriar Zawad, L. Sollis et al.· Scientific Data· 0 citations
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