Biology subjects

Dworetsky, A.

Publications and source records attributed to Dworetsky, A..

13 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↗

bioRxiv2026

Parallel processing chains span cytoarchitectures to organize association cortex

Task fMRI1 and electrophysiology2 have revealed distributed, linked cortical patches with shared category preferences (e.g., faces, objects, places)1,3-5, smaller than cytoarchitectonic areas. Resting-state functional connectivity (RSFC) similarly showed that somato-cognitive action network (SCAN) nodes interleave with effectors (foot, hand, mouth), subdividing the precentral gyrus6. Here, using multiple precision functional mapping (PFM) modalities (RSFC, task, lags), we discovered that most of association cortex is organized like face processing and SCAN, with small, discrete patches interconnected into chains. Such patch-chains densely tile prefrontal cortex but are largely absent from primary cortex. Cortico-striatal connectivity is organized such that patches of the same chain connect to the same striatal location. Within chains, infra-slow fMRI signals are ordered in time. RSFC-defined chains align with task fMRI localizers (e.g., visual, motor, pain). Chains are absent at birth and emerge in the first year of life, suggesting their formation is at least partially experience-driven. Cytoarchitectonic areas are subdivided by patches, and patches in the same chain are distributed across different cytoarchitectures. Chains represent parallel ordered processing streams that are separated by information domain and behavioral goals, not cytoarchitectonics. Functional subdivision of architectonics into smaller patches, interlinked to form cross-architecture chains, enable greater parallelization and flexible specialization of processing.

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bioRxiv2025

Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex

Dominant models of human lateral prefrontal cortex (LPFC) organization emphasize broad domain-general zones and smooth functional gradients. However, these models rely heavily on group-averaged neuroimaging, which can obscure fine-scale cortical features - especially in highly inter-individually variable regions like the LPFC. To address this limitation, we collected a new precision fMRI dataset from 10 individuals, each with approximately 2 hours of resting-state and 6 hours of task data. We mapped individual-specific LPFC networks using resting-state fMRI and tested network-level functional preferences using task fMRI. We found that individual LPFC organization differed markedly from group-averaged estimates. Individual maps showed more fragmented and interdigitated networks - especially in anterior LPFC - including novel conserved motifs present across individuals. Task fMRI revealed that distinct but adjacent networks support domain-specific processes (i.e., language, social cognition, episodic projection) versus domain-general control. Sharp functional boundaries were visible at the individual level that could not be observed in group data. These findings uncover previously hidden organizational principles in the LPFC and offer a framework for understanding how the LPFC supports flexible, complex cognition through a finely organized architecture.

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bioRxiv2025

Towards Precision Functional Brain Network Mapping in Parkinsons Disease

BackgroundParkinsons disease (PD) is a complex neurodegenerative condition that leads to widespread disruption of large-scale brain networks and is further complicated by substantial individual variability in symptomology, progression rates, and treatment response. Consequently, the investigation of individual differences in networks measured via resting state functional connectivity (RSFC) may provide insight. However, most RSFC studies are unable to identify interindividual differences due to poor reliability and group average network definitions. "Precision" RSFC addresses these shortcomings through extended data collection, strict denoising, and individual network definition, but remains untested in PD. ObjectivesTo evaluate the feasibility and reliability of precision RSFC studies in PD. MethodsWe collected >100 minutes of RSFC data from 20 PD and 6 healthy controls participants. We evaluated the level of motion, reliability and stability of RSFC measures in each participant and contrasted these measures between the PD and HC groups, as well as compared to a conventional 5 minutes of RSFC data. In addition, we created individualized brain network measures in PD participants to establish feasibility in this population. ResultsUsing precision methods, the PD group produced reliable and stable RSFC measures of brain networks of similar quality to healthy controls and substantially better than conventional methods. Individualized network maps from individuals with PD demonstrate differences from group averages and from each other, including in key motor systems. ConclusionPrecision RSFC is feasible and reliable in individuals with PD. This approach holds promise for advancing personalized diagnostics and identifying brain-based biomarkers underlying clinical variability in PD.

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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.

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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

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bioRxiv2022

A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography

The brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of precision functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision network maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), an overlapping template matching method for identifying integration zones, as well as an unsupervised community detection algorithm (Infomap). From these individualized maps we also generated probabilistic network maps and integration zones for two demographically-matched groups of n[~]3000 each. We demonstrate high reproducibility between groups (Pearsons r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Brain Atlas allowed us to derive a set of brain regions that are largely invariant in network topography across populations, which provides more reproducible statistical maps of executive function in brain-wide associations. We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Brain Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.

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bioRxiv2021

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector hubs are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

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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.

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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.

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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.

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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.

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bioRxiv2019

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

Denoising fMRI data requires assessment of frame-to-frame head motion and removal of the biases motion introduces. This is usually done through analysis of the parameters calculated during retrospective head motion correction (i.e., motion parameters). However, it is increasingly recognized that respiration introduces factitious head motion via perturbations of the main (B0) field. This effect appears as higher-frequency fluctuations in the motion parameters (> 0.1 Hz, here referred to as HF-motion), primarily in the phase-encoding direction. This periodicity can sometimes be obscured in standard single-band fMRI (TR 2.0 - 2.5 s.) due to aliasing. Here we examined (1) how prevalent HF-motion effects are in seven single-band datasets with TR from 2.0 - 2.5 s and (2) how HF-motion affects functional connectivity. We demonstrate that HF-motion is relatively trait-like and more common in older adults, those with higher body mass index, and those with lower cardiorespiratory fitness. We propose a low-pass filtering approach to remove the contamination of high frequency effects from motion summary measures, such as framewise displacement (FD). We demonstrate that in most datasets this filtering approach saves a substantial amount of data from FD-based frame censoring, while at the same time reducing motion biases in functional connectivity measures. These findings suggest that filtering motion parameters is an effective way to improve the fidelity of head motion estimates, even in single band datasets. Particularly large data savings may accrue in datasets acquired in older and less fit participants. Highlights- Single-band fMRI motion traces show factitious high-frequency content (HF-motion) - The magnitude of HF-motion relates to age and other demographic factors - HF-motion elevates framewise displacement (FD) and causes data loss - Substantial fMRI data can be recovered from censoring by filtering motion traces - Filtering motion traces reduces motion artifacts in functional connectivity

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