Biology subjects

Leach, S. C.

Publications and source records attributed to Leach, S. C..

5 recordsLinked to original sources

bioRxiv2026

Estimating trial-wise modulation of functional connectivity using event-related fMRI

Understanding the neural basis of human cognition requires measuring not only localized brain activity but also how functional interactions between brain regions change in response to different cognitive demands. Event-related fMRI is an efficient design for linking trial-wise behavioral and computational variables to brain activity, but comparable methods for examining their effects on functional connectivity remain limited. Here, we develop beta-PPI (beta-series psychophysiological interaction), a method that leverages single-trial response estimates from even-related fMRI to quantify how trial-wise variables modulate functional connectivity. This task-based functional connectivity method provides a flexible approach for studying functional connectivity for event-related fMRI designs. We evaluated beta-PPI using comprehensive simulations across several experimental conditions and signal qualities. Beta-PPI can sensitively detect ground-truth effects and exhibited good parameter recovery. Compared with generalized psychophysiological interaction, beta-PPI achieved comparable performance across most conditions while demonstrating improved statistical power under lower signal-to-noise conditions. We further validated beta-PPI using empirical event-related fMRI data. Distinct trial-wise cognitive variables selectively modulated functional connectivity during their corresponding trial epochs, demonstrating the temporal specificity and flexibility of the approach. By testing how trial-wise variables modulate functional connectivity, beta-PPI extends task-based connectivity analysis to model-based fMRI and provides a common single-trial framework that could facilitate the integration of connectivity, activation, and representational analyses in event-related fMRI.

neuroscience↗

bioRxiv2026

Hierarchical Reconfiguration of Neurocognitive Task Set Representations Mediates Cognitive Flexibility

Cognitive control organizes abstract contexts, stimuli, and actions into hierarchically structured representations. This organization supports flexible behavior but requires updating at multiple levels of the hierarchy, a process reflected in task switch costs. However, it remains unclear how updating differs across levels of abstraction and how these differences relate to behavior and neural representations. Here, we investigated the behavioral and neural sources of switch costs using fMRI and behavioral data from healthy adult participants (male and female). We employed a hierarchical control task that dissociates abstract context reconfiguration from more concrete task-set reconfiguration of stimulus-response mappings. We predicted that task sets, which incorporate sensory-motor mappings, would be more strongly influenced by feedforward inputs than higher-level contextual goals, which are more abstracted from immediate perceptual and motor demands. As predicted, subordinate rule switches were faster and were more strongly influenced by task-irrelevant perceptual changes, whereas context switches were slower and relatively insensitive to such interference. To characterize the neural basis of these effects, we quantified trial-to-trial reconfiguration of multivoxel activity patterns. Across the brain, larger pattern shifts predicted larger RT switch costs, linking representational reorganization to behavioral performance. Importantly, representational reconfiguration differed across hierarchical levels and anatomical systems. Subordinate rule updating was modulated by task-irrelevant perceptual features and expressed in distributed perceptual and motor networks, whereas context reconfiguration engaged the mid-lateral frontal cortex and was comparatively insulated from interference. Our results reveal how the hierarchical structure of neural representations supports flexible updating with interference-shielded contextual representations subserving behavioral control. SignificanceDaily activities often require simultaneously updating different tasks and thoughts. Driving requires maintaining a stable destination goal while rapidly updating motor plans (brake, accelerate, turn, etc.) in response to changing perceptual information (traffic lights, pedestrians, etc.). Although people perform such tasks with ease, it remains unclear how neural and cognitive representations are structured to respond to these flexibility demands. The present study suggests that sensory-motor plans prioritize flexibility by allowing greater influence from sensory inputs, which can create interference across brain networks when that input is task-irrelevant. Contextual information is insulated from this interference by representing contexts as distinctly as possible in the lateral prefrontal cortex, resulting in slower but more stable context switching.

neuroscience↗

bioRxiv2025

How Task Representations Integrate Information from Multiple Sources

Adaptive behavior requires integrating information from multiple sources. These sources can originate from distinct channels, such as internally maintained latent cognitive representations or externally presented sensory cues. Because these signals are often stochastic and carry inherent uncertainty, integration is challenging. However, the neural and computational mechanisms that support the integration of such stochastic information remain unknown. We introduce a computational neuroimaging framework to elucidate how brain systems integrate internally maintained and externally cued stochastic information to guide behavior. Neuroimaging data were collected from healthy adult human participants (both male and female). Our computational model estimates trial-by-trial beliefs about internally maintained latent states and externally presented perceptual cues, then integrates them into a unified joint probability distribution. The entropy of this joint distribution quantifies overall uncertainty, which enables continuous tracking of probabilistic task beliefs, prediction errors, and updating dynamics. Results showed that latent state beliefs are encoded in distinct regions from perceptual beliefs. Latent-state beliefs were encoded in the anterior middle frontal gyrus, mediodorsal thalamus, and inferior parietal lobule, whereas perceptual beliefs were encoded in spatially distinct regions including lateral temporo-occipital areas, intraparietal sulcus, and precentral sulcus. The integrated joint probability and its entropy converged in frontoparietal hub areas, notably middle frontal gyrus and intraparietal sulcus. These findings suggest that frontoparietal hubs read out and resolve distributed uncertainty to flexibly guide behavior, revealing how frontoparietal systems implement cognitive integration. SignificanceFlexible human behavior often depends on integrating information from multiple sources, such as memory and perception, each of which can be corrupted by noise. For example, a driver must integrate traffic signals (external cues) with their destination plan (internal goals) to decide when to turn. This study reveals how the human brain integrates multiple information sources to guide flexible behavior. More specifically, distinct brain regions encode internal beliefs and external sensory representations, while frontoparietal regions integrate this information in response to input noise. These findings provide a complete account of how the brain encodes and integrates multiple inputs to guide adaptive behavior.

neuroscience↗

bioRxiv2020

The Maryland Analysis of Developmental EEG (MADE) Pipeline

Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG. In an effort to achieve greater standardization of EEG preprocessing, and in particular for the analysis of pediatric data, we developed the Maryland Analysis of Developmental EEG (MADE) pipeline as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings. MADE uses EEGLAB and functions from some EEGLAB plugins, and includes additional customizable features particularly useful for EEG data collected from pediatric populations. MADE processes event-related and resting state EEG from raw data files through a series of preprocessing steps and outputs processed clean data ready to be analyzed in time, frequency, or time-frequency domain. MADE provides a report file at the end of the preprocessing that describes a variety of features of the processed data to facilitate the assessment of the quality of processed data. In this paper we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues. MADE is freely available under the terms of the GNU General Public License at https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline.

neuroscience↗

bioRxiv2019

Adjusting ADJUST: Optimizing the ADJUST Algorithm for Pediatric Data Using Geodesic Nets

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults using the international 10-20 system, but to our knowledge no such algorithms have been optimized for the geodesic sensor net, which is often used with infants and children. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUSTs algorithm. Our \"adjusted-ADJUST\" algorithm was compared to the \"original-ADJUST\" algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after pre-processing with each algorithm. Overall, the adjusted-ADJUST algorithm performed better than the original-ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms for data collected with a geodesic net improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations.

neuroscience↗