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Colcombe, S.

Publications and source records attributed to Colcombe, S..

11 recordsLinked to original sources
bioRxiv2026

Ongoing brain state dynamics shape spontaneous awake reactivation and its relationship with memory

Memory reactivation is thought to support memory consolidation, but how ongoing brain states shape awake reactivation and its relationship to memory remain poorly understood. Here, we asked whether spontaneous hippocampal-cortical reactivation during awake rest is embedded in ongoing brain state dynamics by examining its coupling with whole-brain activity patterns and infra-slow global signal (GS) fluctuations, and whether these dynamics shape its relationship to memory retention. Reactivation preferentially occurred during an internally oriented brain state characterized by default mode network activation and dorsal attention network suppression, as well as states that reinstated encoding-related visual activity. Reactivation also clustered around the peak of infra-slow GS fluctuations and in high GS amplitude time windows, features previously associated with lower arousal. Importantly, the relationship between reactivation and memory depended on the ongoing brain state: distinct whole-brain activity patterns expressed during reactivation were associated with opposing memory outcomes, while reactivation during high GS amplitude windows was positively associated with retention. Together, these findings show that awake memory reactivation is embedded in dynamic whole-brain states that may influence its memory outcome, suggesting that recurring brain states may provide favorable windows for awake memory consolidation.

neuroscience↗
bioRxiv2025

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

neuroscience↗
bioRxiv2022

Relative Brain Age Is Associated with Socioeconomic Status and Anxiety/Depression Problems in Youth

Socioeconomic status (SES) has been linked to differences in brain structure and psychiatric risk across the lifespan. Despite many neuropsychiatric disorders emerging in childhood, few studies have examined the influence of SES on brain aging and psychopathology in youth. We re-analyzed relative brain age (RBA) data from the Healthy Brain Network to examine the influence of SES components (parent education, occupation, household income-to-needs ratio (INR), public assistance enrollment) on RBA. RBA was previously determined using covariation patterns for cortical morphology, white, and subcortical gray matter volumes without SES in predictive models. We also examined associations between RBA and psychiatric symptoms (child behavior checklist). Full case analysis included 470 youth (5-17 years; 61.3% male), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Mean household income was 3.95{+/-}2.33 (Mean{+/-}SD) times the federal poverty threshold. Multiple linear regression examined if 1) SES components associated with RBA, and 2) RBA associated with psychiatric symptoms. Models covaried for sex, scan location, and parent psychiatric diagnoses. RBA associated with public assistance (p = 0.03), parent occupation (p = 0.01), and parent psychiatric diagnosis (p = 0.01), but not with INR and parent education. Parent occupation (p = 0.02) and RBA (p = 0.04) associated with CBCL anxiety/depression scores. Components of SES associated with brain aging, underscoring the risk of omitting these factors in developmental brain research. Further, delayed brain aging was associated with low parental occupational prestige and child anxiety/depression scores, suggesting a possible biological pathway from SES to mental health risk.

neuroscience↗
bioRxiv2021

Predicting multimodal MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6-12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following resting state fMRI scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.

neuroscience↗
bioRxiv2020

Human belief state-based exploration and exploitation in an information-selective reversal bandit task

Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.

neuroscience↗
bioRxiv2020

Reliability of dynamic network reconfiguration: Impact of code implementation, parameter selection, scan duration, and task condition

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization. HighlightsO_LIDynamic network reliability is highly dependent on many methodological decisions C_LIO_LIThe default multilayer community detection algorithm generates erroneous results C_LIO_LIReliability-optimized intra-/inter-layer coupling parameters are dataset-dependent C_LIO_LIScan duration is a much stronger determinant of reliability than scan condition C_LIO_LIMovies are the most reliable condition, requiring at least 20 min of data C_LI

neuroscience↗
bioRxiv2019

Reliability and Reproducibility of T1-weighted images: comparing MPRAGE and Prospective Motion Correction with Volumetric Navigators

New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N=348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N=71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.

neuroscience↗
bioRxiv2018

Evaluating fMRI-Based Estimation of Eye Movement during Naturalistic Viewing

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye-tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eyes orbit using a 1.5-minute calibration scan. Here, we provide confirmatory validation of the PEER methods ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2{degrees}). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of two movies is being watched based on the predicted eye gaze patterns (area under the curve = .90 {+/-} .02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

neuroscience↗
bioRxiv2017

Assessment of the impact of shared data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used pay to play models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

bioinformatics↗
bioRxiv2017

Delineating the macroscale areal organization of the macaque cortex in vivo

Complementing longstanding traditions centered around histology, fMRI approaches are rapidly maturing in delineating brain areal organization at the macroscale. The non-human primate (NHP) provides the opportunity to overcome critical barriers in translational research. Here, we establish the data and scanning conditions for achieving reproducible, stable and internally valid areal parcellations in individuals. We demonstrate that these functional boundaries serve as a functional fingerprint of the individual animals, and can be achieved under anesthesia or awake conditions (rest, naturalistic viewing), though differences between awake and anesthetized states precluded the detection of individual differences across states. Comparison of awake and anesthetized states suggested a more nuanced picture of changes in connectivity for higher order association areas, as well as visual and motor cortex. These results establish feasibility and data requirements for the generation of reproducible individual-specific parcellations in NHP, as well as provide insights into the impact of scan state and motivate efforts toward harmonizing protocols.

neuroscience↗