Biology subjects

Simon, J. P.

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

3 recordsLinked to original sources

bioRxiv2025

Distributed activity in the human posterior putamen distinguishes goal-directed from habitual control in humans

How do some individuals rapidly form habits while others maintain flexible, goal-directed control? Using multivariate fMRI decoding in 199 participants, we show that distributed neural activity patterns in the left posterior putamen during initial learning predict individual behavioral strategy on a subsequent outcome devaluation test. This prediction generalized across two independent cohorts of healthy adults and psychiatric patients with heterogeneous diagnoses and was anatomically specific to the posterior putamen. Critically, predictive neural signatures were present during training, before strategy expression after devaluation, enabling prospective classification of habitual versus goal-directed behavior. These findings demonstrate that stable individual differences in behavioral control are reflected in circumscribed brain activity during learning, highlighting the posterior putamen as a candidate neural marker of habit propensity with potential clinical relevance.

neuroscience↗

bioRxiv2025

Model-based and model-free valuation signals in the human brain vary markedly in their relationship to individual differences in behavioral control

Human action selection under reinforcement is thought to rely on two distinct strategies: model-free and model-based reinforcement learning. While behavior in sequential decision-making tasks often reflects a mixture of both, the neural basis of individual differences in their expression remains unclear. To investigate this, we conducted a large-scale fMRI study with 179 participants performing a variant of the two-step task. Using both cluster-defined subgroups and computational parameter estimates, we found that the ventromedial prefrontal cortex encodes model-based and model-free value signals differently depending on individual strategy use. Model-based value signals were strongly linked to the degree of model-based behavioral reliance, whereas model-free signals appeared regardless of model-free behavioral influence. Leveraging the large sample, we found individuals lacking both model-based behavior and model-based neural signals exhibited impaired state prediction errors, suggesting a difficulty in building or updating their internal model of the environment. These findings indicate that model-free signals are ubiquitous across individuals, even in those not behaviorally relying on model-free strategies, while model-based representations appear only in those individuals utilizing such a strategy at the behavioral level, the absence of which may depend in part on underlying difficulties in forming accurate model-based predictions.

neuroscience↗

bioRxiv2021

Robust T1 MRI cortical surface pipeline for neonatal brain and systematic evaluation using multi-site MRI datasets

The human brain grows the most dramatically during the perinatal and early postnatal periods, during which preterm birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal MRI pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the CSF/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 preterm and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532{+/-}0.035mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with postmenstrual age (PMA) at scan (p<0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3mm at 28 weeks of PMA to 1.8mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.

bioinformatics↗