Biology subjects

Seitzman, B. A.

Publications and source records attributed to Seitzman, B. A..

8 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↗

bioRxiv2022

The cingulo-opercular network is composed of two distinct sub-systems

The cingulo-opercular (CO) network and its two best studied regions - the dorsal anterior cingulate and anterior insula - have been linked to task control, but also implicated in many additional processes across cognitive, social, and emotional domains. However, most prior work investigating the CO network has used a group-average approach, which may mix signals across nearby regions that vary across individuals. Here, we reevaluate the CO networks role in task control with both task and rest fMRI, using regions with a high probability of CO network agreement across individuals. Hierarchical clustering analyses suggest heterogeneity in the CO networks task response properties, with one sub-system (CO1) showing consistency with prior task control characterizations while another sub-system (CO2) has weak task control responses, but preserved ties to pain and motor functions. Resting-state connectivity confirms subtle differences in the architecture of these two sub-systems. This evidence suggests that, when individual variation in network locations is addressed, the CO network includes (at least) two linked sub-systems with differential roles in task control and other cognitive/motor/interoceptive responses, which may help explain varied accounts of its functions. We propose that this fractionation may reflect expansion of primary CO body-oriented control functions to broader domain-general contexts.

neuroscience↗

bioRxiv2022

BOLD cofluctuation 'events' are predicted from static functional connectivity

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; [~]50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure. HIGHLIGHTSO_LIPast results suggested high cofluctuation BOLD "events" drive fMRI functional connectivity, FC C_LIO_LIHere, events were examined in both real fMRI data and a stationary null model to test this model C_LIO_LIIn real data, >50% of BOLD timepoints show high modularity and similarity to time- averaged FC C_LIO_LIStationary null models identified events with similar behavior to real data C_LIO_LIEvents may not be a transient driver of static FC, but rather an expected outcome of it. C_LI

neuroscience↗

bioRxiv2021

Two common and distinct forms of variation in human functional brain networks

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most prior work treats all individual differences as equivalent and primarily linked to boundary shifts between the borders of regions, here we show that cortical variants actually occur in two different forms. In addition to border shifts, variants also occur at a distance from their typical position, forming ectopic intrusions. Both forms of variants are common across individuals, but the forms differ in their location, network associations, and activations during tasks, patterns that replicate across datasets and methods of definition. Border shift variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. Further, variant properties are categorically different between subgroups of individuals. Exploratory evidence suggests that variants can predict individual differences in behavior, but the two forms differ in which behavioral phenotypes they predict. This work argues that individual differences in brain organization commonly occur in two dissociable forms - border shifts and ectopic intrusions - suggesting that these types of variation are indexing distinct forms of cortical variation that must be separately accounted for in the analysis of cortical systems across people. This work expands our knowledge of cortical variation in humans and helps reconceptualize the discussion of how cortical systems variability arises and links to individual differences in cognition and behavior.

neuroscience↗

bioRxiv2020

Probabilistic mapping of human functional brain networks identifies regions of high group consensus

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brains large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in future group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

neuroscience↗

bioRxiv2020

Network variants are similar between task and rest states

Recent work has demonstrated that individual-specific variations in functional networks (that we call "network variants") can be identified in individuals using functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time with resting-state fMRI data. These properties have suggested that network variants may be trait-like markers of individual differences in brain organization. Another test of this conclusion would be to examine if network variants are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in different states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.

neuroscience↗

bioRxiv2020

Cognitive Deficits and Altered Functional Brain Network Organization in Pediatric Brain Tumor Patients

Pediatric brain tumor survivors experience significant cognitive sequelae from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Large-scale, distributed brain systems provide a window into brain organization and function that may yield insight into these mechanisms and outcomes. Here, we evaluated functional network architecture, cognitive performance, and brain-behavior relationships in pediatric brain tumor patients. Patients ages 4-18 years old with diagnosis of a brain tumor underwent awake resting state fMRI during regularly scheduled clinical visits and were tested with the NIH Toolbox Cognition Battery. We observed that functional network organization was significantly altered in patients compared to age- and sex-matched healthy controls, with the integrity of the dorsal attention network particularly affected. Moreover, patients demonstrated significant impairments in multiple domains of cognitive performance, including attention. Finally, a significant amount of variance of age-adjusted total composite scores from the Toolbox was explained by changes in segregation between the dorsal attention and default mode networks. Our results suggest that changes in functional network organization may provide insight into long-term changes in cognitive function in pediatric brain tumor patients.

neuroscience↗

bioRxiv2018

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011 and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

neuroscience↗