Biology subjects

Owen, L. L. W.

Publications and source records attributed to Owen, L. L. W..

7 recordsLinked to original sources

bioRxiv2026

Sparse Distributed Archetypes Reveal Compressible Network Motifs Underlying Naturalistic Cognition

Naturalistic cognition emerges from coordinated interactions among distributed brain systems operating across multiple representational scales. Characterizing this organization remains challenging because cognitively relevant information is embedded within high-dimensional neural activity. Here, we apply Multisubject Archetypal Analysis (MS-AA) [1] to naturalistic fMRI data collected during intact narrative listening, word-scrambled audio, and rest to investigate how condition-relevant information is distributed across archetypal representations. We examine both spatial and temporal formulations of MS-AA as complementary views of naturalistic brain activity. Across analyses, decoding performance consistently followed the hierarchy intact > word-scrambled > rest, indicating that archetypal representations preserve meaningful condition-related structure. Top-m decoding analyses further revealed that this information is highly compressible: relatively small subsets of archetypes frequently recovered substantial fractions of full-model decoding performance. Spatial AA exhibited a stable sparse-decoding regime that persisted across representational scales. Across a broad range of matched component ratios, approximately 5-15 archetypes consistently captured disproportionate amounts of condition-relevant information. These same sparse subsets also organized subjects into condition-aligned clusters more strongly than expected from random archetype subsets, with the strongest joint decoding-clustering effects occurring repeatedly within an intermediate representational regime (K {approx}50 - 88). Network over-representation analyses revealed that informative archetypes were not isolated canonical networks but distributed mixtures of interacting systems. Across the highest-performing decoding- clustering configurations, default mode and frontoparietal systems were consistently overrepresented relative to network size, whereas visual and limbic systems were underrepresented. Together, these findings suggest that the archetypal motifs most informative for distinguishing cognitive states are sparse, distributed subnetworks enriched for higher-order association systems. More broadly, the results demonstrate that MS-AA provides a useful framework for studying the compressibility, geometry, and multiscale organization of cognitive brain states.

neuroscience↗

bioRxiv2025

Altered Decision-Making Across Acute and Chronic Pain States: A Bias Towards Context-Dependent Valuations

Chronic pain is often characterized by the entrenchment of maladaptive behaviors -- avoidance, inactivity, and hypervigilance -- that outlast tissue damage. A central but mechanistically underspecified question is how behaviors acquired during acute pain become locked in as pain chronifies. Influential accounts, including the fear-avoidance model, suggest that behavioral patterns learned in a specific context -- such as avoidance during acute injury -- overgeneralize to situations where they are no longer advantageous. Whether this reflects altered learning or changes in how learned information guides decisions has remained unclear. To address this, we administered a probabilistic reinforcement learning task to 239 individuals with chronic pain, acute pain, or no pain, designed to dissociate two decision strategies: reliance on recent reinforcement history -- what was reinforced in a prior context -- versus global expected value -- the objective worth of an option regardless of context. Learning performance was comparable across all groups, confirming intact associative learning. However, groups differed significantly in decision-making: individuals with chronic pain favored options with stronger context-dependent reinforcement history over those with higher global expected value; however, those without pain showed no preference for one over the other, with the acute pain group showing an intermediate pattern. Computational modeling confirmed this, with chronic pain patients showing significantly reduced weighting toward global expected value. Critically, this shift tracked pain duration rather than intensity -- suggesting prolonged pain exposure gradually biases decisions away from global expected value towards a context-dependent history. These findings offer a computational explanation for behavioral persistence in chronic pain, and this duration-dependent shift, already evident in the acute pain group, may represent an early cognitive signature of chronification.

neuroscience↗

bioRxiv2023

High-level cognition is supported by information-rich but compressible brain activity patterns

Brain activity patterns are highly flexible and often complex, but also highly structured. Here we examined how fundamental properties of brain activity patterns relate to ongoing cognitive processes. To this end, we applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. These experimental conditions were intended to require different depths of processing and inspire different levels of cognitive engagement. We considered two primary aspects of the data. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands, and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower levels of engagement. Significance StatementHow our brains respond to ongoing experiences depends on what we are doing and thinking about, among other factors. To study how brain activity reflects ongoing cognition, we examined two fundamental aspects of brain activity under different cognitive circumstances: informativeness and compressibility. Informativeness refers to the extent to which brain patterns are both temporally specific and consistent across different people. Compressibility refers to how robust the informativeness of brain patterns is to dimensionality reduction. Brain activity evoked by higher-level cognitive tasks are both more informative and more compressible than activity evoked by lower-level tasks. Our findings suggest that our brains flexibly reconfigure themselves to optimize different aspects of how they function according to ongoing cognitive demands.

neuroscience↗

bioRxiv2021

Preserved motor memory in Parkinson's disease

Patients with Parkinsons disease, who lose the dopaminergic projections to the striatum, are impaired in certain aspects of motor learning. Recent evidence suggests that, in addition to its role in motor performance, the striatum plays a key role in the memory of motor learning. Whether Parkinsons patients have impaired motor memory and whether motor memory is modulated by dopamine at the time of initial learning is unknown. To address these questions, we measured memory of a learned motor sequence in Parkinsons patients who were either On or Off their dopaminergic medications. We compared them to a group of older and younger controls. Contrary to our predictions, motor memory was not impaired in patients compared to older controls, and was not influenced by dopamine state at the time of initial learning. To probe post-learning consolidation processes, we also tested whether learning a new sequence shortly after learning the initial sequence would interfere with later memory. We found that, in contrast to younger adults, neither older adults nor patients were susceptible to this interference. These findings suggest that motor memory is preserved in Parkinsons patients and raise the possibility that motor memory in patients is supported by compensatory non-dopamine sensitive mechanisms. Furthermore, given the similar performance characteristics observed in the patients and older adults and the absence of an effect of dopamine, these results raise the possibility that aging and Parkinsons disease affect motor memory in similar ways.

neuroscience↗

bioRxiv2020

Biomarkers of Depression Symptoms Defined by Direct Intracranial Neurophysiology

Quantitative biological substrates of depression remain elusive. We carried out this study to determine whether application of a novel computational approach to high spatiotemporal resolution direct neural recordings may unlock the functional organization and coordinated activity patterns of depression networks. We identified two subnetworks conserved across the majority of individuals studied. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings identify distributed circuit activity associated with depression, link neural activity with functional connectivity profiles, and inform strategies for personalized targeted intervention.

neuroscience↗

bioRxiv2019

High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brains functional connectome that display homologous lower-level dynamic correlations. We tested the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We developed an approach to estimating high-order dynamic correlations in timeseries data, and we applied the approach to neuroimaging data collected as human participants either listened to a ten-minute story or listened to a temporally scrambled version of the story. We trained across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We found that classifiers trained to decode from high-order dynamic correlations yielded the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

neuroscience↗

bioRxiv2017

Towards Human Super EEG

We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different peoples brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individuals brain: given recordings from a limited set of locations in that individuals brain, along with the observed spatial correlations learned from other peoples recordings, how much can be inferred about ongoing activity at other locations throughout that individuals brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

neuroscience↗