Biology subjects

Roalf, D. R.

Publications and source records attributed to Roalf, D. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Olfactory Deficits and Structural Anomalies of the Peripheral Olfactory System in 22q11.2 Deletion Syndrome

Background: Approximately 25% of individuals with 22q11.2 deletion syndrome (22q11DS) develop a psychotic disorder, and the syndrome shares key neurodevelopmental features with psychosis in the general population. The olfactory system develops early in utero, providing a window into gestational disruptions relevant to psychosis risk. We hypothesized that 22q11DS would show impairments in olfactory function and structural anomalies of the olfactory system relative to psychosis spectrum (PS) youth without a known genetic syndrome and typically developing controls (TD). Methods: Participants included individuals with 22q11DS (n=101), PS (n=56), and TD (n=52). Sinonasal symptoms, odor identification, and discrimination (Sniffin' Sticks) behaviorally. Structural 3T MRI measured olfactory bulb volume, sulcus depth, and sulcus length. Sample sizes vary per analysis. Results: Relative to TD, 22q11DS showed elevated sinonasal burden (p<.001), lower odor identification (p<.001), and lower odor discrimination (p<.001). PS showed elevated sinonasal burden (p<.001) and marginally lower odor identification (p=.06), but no discrimination deficit. Olfactory bulb volume was significantly smaller in 22q11DS versus TD (p=.007, r=.39); PS showed a non-significant trend (p=.07). Olfactory sulcus depth (p=.52) and length (p=.82) did not differ across groups. Conclusions: 22q11DS is associated with elevated sinonasal burden, robust olfactory identification and discrimination deficits exceeding those of PS and reduced olfactory bulb volume. The co-occurrence of functional deficits and reduced bulb volume without sulcal anomalies may distinguish the 22q11DS olfactory phenotype from that of PS. These hypothesis-generating findings are consistent with partially dissociable neurodevelopmental mechanisms and support the utility of 22q11DS as a genetic model for psychosis risk.

neuroscience↗

bioRxiv2026

An Open Test--Retest Dataset of Rapid Myelin Mapping Acquisitions

Myelin supports rapid and efficient neural communication and is implicated in brain development, learning, aging, and neurological disease. Although numerous magnetic resonance imaging (MRI) approaches have been proposed to characterize myelin in vivo, they have rarely been evaluated together within the same participants and across repeated acquisitions. To address these gaps, we introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an openly available multimodal test--retest MRI dataset of diffusion MRI (dMRI), inhomogeneous magnetization transfer imaging (ihMT), quantitative susceptibility mapping (QSM), MP2RAGE, T1w/T2w ratio mapping, and transverse relaxometry in 22 healthy adults. We evaluated gray--white matter differentiation, intermetric correspondence, test--retest reliability, and participant specificity across 27 myelin-sensitive metrics. Gray--white matter differentiation was strong for dMRI derivatives, MP2RAGE-derived R1, T1w/T2w, and ihMT, while being substantially weaker for QSM measures. Intermetric spatial correlations were generally strongest within acquisition or modeling families. Test--retest reliability was generally high, particularly for dMRI-derived metrics, but was more variable among QSM-, ihMT-, and R2*-derived measures. Regional spatial profiles were also highly participant-specific, with discriminability approaching its upper bound for most metrics. All imaging data, derived maps, and processing code are openly released. MIRROR provides a benchmark for characterizing the measurement properties and practical tradeoffs of scalable myelin-sensitive MRI methods, supporting principled acquisition selection and accelerating methodological development and validation.

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bioRxiv2026

The PMADS Project: A Longitudinal Multimodal Cohort Study to Understand Risk for Perinatal Mood and Anxiety Disorders

BackgroundPerinatal mood and anxiety disorders (PMADs) are among the most common and consequential complications of pregnancy. The perinatal period is also characterized by profound hormonal fluctuations and large-scale brain plasticity. However, the mechanisms linking these neurobiological changes to psychiatric risk are poorly understood. Prospective, clinically informed studies are needed to identify quantitative biomarkers and clarify pathways linking perinatal neurobiology to PMADs risk. MethodsThis report describes the design of a prospective, longitudinal cohort study integrating multimodal neuroimaging, biofluid sampling, and deep clinical phenotyping to enable precision characterization of neurobiological trajectories of PMADs risk. Twenty-five individuals at elevated risk for PMADs will be recruited prior to conception and followed across six in-person timepoints spanning the menstrual cycle, pregnancy, and early postpartum, with additional remote follow-ups through the first postpartum year. Data collection includes high-resolution structural MRI, functional brain mapping using multi-echo resting-state fMRI, diffusion MRI, arterial spin labeling, ultra-high field MR-based techniques for measuring glutamate (GluCEST and 1HMRS), biofluid sampling, and comprehensive clinical, behavioral, and cognitive assessments. Structured clinical interviews assess categorical diagnoses while dimensional symptom measures capture heterogeneity and transdiagnostic features of perinatal psychopathology. Longitudinal analyses will model nonlinear trajectories of brain and symptom change across the perinatal period as well as evaluate whether preconception network features and menstrual cycle-related brain changes are associated with subsequent perinatal symptom emergence. DiscussionThis cohort study establishes a longitudinal, multimodal framework for investigating neurobiological changes across the transition to pregnancy in individuals at elevated risk for PMADs. By anchoring pregnancy-related brain changes to preconception and menstrual cycle-related variability within the same individuals, this study is designed to evaluate associations between preconception hormone sensitivity, pregnancy-induced neuroplasticity, and PMADs risk. The resulting dataset will provide a deeply phenotyped longitudinal resource for investigating brain-behavior relationships across the perinatal period. Findings are expected to inform future larger-scale studies aimed at advancing mechanistic understanding of PMADs, improving individualized risk stratification, and supporting development of personalized preventive and neuromodulatory interventions.

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bioRxiv2025

Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity

Long-range white matter (WM) tracts support cognition by enabling communication between distant cortical regions, which are organized along a hierarchy defined by the sensorimotor-to-association (S-A) axis. However, it remains unknown how WM tracts are positioned within the cortical hierarchy to support cognition. Here, we show that WM tracts are differentially positioned in the cortical hierarchy to support specific cognitive functions, and that tracts spanning the hierarchy connect regions with greater cognitive diversity. Moreover, tracts situated within the same hierarchical level connect biologically similar regions, while those crossing the hierarchy bridge distinct biological milieux to support diverse cognitive functions. The placement of tracts in the cortical hierarchy also reflects developmental variation in tract microstructure and individual differences in cognition. Together, these findings provide a framework that moves beyond conventional categories of association or projection tracts and links WM tract anatomy to cortical organization, cognitive function, cortical neurobiology, and neurodevelopment. We anticipate that this cortex-anchored framework for describing WM tracts may aid the interpretation of individual differences in WM structure related to development and behavior.

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bioRxiv2025

An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

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bioRxiv2025

Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy

Cannabis use is linked to elevated psychosis risk, yet the neurobiological mechanisms that couple use to symptom expression remain unclear. Because glutamatergic dysregulation has been implicated in both cannabis effects and psychosis vulnerability, we examined whether brain glutamate relates to dimensional symptoms as a function of cannabis use across the psychosis spectrum. Seventy-nine participants--typically developing controls, clinical high-risk individuals, and patients with psychosis--completed dimensional clinical assessments, detailed cannabis surveys, urine toxicology, and ultra-high-field 7T 1HMRS quantification of anterior cingulate cortex (ACC) glutamate levels. Linear models assessed the main and interactive effects of ACC glutamate and cannabis use on positive and negative symptoms. Self-reported cannabis use showed strong concordance with urine toxicology. Cannabis use was associated with higher positive and negative symptoms. Independently, higher ACC glutamate predicted greater positive and negative symptoms. Notably, lower glutamate levels were associated with higher positive symptoms in cannabis users. Exploratory analyses suggested interactions for depressive and manic symptoms, indicating that glutamatergic abnormalities may amplify the overall severity of cannabis-related symptoms. Sensitivity analyses revealed lower ACC glutamate in psychosis patients--especially cannabis users--highlighting diagnostic group differences and reinforcing the link between cannabis exposure and glutamatergic dysfunction. These findings implicate ACC glutamatergic dysfunction as a transdiagnostic correlate of symptom burden, particularly in those with psychosis who are cannabis users. Glutamate-targeted interventions and longitudinal designs will be needed to examine causal pathways linking cannabis exposure to psychosis-relevant outcomes.

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bioRxiv2025

Benchmarking Orientation Distribution Function Estimation Methods for Tractometry in Single-Shell Diffusion Magnetic Resonance Imaging - An Evaluation of Test-Retest Reliability and Predictive Capability

Deriving white matter (WM) bundles in-vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi-shell diffusion MRI (dMRI) acquisitions that enable advanced reconstruction methods. However, these advanced acquisitions are both time-consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single-shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population-level generalizability. While legacy scans may offer a valuable, large-scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64-direction dMRI scan was acquired as two independent 32-direction runs per subject. To investigate how recently developed bundle segmentation methods generalize to this data, we evaluated the test-retest reliability of the two 32-direction scans, of WM bundle extraction across three orientation distribution function (ODF) reconstruction methods: generalized q-sampling imaging (GQI), constrained spherical deconvolution (CSD), and single-shell three-tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32-direction, single-shell acquisition scheme. The mean dice coefficient of reconstructed WM bundles was consistently higher within-subject than between-subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person-specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies (r: 0.15-0.36) across the test-retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity in external validation, a high intra-class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle segmentation can achieve robust performance even on lower angular resolution, single-shell dMRI, with particular advantages for ODF methods optimized for single-shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.

neuroscience↗

bioRxiv2025

An open, fully-processed data resource for studying mood and sleep variability in the developing brain

Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.

neuroscience↗

bioRxiv2025

Two Axes of White Matter Development

Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2,716). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.

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bioRxiv2024

Reproducible Sex Differences in Personalized Functional Network Topography in Youth

BackgroundA key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex. AimsWe aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks. MethodWe leveraged person-specific atlases of functional brain networks defined using non-negative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression. ResultsSex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons. ConclusionsSex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.

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bioRxiv2024

A sensorimotor-association axis of thalamocortical connection development

Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brains capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here 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 cortexs 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.

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bioRxiv2023

Network Enrichment Significance Testing in Brain-Phenotype Association Studies

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about the spatial structure of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genomics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose Network Enrichment Significance Testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study phenotype associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

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bioRxiv2023

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

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bioRxiv2023

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

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bioRxiv2023

Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth 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.

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bioRxiv2022

Compression supports low-dimensional representations of behavior across neural circuits

Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed activity, and may do so in a heterogeneous manner across diverse neural circuits that represent low-level (sensory) or high-level (cognitive) stimuli. However, precisely how compression and representational capacity differ across the cortex remains unknown. Here we predict different levels of compression across regional circuits by using random walks on networks to model activity flow and to formulate rate-distortion functions, which are the basis of lossy compression. Using a large sample of youth (n = 1, 040), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks. Our network theory of compression predicts the dimensionality of activity (t = 12.13, p < 0.001) and the representational capacity of biological (r = 0.53, p = 0.016) and artificial (r = 0.61, p < 0.001) networks. The model suggests how a basic form of compression is an emergent property of activity flow between distributed circuits that communicate with the rest of the network.

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bioRxiv2022

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brains functional prioritization, quantifying variation in the topography 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 (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSM Study. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortexs sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

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bioRxiv2022

Intrinsic Activity Develops Along a Sensorimotor-Association Cortical Axis in Youth

Animal studies of neurodevelopmental plasticity have shown that intrinsic brain activity evolves from high amplitude and globally synchronized to suppressed and sparse as plasticity declines and the cortex matures. Leveraging resting-state functional MRI data from 1033 individuals (8-23 years), we reveal that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental plasticity during childhood and adolescence. Specifically, we demonstrate that declines in the amplitude of intrinsic activity are initiated heterochronously across regions, coupled to the maturation of a plasticity-restricting structural feature, and temporally staggered along a hierarchical sensorimotor-association axis from ages 8 to 18. Youth from disadvantaged environments exhibit reduced intrinsic activity in regions further up the sensorimotor-association axis, suggestive of a reduced level of plasticity in late-maturing cortices. Our results uncover a hierarchical axis of neurodevelopment and offer insight into the temporal sequence of protracted neurodevelopmental plasticity in humans.

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