Biology subjects

Bissett, P. G.

Publications and source records attributed to Bissett, P. G..

7 recordsLinked to original sources

bioRxiv2026

Functional brain organization is stable within individuals across years

Brain regions exhibit dynamic yet highly coordinated activity patterns that form large-scale functional networks measurable through resting-state correlations. While their association with fluctuating activity may intuitively suggest functional networks to be temporally transient and dependent on state, a growing body of literature suggests that they are person-specific and stable across days. If truly person-specific, then functional networks should preserve unique characteristics over extended periods. To test this hypothesis, we collected longitudinal precision fMRI data ([≥]60 minutes per participant per time point) from 10 healthy young adults across 1-3 year intervals, as well as three adults over 8-13 years. We further replicated findings in the MyConnectome dataset and its 10-year follow-up. Functional network organization--when sufficient per-participant data were collected-- remained largely stable within individuals over prolonged periods of up to 13 years, suggesting that individualized brain organization constitutes persistent features of personal identity that may be supported by homeostatic mechanisms. SIGNIFICANCEWhile many aspects of brain function are portrayed as dynamic and malleable, our study provides evidence for the long-term preservation of functional organization of the healthy young adult brain. Whole-brain functional organization exhibited unique individual characteristics that were preserved for years, even over a decade. The stability of such individualized neural architecture suggests that the brain encodes features of personhood that may be invariant across contexts and temporal fluctuations. This observation reframes our understanding of brain resilience and plasticity by offering new insights into the extent that homeostatic mechanisms of brain organization can withstand stressors and environmental changes through time to maintain a constant functional organization. These insights pave the way for a more comprehensive understanding of the neural mechanisms behind this stability, with the possibility of pinpointing critical markers of healthy brain function and its disruptions.

neuroscience↗

bioRxiv2025

Frontostriatal salience network expands as executive networks contract in Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD), marked by intrusive thoughts (obsessions) and repetitive behaviours (compulsions), is linked to dysfunction in frontostriatal circuits. However, neural differences potentially contributing to these alterations are often small, and conflicting evidence obscures the directionality and underlying mechanisms of these alterations. Like many psychiatric conditions, OCD follows a fluctuating symptom trajectory, with symptoms shifting dramatically over months--either naturally or due to treatment. Yet, the absence of longitudinal neuroimaging studies limits our understanding of the neural mechanisms driving these changes. Here, we used precision functional mapping in highly sampled individuals with OCD, uncovering a striking imbalance within frontostriatal networks. We identified a twofold expansion of the salience network in these individuals and a concomitant contraction of the frontoparietal network. Notably, salience network expansion was driven by border shifts, encroaching on adjacent executive networks and leading to their contraction. This imbalance within frontostriatal networks may relate to excessive attention to internal stimuli and lack of goal-directed control, which are classically observed in OCD. Longitudinal analyses of neuroimaging data collected over several months revealed frontostriatal connectivity changes tracking symptom severity. Overall, these findings pinpoint network-level features that may confer risk for individuals with OCD and highlight dynamic connectivity shifts tied to severity of OCD symptoms over time. By isolating frontostriatal abnormalities at the individual level in OCD and their relationship to symptom severity for the first time, this work paves the way for more targeted, personalized treatment strategies and identifies precision functional mapping as a model for precision psychiatry.

neuroscience↗

bioRxiv2023

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

We describe the following shared data from N=103 healthy adults who completed a broad set cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90- minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. Subjects also completed resting state scans. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

neuroscience↗

bioRxiv2023

The response time paradox in functional magnetic resonance imaging analyses

The functional MRI (fMRI) signal is a proxy for an unobservable neuronal signal, and differences in fMRI signals on cognitive tasks are generally interpreted as reflecting differences in the intensity of local neuronal activity. However, changes in either intensity or duration of neuronal activity can yield identical differences in fMRI signals. When conditions differ in response times (RTs), it is thus impossible to determine whether condition differences in fMRI signals are due to differences in the intensity of neuronal activity or to potentially spurious differences in the duration of neuronal activity. The most common fMRI analysis approach ignores RTs, making it difficult to interpret condition differences that could be driven by RTs and/or intensity. Because differences in response time are one of the most important signals of interest for cognitive psychology, nearly every task of interest for fMRI exhibits RT differences across conditions of interest. This results in a paradox, wherein the signal of interest for the psychologist is a potential confound for the fMRI researcher. We review this longstanding problem, and demonstrate that the failure to address RTs in the fMRI time series model can also lead to spurious correlations at the group level related to RTs or other variables of interest, potentially impacting the interpretation of brain-behavior correlations. We propose a simple approach that remedies this problem by including RT in the fMRI time series model. This model separates condition differences from RT differences, retaining power for detection of unconfounded condition differences while also allowing the identification of RT-related activation. We conclude by highlighting the need for further theoretical development regarding the interpretation of fMRI signals and their relationship to response times.

neuroscience↗

bioRxiv2022

Discovering dynamical models of human behavior

Response time (RT) data collected from cognitive tasks are a cornerstone of psychology and neuroscience research, yet existing models of these data either make strong assumptions about the data generating process or are limited to modeling single trials. We introduce task-DyVA, a deep learning framework in which expressive dynamical systems are trained to reproduce sequences of RTs observed in data from individual human subjects. Models fitted to a large task-switching dataset captured subject-specific behavioral differences with high temporal precision, including task-switching costs. Through perturbation experiments and analyses of the models latent dynamics, we find support for a rational account of switch costs in terms of a stability-flexibility tradeoff. Thus, our framework can be used to discover interpretable cognitive theories that explain how the brain dynamically gives rise to behavior.

neuroscience↗

bioRxiv2020

A cautionary note on stop-signal data from the Adolescent Brain Cognitive Development study

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 10,000 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to: variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data.

neuroscience↗

bioRxiv2019

Dataset Decay: the problem of sequential analyses on open datasets

Open data has two principal uses: (i) to reproduce original findings and (ii) to allow researchers to ask new questions with existing data. The latter enables discoveries by allowing a more diverse set of viewpoints and hypotheses to approach the data, which is self-evidently advantageous for the progress of science. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives in the literature. Current practice suggests that the number of tests to be corrected is the number of simultaneous tests performed by a researcher. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. This finding is troubling because, as more researchers embrace an open dataset, the likelihood of false positives (i.e. type I errors) will increase. Thus, we should expect a datasets utility for discovering new true relations between variables to decay. We consider several sequential correction procedures. These solutions can reduce the number of false positives but, at the same time, can prompt undesired challenges to open data (e.g. incentivising restricted access).

bioinformatics↗