Functional neuroimaging is an essential tool for neuroscience research. However, post-processing is not standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues.

We investigated the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-assocation axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3,355; ages 5-23 years). In each dataset, the development of functional connectivity systematically varied along the S-A axis. These robust and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Symptoms of Borderline Personality Disorder (BPD) often manifest in adolescence, yet the relationship between these debilitating symptoms and functional networks is not well understood. Here we investigated how multivariate patterns of functional connectivity are associated with symptoms of BPD in a large sample of young adults and adolescents. We found that functional connectivity predicts symptoms of BPD in young adults and adolescents. Moreover, findings demonstrated that brain regions whose functional connectivity develops the most in youth align with those associated with BPD, providing evidence for understanding BPD as a neurodevelopmental disorder.

We sought to delineate the relationship between white-matter maturation and executive function, a higher-order cognitive function that undergoes extensive changes during youth. Increasingly, the field of neuroimaging is shifting toward data-driven machine-learning methods, but there have been few applications to developing white matter. Here, we leveraged machine-learning to delineate how white-matter networks develop in a large sample of youth from the Philadelphia Neurodevelopmental Cohort. We find 14 different white-matter networks that each have distinct spatial organization and maturational trajectories. These data-driven patterns of white-matter development are also linked to developmental changes in executive function, confirming that white-matter networks play a critical role in the maturation of higher-order cognition.

In this systematic review of fMRI studies examining acute responses to experimentally administered psychedelics, we found both substantial evidence of methodological heterogeneity and convergent results.

Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. Analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Further analyses suggested that delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

Hierarchical processing requires activity propagating between higher and lower-order cortical areas. Here, we leveraged advances in neuroimaging and computer vision to describe propagations robustly ascend and descend the cortical hierarchy. Notably, top-down propagations become both more prevalent with cognitive control demands and with development in youth.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Finally, we conducted extensive quality control. This work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health,

Connectivity-guided transcranial magnetic stimulation applied to the ventrolateral prefrontal cortex elicited acute modulations of amygdala fMRI activity, demonstrating therapeutic potential for emotion-related psychopathology.

We demonstrate that statistical patterns of smartphone-based mobility features represent unique “footprints” that allow individual identification. Critically, mobility footprints exhibit varying levels of person-specific distinctiveness and are associated with individual differences in affective instability, circadian variability, and brain functional connectivity.

In this study we found that lower neighborhood-level SES is associated with reduced activation of the executive system, even when controlling for other measures of developmental adversity.

We review how human brain maturation progresses along an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This spatiotemporal developmental program endows association cortices with a protracted period of plasticity, unique neurobiological properties, and heightened maturational variability linked to psychopathology.

Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

We used ultra-high field (7T) glutamate imaging to delineate associations between reward network neurochemistry and transdiagnostic reward responsiveness deficits.

This review provides an overview of methods for defining common and dissociable aspects of psychopathology

Individual-specific association networks are refined in youth and predict cognition

Diminished brain iron accumulation in the basal ganglia is associated with poorer cognitive performance during adolescence.

We used a semi-supervised machine learning tool to parse heterogeneity in mood and anxiety disorders in youth

We define a structured protocol for the evaluation and mitigation of motion artifact in functional connectivity MRI.

Prematurity is associated with the deficit in executive function and abnormalities in structural covariance networks.

In collaboration with Joe Kable, we demonstrate that impulsive choice during adolescence is associated with reduced cortical thickness in regions critical for reward valuation.

Higher levels of overall psychopathology across disorders are associated with differences in cerebral blood flow in key brain regions such as the anterior cingulate cortex.

In collaborative work with Danielle Bassett, we uncover novel developmental network features in high-resolution network hypergraphs.

We review recent work investigating the neural basis for executive development during childhood and adolescence.

We document structural brain abnormalities in youth with psychosis-spectrum symptoms, including medial temporal lobe regions such as the hippocampus.

Similar reward system deficits are dimensionally present in both unipolar and bipolar depression.

This provides an overview of the imaging procedures of the PNC, a large-scale NIMH-funded study (PI: Raquel E. Gur).

We provide one of the first descriptions of motion artifact's impact on functional connectivity.

Network neuroscience is principally concerned with studying the connectome, the complete description of whole brain connectivity. This connectome is often encoded as a graph of nodes interconnected by edges that can be defined across multiple scales, species, and data modalities. In any case, these descriptions of brain connectivity give rise to complex topology, and understanding how this topology shapes and constrains the brain’s rich repertoire of dynamics is a central goal of network neuroscience. NCT provides a simple yet powerful approach to studying these dynamics that yields insights into how they emerge from this topology.

If you're a neuroscientist who wants to use DSI to look at some cool microstructural details but can't afford to add the extra 20 minutes of scan time to your protocol, compressed sensing might be your new best friend!

We show that CS-DSI schemes can generate white matter derivatives comparable to those generated by a full DSI scheme, allowing up to a 60% decrease in scan time with minimal loss in accuracy or reliability! This could allow both researchers and even clinicians to harness the advantages of DSI sequences that were previously impractical to deploy.

We report a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both FSL's Eddy and QSIPrep's SHOREline methods perform remarkably well. However, we show that performance can be impacted by sampling scheme, the pervasiveness of head motion, and the denoising strategy applied before head motion correction. Our study also provides an open and fully-reproducible workflow that could be used to accelerate evaluation studies of other dMRI processing methods in the future.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Quantifying variation in the total spatial representation of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks that account for inter-individual heterogeneity in 9-10 year olds from the Adolescent Brain Cognitive Development Study. Across thousands of individuals, the total cortical representation of personalized networks could predict youth cognition. These results establish that heterogeneity in functional network topography is associated with individual differences in cognition before the critical transition into adolescence.

We propose that hierarchical development along the S–A axis in humans is driven by a cascade of critical periods that culminate in association cortices during adolescence.
We also highlight advances in in vivo neuroimaging and computational approaches that can provide insights into the development of critical period mechanisms along the S–A axis in humans.

Leveraging intrinsic activity amplitude as a functional marker of plasticity, we find evidence for a hierarchical gradient of cortical neurodevelopment in youth. Declines in the amplitude of intrinsic activity were initiated heterochronously across regions, coupled to the maturation of a plasticity-breaking factor, impacted by children’s developmental environments, and temporally staggered along a sensorimotor-association axis from ages 8 to 18.

How are developmental improvements in youth executive function supported by hierarchically organized maturational changes in functional brain systems? We highlight evidence that functional brain systems are embedded within a hierarchical sensorimotor-association axis and review evidence that functional system development varies along this axis. Developmental changes that strengthen the hierarchical organization of the cortex may support EF by facilitating top-down information flow and balancing within- and between-system communication.

CuBIDS is a neuroinformatics workflow and open-source Python-based software package designed to facilitate reproducible curation of neuroimaging data. CuBIDS also helps users summarize, categorize, and visualize the metadata heterogeneity present in their BIDS data, test pipelines on their dataset's entire parameter space, and perform metadata-based quality control.

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for non-invasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data.

Leveraging a large sample and advances in machine learning, we demonstrate that functional topography significantly predicts individual differences in diverse dimensions of psychopathology.

We develop and test a theory of how brain network architecture and biology perform lossy compression to support efficient communication among spatially distributed brain regions.

We found that average controllability better predicted the symptoms of psychosis spectrum than commonly used graph-theoretic summaries of regional structural connectivity. We found that this improved prediction was driven by regions located in the transmodal cortex.

We examined how transdiagnostic dimensions of psychopathology tracked deviations from normative neurodevelopment of gray matter volume. We found that modeling cortical volume using normative models improved out-of-sample prediction of participants' psychopathology symptoms.

A comprehensive meta-analysis revealed that chronic pain does not appear to be associated with consistent alterations in the brain’s response to noxious stimuli.

We find key advantages of multi-shell diffusion metrics, including increased neurodevelopmental coupling and decreased spurious relationships with imaging data quality.

We leveraged advances in network control theory to describe how the structural connectome facilitates transitions to executive activation states

Coupling between structural and functional brain networks evolves in a hierarchy-dependent manner in youth

Irritability is associated with accelerated cortical thinning in youth

Machine-learning links dimensions of psychopathology to abnormalities of functional brain networks.

We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.

In collaborative work with Danielle Bassett, we show that brain networks become more controllable with development in adolescence.

We benchmark 14 of the most commonly used preprocessing pipelines for functional connectivity. This paper was one of the top 3 cited papers in 2017 published in NeuroImage.

In collaborative work with Christos Davatzikos, we define novel structural covariance networks, and show that their development during adolescence is associated with evolutionary expansion.

Here we introduce a new measure --cortical coupling-- that describes the local association between brain measures.

We investigated executive system deficits in association with dimensions of psychiatric symptoms in youth using a working memory paradigm.

Here we show that cortical thinning in adolescence is partially governed by sulcal topology.

Using fMRI and machine learning tools, we demonstrate that executive system activation is more strongly linked to individual differences in performance ability than chronological age.

We evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortex’s sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.

Despite the importance of the environment in shaping individual differences in cognitive neurodevelopment, it has been challenging to quantify the many interconnected features of a child's environment ('exposome'). Here, we leverage a large-scale dataset to investigate to demonstrate that the exposome is associated with individual differences in functional brain network organization and cognition. We find that models trained on a single variable capturing a child′s exposome can more accurately and parsimoniously predict future cognitive performance than models trained on a wealth of personalized neuroimaging data. This highlights the importance of childhood environments in shaping neurocognitive development.

BABS is a user-friendly and generalizable Python package for reproducible image processing at scale. Leveraging DataLad and the FAIRly big framework, BABS allows the reproducible application of BIDS Apps to large-scale datasets on HPC clusters (SGE and Slurm). Comprehensive documentation of BABS can be found at: https://pennlinc-babs.readthedocs.io/

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Here, we investigate how white matter network disruption is related to depression in MS.

Iron is essential to brain function, and iron deficiency during youth may adversely impact neurodevelopment. We mapped the development of iron status in a sample of nearly 5,000 youth. We show that iron status evolves during youth and is lowest in females and individuals of low socioeconomic status during adolescence. Diminished iron status during adolescence has consequences for neurocognition, suggesting that this critical period of neurodevelopment may be an important window for intervention that has the potential to reduce health disparities in at-risk populations.

ModelArray is a scalable R package for statistical analysis of fixel-wise data derived from diffusion MRI (and beyond). It supports linear and nonlinear modeling and is extensible to more models. Full documentation: https://pennlinc.github.io/ModelArray/

We used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy indicating that bottom-up transitions were easier to complete compared to top-down transitions.

We identified normative developmental sex differences in the functional topography of personalized association networks including the ventral attention network and default mode network. Furthermore, chromosomal enrichment analyses revealed that sex differences in multivariate patterns of functional topography were spatially coupled to the expression of X- linked genes as well as astrocytic and excitatory neuronal cell-type signatures. These results highlight the role of sex as a biological variable in shaping functional brain development in youth.

Caregiving plays an important role in supporting cognitive development. We characterized replicable yet specific associations between caregiver monitoring, but not caregiver warmth, and cognition in two large sub-samples of 9-10 year olds. Caregiver monitoring partially mediated the association between household income and cognition, furthering our understanding of how socioeconomic disparities may contribute to disadvantages in cognitive development.

Using multi-scale personalized functional networks in a large sample of youth, we demonstrate that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we demonstrate that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function.

Using a combination of pharmacological fMRI data developmental fMRI data, we provide evidence for a developmental reduction of the excitation:inhibition ratio during adolescence that is specific to association cortex.

Curating your imaging data into BIDS (Brain Imaging Data Structure) can be a time consuming, repetitive, and error prone task, particularly on large scale neuroimaging databases like Flywheel. We developed FlywheelTools, a toolkit of software packages, to accelerate the reproducible curation of imaging data on Flywheel.

We demonstrate that non-invasive imaging proxies of neurovascular coupling in children undergo substantial maturation in youth, with evidence of both prominent sex differences and functional relevance to executive function.

We identified neurocognitive subtypes of depressed youth using semi-supervised machine learning.

Diverse experiments inducing acute pain in healthy volunteers recruit a convergent pattern of activation.

We designed, implemented, and deployed a multi-variate statistical tool to extract novel brain‐behavior relationships that are present on multiple scales.

Dissociable impact of dimensions of psychopathology on brain structure

We a recently-developed stochastic block model that simultaneously uncovers modular and core–periphery structure

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI.

We assessed quantitative metrics of structural image quality, and described how image quality may impact inference about brain development.

This review article provides an overview of the way in-scanner motion impacts functional connectivity data, as well as common approaches for mitigating this artifact.

Structural brain networks grow more segregated during adolescent development, and support executive functioning.

We delineate common functional connectivity abnormalities in the brain's reward system across mood and psychotic disorders.

We demonstrate that normative developmental sex differences in perfusion of brain regions involved in emotion and social cognition -- such as the amygdala-- mediate established sex differences in mood and anxiety symptoms in youth.

In collaborative work with Danielle Bassett, we track how the roles of functional sub-networks evolve in adolescence.

We delineate fundamental normative sex differences in CBF evolution during adolescence.

We introduce a top-performing strategy for reducing motion artifact in functional connectivity data.