bioRxiv ScienceSearch

bioRxiv · 10.1101/582858

Representation of task episodes in human cortical networks

Abstract

Task episodes consist of sequences of steps that are performed to achieve a goal. We used fMRI to examine neural representation of task identity, component items, and sequential position, focusing on two major cortical systems - the multiple-demand (MD) and default mode networks (DMN). Human participants (20 male, 22 female) learned six tasks each consisting of four steps. Inside the scanner, participants were cued which task to perform and then sequentially identified the target item of each step in the correct order. Univariate time-course analyses indicated that intra-episode progress was tracked by a tonically increasing global response, plus an increasing phasic step response specific to MD regions. Inter-episode boundaries evoked a widespread response at episode onset, plus a marked offset response specific to DMN regions. Representational similarity analysis was used to examine encoding of task identity and component steps. Both networks represented the content and position of individual steps, but the DMN preferentially represented task identity while the MD network preferentially represented step-level information. Thus, although both DMN and MD networks are sensitive to step-level and episode-level information in the context of hierarchical task performance, they exhibit dissociable profiles in terms of both temporal dynamics and representational content. The results suggest collaboration of multiple brain regions in control of multi-step behavior, with MD regions particularly involved in processing the detail of individual steps, and DMN adding representation of broad task context. Significance StatementAchieving ones goals requires knowing what to do and when. Tasks are typically hierarchical, with smaller steps nested within overarching goals. For effective, flexible behavior, the brain must represent both levels. We contrast response time-courses and information content of two major cortical systems - the multiple-demand (MD) and default mode networks (DMN) - during multi-step task episodes. Both networks are sensitive to step-level and episode-level information, but with dissociable profiles. Intra-episode progress is tracked by tonically increasing global responses, plus MD-specific increasing phasic step responses. Inter-episode boundaries evoke widespread responses at episode onset, plus DMN-specific offset responses. Both networks encode content and position of individual steps, but the DMN and MD networks favor task identity and step-level information respectively.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wen, T., Duncan, J., Mitchell, D. J.. 2019-03-21. Representation of task episodes in human cortical networks. https://doi.org/10.1101/582858

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience