Biology subjects

Faskowitz, J.

Publications and source records attributed to Faskowitz, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

How much data do we need? Lower bounds of brain activation states to predict human cognitive ability

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individuals network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

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bioRxiv2022

Redefining the connectome: A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal (combining diffusion and functional MRI data) regression-based, explanatory model that endows reconstructed fiber tracts with directed and signed weights. Benchmarking this method on Human Connectome Project data, we find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples. Next, we analyze the resulting network using graph-theoretic tools from network neuroscience, revealing bilaterally symmetric communities that span cerebral hemispheres. These communities exhibit a clear mapping onto known functional systems. We also study the shortest paths structure of this network, discovering that almost every edge participates in at least one shortest path. We also find evidence of robust asymmetries in edge weights, that the network reconfigures in response to naturalistic stimuli, and that estimated edge weights differ with age. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

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bioRxiv2022

System-level high-amplitude co-fluctuations

Edge time series decompose interregional correlations (functional connectivity; FC) into their time-varying contributions. Previous studies have revealed that brief, high-amplitude, and globally-defined "events" contribute disproportionately to the time-averaged FC pattern. This whole-brain view prioritizes systems that occupy vast neocortical territory, possibly obscuring extremely high-amplitude co-fluctuations that are localized to smaller brain systems. Here, we investigate local events detected at the system level, assessing their independent contributions to global events and characterizing their repertoire during resting-state and movie-watching scans. We find that, as expected, global events are more likely to occur when large brain systems exhibit events. Next, we study the co-fluctuation patterns that coincide with system events-i.e. events detected locally based on the behavior of individual brain systems. We find that although each system exhibits a distinct co-fluctuation pattern that is dissimilar from those associated with global events, the patterns can nonetheless be grouped into two broad categories, corresponding to events that coincide with sensorimotor and attention systems and, separately, association systems. We then investigate system-level events during movie-watching, discovering that the timing of events in sensorimotor and attention systems decouple, yielding reductions in co-fluctuation amplitude. Next, we show that by associating each edge with its most similar system-averaged edge time series, we recover overlapping community structure, obviating the need for applying clustering algorithms to high-dimensional edge time series. Finally, we focus on cortical responses to system-level events in subcortical areas and the cerebellum. We show that these structures coincide with spatially distributed cortical co-fluctuations, centered on prefrontal and somatosensory systems. Collectively, the findings presented here help clarify the relative contributions of large and small systems to global events, as well as their independent behavior.

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bioRxiv2022

Synchronous high-amplitude co-fluctuations of functional brain networks during movie-watching

Recent studies have shown that functional connectivity can be decomposed into its exact framewise contributions, revealing short-lived, infrequent, and high-amplitude time points referred to as "events." Events contribute disproportionately to the time-averaged connectivity pattern, improve identifiability and brain-behavior associations, and differences in their expression have been linked to endogenous hormonal fluctuations and autism. Here, we explore the characteristics of events while subjects watch movies. Using two independently-acquired imaging datasets in which participants passively watched movies, we find that events synchronize across individuals and based on the level of synchronization, can be categorized into three distinct classes: those that synchronize at the boundaries between movies, those that synchronize during movies, and those that do not synchronize at all. We find that boundary events, compared to the other categories, exhibit greater amplitude, distinct co-fluctuation patterns, and temporal propagation. We show that underlying boundary events is a specific mode of co-fluctuation involving the activation of control and salience systems alongside the deactivation of visual systems. Events that synchronize during the movie, on the other hand, display a pattern of co-fluctuation that is time-locked to the movie stimulus. Finally, we found that subjects time-varying brain networks are most similar to one another during these synchronous events.

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bioRxiv2022

Multi-policy models of interregional communication in the human connectome

Network models of communication, e.g. shortest paths, diffusion, navigation, have become useful tools for studying structure-function relationships in the brain. These models generate estimates of communication efficiency between all pairs of brain regions, which can then be linked to the correlation structure of recorded activity, i.e. functional connectivity (FC). At present, however, communication models have a number of limitations, including difficulty adjudicating between models and the absence of a generic framework for modeling multiple interacting communication policies at the regional level. Here, we present a framework that allows us to incorporate multiple region-specific policies and fit them to empirical estimates of FC. Briefly, we show that many communication policies, including shortest paths and greedy navigation, can be modeled as biased random walks, enabling these policies to be incorporated into the same multi-policy communication model alongside unbiased processes, e.g. diffusion. We show that these multi-policy models outperform existing communication measures while yielding neurobiologically interpretable regional preferences. Further, we show that these models explain the majority of variance in time-varying patterns of FC. Collectively, our framework represents an advance in network-based communication models and establishes a strong link between these patterns and FC. Our findings open up many new avenues for future inquiries and present a flexible framework for modeling anatomically-constrained communication.

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bioRxiv2022

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Edge time series decompose FC into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames, including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations. Here, we address those questions directly, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multi-scale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.

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bioRxiv2022

Multi-modal and multi-subject modular organization of human brain networks

The human brain is a complex network of anatomically interconnected brain areas. Spontaneous neural activity is constrained by this architecture, giving rise to patterns of statistical dependencies between the activity of remote neural elements. The non-trivial relationship between structural and functional connectivity poses many unsolved challenges about cognition, disease, development, learning and aging. While numerous studies have focused on statistical relationships between edge weights in anatomical and functional networks, less is known about dependencies between their modules and communities. In this work, we investigate and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi-layer framework that expands the classical concept of multi-layer modularity optimization. By simultaneously mapping anatomical and functional networks estimated from different subjects into communities, this approach allows us to carry out a multi-subject and multi-modal analysis of the brains modular organization. Here, we investigate the relationship between anatomical and functional modules during resting state, finding unique and shared structures. The proposed framework constitutes a methodological advance in the context of multi-layer network analysis and paves the way to further investigate the relationship between structural and functional network organization in clinical cohorts, during cognitively demanding tasks, and in developmental or lifespan studies.

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bioRxiv2021

Cortico-Subcortical Interactions in Overlapping Communities of Edge Functional Connectivity

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

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bioRxiv2021

Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

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bioRxiv2021

High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and both brain activity and connectivity. However, how hormonal fluctuations impact fast changes in brain network structure over short timescales remains unknown. Here, we leverage "edge time series" analysis to investigate the relationship between high-amplitude network states and quotidian variation in sex steroid and gonadotropic hormones in a single individual sampled over the course of two endocrine states, across a natural menstrual cycle and under a hormonal regimen. We find that the frequency of high-amplitude network states are associated with follicle-stimulating and luteinizing hormone, but not the sex hormones estradiol and progesterone. Nevertheless, we show that scan-to-scan variation in the co-fluctuation patterns expressed during network states are robustly linked with the concentration of all four hormones, positing a network-level target of hormonal control. We conclude by speculating on the role of hormones in shaping ongoing brain dynamics.

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bioRxiv2021

Local structure-function relationships in human brain networks across the human lifespan

The human connectome is the set of physical pathways linking brain regions to one another. Empirical and in silico studies have demonstrated that the structure of this network helps shape patterns of functional coupling between brain regions. To better understand this link between structure and function, a growing number of studies have derived geometric, dynamic, and topological predictors from structural connectivity in order to make predictions about correlation structure. These studies, however, have typically focused on global (whole-brain) predictions using a restricted set of predictors. Here, we investigate a wide range of predictors and shift focus onto predictions of local (regional) patterns of functional coupling. We show that, globally, no individual predictor performs well and, that even the best predictors are largely driven by their ability to predict functional coupling between directly connected regions. We then use the same predictors to make predictions of local coupling and find marked improvement. Notably, the most predictable local FC is linked to sensorimotor regions, which are best predicted by measures based on topological similarity, mean first passage times of random walkers, and the brains embedding in Euclidean space. We then show that by combining the predictive power of more than one predictor using multi-linear models, we can further improve local predictions. Finally, we investigate how global and local structure-function coupling differences across the human lifespan. We find that, globally, the magnitude of coupling decreases with biological age, which is paralleled by an increase in the number of multi-step pathways. We also show that, locally, structure-function coupling is preserved in higher order cognitive systems, but preferentially decreases with age in sensorimotor systems. Our results illuminate the heterogeneous landscape of structure-function coupling across the cerebral cortex and help clarify its differences with age.

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bioRxiv2021

Effects of diffusion signal modeling and segmentation approaches on subthalamic nucleus parcellation

The subthalamic nucleus (STN) is commonly used as a surgical target for deep brain stimulation in movement disorders such as Parkinsons Disease. Tractography-derived connectivity-based parcellation (CBP) has been recently proposed as a suitable tool for non-invasive in vivo identification and pre-operative targeting of specific functional territories within the human STN. However, a well-established, accurate and reproducible protocol for STN parcellation is still lacking. The present work aims at testing the effects of different tractography-based approaches for the reconstruction of STN functional territories. We reconstructed functional territories of the STN on the high-quality dataset of 100 unrelated healthy subjects and on the test-retest dataset of the Human Connectome Project (HCP) repository. Connectivity-based parcellation was performed with a hypothesis-driven approach according to cortico-subthalamic connectivity, after dividing cortical areas into three groups: associative, limbic and sensorimotor. Four parcellation pipelines were compared, combining different signal modeling techniques (single-fiber vs multi-fiber) and different parcellation approaches (winner takes all parcellation vs fiber density thresholding). We tested these procedures on STN regions of interest obtained from three different, commonly employed, subcortical atlases. We evaluated the pipelines both in terms of between-subject similarity, assessed on the cohort of 100 unrelated healthy subjects, and of within-subject similarity, using a second cohort of 44 subjects with available test-retest data. We found that each parcellation provides converging results in terms of location of the identified parcels, but with significative variations in size and shape. Higher between-subject similarity was found with multi-fiber signal modeling techniques combined with fiber density thresholding. All the pipelines obtained very high within-subject similarity, with tensor-based approaches outperforming multi-fiber pipelines. We suggest that a fine-tuning of tractography-based parcellation may lead to higher reproducibility and aid the development of an optimized surgical targeting protocol.

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bioRxiv2021

Mapping structure to function and behavior with individual-level connectome embedding

The connectome, a comprehensive map of the brains anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes "context" is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

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bioRxiv2021

An edge-centric model for harmonizing multi-relational network datasets

Functional and structural connections vary across conditions, measurements, and time. However, how to resolve multi-relational measures of connectivity remains an open challenge. Here, we propose an extension of structural covariance and morphometric similarity methods to integrate multiple estimates of connectivity into a single edge-centric network representation. We highlight the utility of this method through two applications: an analysis of multi-task functional connectivity data and multi-measure structural networks. In these analyses, we use data-driven clustering techniques to identify collections of edges that covary across tasks and measures, revealing overlapping mesoscale architecture. We also link these features to node-level properties such as modularity and canonical descriptors of brain systems. We further demonstrate that, in the case of multi-task functional networks, edge-level features are consistent across individuals yet exhibit subject-specificity. We conclude by highlighting other instances where the edge-centric model may be useful.

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bioRxiv2020

Subject identification using edge-centric functional connectivity

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed "fingerprinting" analyses on functional connectivity to identify subjects idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

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bioRxiv2020

Collegiate athlete brain data for white matter mapping and network neuroscience

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

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bioRxiv2020

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

Functional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

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bioRxiv2020

The diversity and multiplexity of edge communities within and between brain systems

The human brain is composed of regions that can be grouped into functionally specialized systems. These systems transiently couple and decouple across time to support complex cognitive processes. Recently, we proposed an edge-centric model of brain networks whose elements can be clustered to reveal communities of connections whose co-fluctuations are correlated across time. It remains unclear, however, how these co-fluctuation patterns relate to traditionally-defined brain systems. Here, we address this question using data from the Midnight Scan Club. We show that edge communities transcend traditional definitions of brain systems, forming a multiplexed network in which all pairs of brain systems are linked to one another by at least two distinct edge communities. Mapping edge communities back to individual brain regions and deriving a novel distance metric to describe the similarity of regions "edge community profiles", we then demonstrate that the within-system similarity of profiles is heterogeneous across systems. Specifically, we find that heteromodal association areas exhibit significantly greater diversity of edge communities than primary sensory systems. Next, we cluster the entire cerebral cortex according to the similarity of regions edge community profiles, revealing systematic differences between traditionally-defined systems and the detected clusters. Specifically, we find that regions in heteromodal systems exhibit dissimilar edge community profiles and are more likely to form their own clusters. Finally, we show show that edge communities are highly personalized and can be used to identify individual subjects. Collectively, our work reveals the pervasive overlap of edge communities across the cerebral cortex and characterizes their relationship with the brains system level architecture. Our work provides clear pathways for future research using edge-centric brain networks to investigate individual differences in behavior, development, and disease.

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