Biology subjects

Simon, A. J.

Publications and source records attributed to Simon, A. J..

4 recordsLinked to original sources

bioRxiv2026

Simulated field potentials capture multiple simultaneous processes underlying human temporal attention

Essential cognitive processes like attention, memory, and decision-making require the brain to perform many operations simultaneously. Disentangling the neural mechanisms underlying these processes is challenging because they often unfold concurrently with similar dynamics. In auditory temporal expectation, these mechanisms include the accumulation of incoming sensory information in the auditory cortex interacting with cortical signals biasing activity in anticipation of future stimuli. Here, we leverage invasive human field potential recordings from participants performing a subjective temporal expectation task. Rather than relying on traditional neuroimaging methods that average or transform neural responses, we instead simulate each trial's predicted field potential, based on known field potential physiology combined with components of established cognitive theories of the temporal expectation task: sensory input, top-down bias, and sensory anticipation. Our predicted field potentials strongly correlate with neural activity in auditory regions and in regions associated with auditory temporal perception, including the parietal, temporal, and prefrontal cortices. By differentiating individual cognitive mechanisms through simulation, we identify neural signatures of attentional bias and anticipatory sensory activity across multiple cortical regions that predict trial-by-trial perceptual accuracy and confidence.

neuroscience↗

bioRxiv2026

Disordered brain circuits linked to diagnostic specificity and comorbidity revealed by multivariate symptom modeling

Modeling how functional network connectivity underlies transdiagnostic symptomatology has promised to advance psychiatric medicine by revealing neurobiological mechanisms related to comorbidity. However, network mapping methods have yet to yield clinically-actionable insights, largely due to complexities in the neurobiological underpinnings of symptom comorbidity across disorders and symptom heterogeneity within disorders. Here, we sought to address this problem by leveraging a large (n=317) transdiagnostic dataset of adults with extensive fMRI scanning (>50 min), using connectome-based predictive modeling (CPM) to identify network correlates of an array of psychiatric symptoms. The symptom networks spanned a complex web of shared and unique networks, in which individuals displayed significant heterogeneity in their edge-level dysfunction. We then constructed disordered circuit models that jointly accounted for an individuals symptom severity, the multivariate network space, and network heterogeneity. Although all the symptoms were highly comorbid and none showed specificity to any single diagnostic category, many features within the disordered circuit models were uniquely associated with individual diagnoses and comorbidity patters. These findings shed mechanistic insights into how transdiagnostic symptoms arise from different neurobiological processes depending on a patients diagnostic profile. Thus, this approach provides key insights into where an individuals disordered circuits are located, a critical first step in precision psychiatry frameworks.

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2018

Supercharging enables organized assembly of synthetic biomolecules

There are few methods for the assembly of defined protein oligomers and higher order structures that could serve as novel biomaterials. Using fluorescent proteins as a model system, we have engineered novel oligomerization states by combining oppositely supercharged variants. A well-defined, highly symmetrical 16-mer (two stacked, circular octamers) can be formed from alternating charged proteins; higher order structures then form in a hierarchical fashion from this discrete protomer. During SUpercharged PRotein Assembly (SuPrA), electrostatic attraction between oppositely charged variants drives interaction, while shape and patchy physicochemical interactions lead to spatial organization along specific interfaces, ultimately resulting in protein assemblies never before seen in nature.

bioengineering↗