Sydney Covitz

All Publications

• Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health. Neuron Shafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S., Giavasis, S., Lane, C., Mehta, K., Moore, T. M., Salo, T., Tapera, T. M., Calkins, M. E., Colcombe, S., Davatzikos, C., Gur, R. E., Gur, R. C., Pan, P. M., Jackowski, A. P., Rokem, A., Rohde, L. A., Shinohara, R. T., Tottenham, N., Zuo, X. N., Cieslak, M., Franco, A. R., Kiar, G., Salum, G. A., Milham, M. P., Satterthwaite, T. D. 2025

  Abstract

  Mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce Reproducible Brain Charts (RBC), an open resource that integrates data from 5 large studies of brain development in youth from three continents (N = 6,346). Bifactor models were used to create harmonized psychiatric phenotypes, capturing major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner. Initial analyses of RBC emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data-including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives-are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

  View details for DOI 10.1016/j.neuron.2025.08.026

  View details for PubMedID 40987284

• Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases. NEJM AI Ruth, P. S., Uhlrich, S. D., de Monts, C., Falisse, A., Muccini, J., Covitz, S., Vogt-Domke, S., Day, J., Duong, T., Delp, S. L. 2025; 2 (9)

  Abstract

  Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread types of assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers, but it is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable the quantification of three-dimensional movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested.To compare video-based analysis with TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings, we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-times sit-to-stand) while capturing additional disease-specific signatures of movement.Video-based biomechanical analysis reproduced all four TFTs (r>0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (P=0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement, such as differences in gait kinematics, that are not evident in TFTs.Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions. (Funded by the Wu Tsai Human Performance Alliance and others.).

  View details for DOI 10.1056/aioa2401137

  View details for PubMedID 40926960

  View details for PubMedCentralID PMC12416922

• Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets NEUROIMAGE Covitz, S., Tapera, T. M., Adebimpe, A., Alexander-Bloch, A. F., Bertolero, M. A., Feczko, E., Franco, A. R., Gur, R. E., Gur, R. C., Hendrickson, T., Houghton, A., Mehta, K., Murtha, K., Perrone, A. J., Robert-Fitzgerald, T., Schabdach, J. M., Shinohara, R. T., Vogel, J. W., Zhao, C., Fair, D. A., Milham, M. P., Cieslak, M., Satterthwaite, T. D. 2022; 263: 119609

  Abstract

  The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

  View details for DOI 10.1016/j.neuroimage.2022.119609

  View details for Web of Science ID 000863294000005

  View details for PubMedID 36064140

  View details for PubMedCentralID PMC9981813

• Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases. bioRxiv : the preprint server for biology Ruth, P. S., Uhlrich, S. D., de Monts, C., Falisse, A., Muccini, J., Covitz, S., Vogt-Domke, S., Day, J., Duong, T., Delp, S. L. 2025

  Abstract

  Assessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers but is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable quantification of 3D movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested.To compare video-based analysis against TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-time sit-to-stand) while capturing additional disease-specific signatures of movement.Video-based biomechanical analysis reproduced all four TFTs (r > 0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (p = 0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement such as differences in gait kinematics that are not evident in TFTs.Video-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions.

  View details for DOI 10.1101/2024.09.26.613967

  View details for PubMedID 40766489

  View details for PubMedCentralID PMC12324206

• Human thalamocortical structural connectivity develops in line with a hierarchical axis of cortical plasticity. Nature neuroscience Sydnor, V. J., Bagautdinova, J., Larsen, B., Arcaro, M. J., Barch, D. M., Bassett, D. S., Alexander-Bloch, A. F., Cook, P. A., Covitz, S., Franco, A. R., Gur, R. E., Gur, R. C., Mackey, A. P., Mehta, K., Meisler, S. L., Milham, M. P., Moore, T. M., Müller, E. J., Roalf, D. R., Salo, T., Schubiner, G., Seidlitz, J., Shinohara, R. T., Shine, J. M., Yeh, F. C., Cieslak, M., Satterthwaite, T. D. 2025

  Abstract

  Human cortical development follows a hierarchical, sensorimotor-to-association sequence. The brain's capacity to enact this sequence indicates that it relies on unknown mechanisms to regulate regional differences in the timing of cortical maturation. Given evidence from animal systems that thalamic axons mechanistically regulate periods of cortical plasticity, here we evaluate in humans whether the development of structural connections between the thalamus and cortex aligns with cortical maturational heterochronicity. By deriving a new tractography atlas of human thalamic connections and applying it to diffusion data from three youth samples (8-23 years; total n = 2,676), we demonstrate that thalamocortical connectivity matures in a generalizable manner along the cortex's sensorimotor-association axis. Associative cortical regions with thalamic connections that take the longest to mature exhibit neurochemical, structural and functional signatures of protracted developmental plasticity as well as heightened sensitivity to the socioeconomic environment. This work highlights the role of the thalamus in the expression of hierarchical periods of cortical developmental plasticity and environmental receptivity.

  View details for DOI 10.1038/s41593-025-01991-6

  View details for PubMedID 40615590

  View details for PubMedCentralID 8448958

• Generalizable Links Between Borderline Personality Traits and Functional Connectivity. Biological psychiatry Shafiei, G., Keller, A. S., Bertolero, M., Shanmugan, S., Bassett, D. S., Chen, A. A., Covitz, S., Houghton, A., Luo, A., Mehta, K., Salo, T., Shinohara, R. T., Fair, D., Hallquist, M. N., Satterthwaite, T. D. 2024; 96 (6): 486-494

  Abstract

  Symptoms of borderline personality disorder (BPD) often manifest during adolescence, but the underlying relationship between these debilitating symptoms and the development of functional brain networks is not well understood. Here, we aimed to investigate how multivariate patterns of functional connectivity are associated with borderline personality traits in large samples of young adults and adolescents.We used functional magnetic resonance imaging data from young adults and adolescents from the HCP-YA (Human Connectome Project Young Adult) (n = 870, ages 22-37 years, 457 female) and the HCP-D (Human Connectome Project Development) (n = 223, ages 16-21 years, 121 female). A previously validated BPD proxy score was derived from the NEO Five-Factor Inventory. A ridge regression model with cross-validation and nested hyperparameter tuning was trained and tested in HCP-YA to predict BPD scores in unseen data from regional functional connectivity. The trained model was further tested on data from HCP-D without further tuning. Finally, we tested how the connectivity patterns associated with BPD aligned with age-related changes in connectivity.Multivariate functional connectivity patterns significantly predicted out-of-sample BPD scores in unseen data in young adults (HCP-YA ppermuted = .001) and older adolescents (HCP-D ppermuted = .001). Regional predictive capacity was heterogeneous; the most predictive regions were found in functional systems relevant for emotion regulation and executive function, including the ventral attention network. Finally, regional functional connectivity patterns that predicted BPD scores aligned with those associated with development in youth.Individual differences in functional connectivity in developmentally sensitive regions are associated with borderline personality traits.

  View details for DOI 10.1016/j.biopsych.2024.02.1016

  View details for PubMedID 38460580

  View details for PubMedCentralID PMC11338739

• A network control theory pipeline for studying the dynamics of the structural connectome. Nature protocols Parkes, L., Kim, J. Z., Stiso, J., Brynildsen, J. K., Cieslak, M., Covitz, S., Gur, R. E., Gur, R. C., Pasqualetti, F., Shinohara, R. T., Zhou, D., Satterthwaite, T. D., Bassett, D. S. 2024

  Abstract

  Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

  View details for DOI 10.1038/s41596-024-01023-w

  View details for PubMedID 39075309

  View details for PubMedCentralID 5485642

• Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis. Biological psychiatry Baller, E. B., Sweeney, E. M., Cieslak, M., Robert-Fitzgerald, T., Covitz, S. C., Martin, M. L., Schindler, M. K., Bar-Or, A., Elahi, A., Larsen, B. S., Manning, A. R., Markowitz, C. E., Perrone, C. M., Rautman, V., Seitz, M. M., Detre, J. A., Fox, M. D., Shinohara, R. T., Satterthwaite, T. D. 2024; 95 (12): 1072-1080

  Abstract

  Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS.Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain.MS lesions preferentially affected fascicles within versus outside the depression network (β = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (β = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (β = 0.02, 95% CI = 0.003 to 0.040, p = .020).We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

  View details for DOI 10.1016/j.biopsych.2023.11.010

  View details for PubMedID 37981178

  View details for PubMedCentralID PMC11101593

• A sensorimotor-association axis of thalamocortical connection development. bioRxiv : the preprint server for biology Sydnor, V. J., Bagautdinova, J., Larsen, B., Arcaro, M. J., Barch, D. M., Bassett, D. S., Alexander-Bloch, A. F., Cook, P. A., Covitz, S., Franco, A. R., Gur, R. E., Gur, R. C., Mackey, A. P., Mehta, K., Meisler, S. L., Milham, M. P., Moore, T. M., Muller, E. J., Roalf, D. R., Salo, T., Schubiner, G., Seidlitz, J., Shinohara, R. T., Shine, J. M., Yeh, F., Cieslak, M., Satterthwaite, T. D. 2024

  Abstract

  Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brain's 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 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.

  View details for DOI 10.1101/2024.06.13.598749

  View details for PubMedID 38915591

• BABS in Action: Merging Reproducibility and Scalability for Large-Scale Neuroimaging Analysis With BIDS Apps Zhao, C., Chen, Y., Jarecka, D., Ghosh, S., Cieslak, M., Satterthwaite, T. D. ELSEVIER SCIENCE INC. 2024: S21-S22

  View details for Web of Science ID 001282811900050

• Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nature communications Luo, A. C., Sydnor, V. J., Pines, A., Larsen, B., Alexander-Bloch, A. F., Cieslak, M., Covitz, S., Chen, A. A., Esper, N. B., Feczko, E., Franco, A. R., Gur, R. E., Gur, R. C., Houghton, A., Hu, F., Keller, A. S., Kiar, G., Mehta, K., Salum, G. A., Tapera, T., Xu, T., Zhao, C., Salo, T., Fair, D. A., Shinohara, R. T., Milham, M. P., Satterthwaite, T. D. 2024; 15 (1): 3511

  Abstract

  Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

  View details for DOI 10.1038/s41467-024-47748-w

  View details for PubMedID 38664387

  View details for PubMedCentralID 4879139

• ModelArray: An R package for statistical analysis of fixel-wise data. NeuroImage Zhao, C., Tapera, T. M., Bagautdinova, J., Bourque, J., Covitz, S., Gur, R. E., Gur, R. C., Larsen, B., Mehta, K., Meisler, S. L., Murtha, K., Muschelli, J., Roalf, D. R., Sydnor, V. J., Valcarcel, A. M., Shinohara, R. T., Cieslak, M., Satterthwaite, T. D. 2023; 271: 120037

  Abstract

  Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

  View details for DOI 10.1016/j.neuroimage.2023.120037

  View details for PubMedID 36931330

  View details for PubMedCentralID PMC10119782

• Depression as a Disease of White Matter Network Disruption: Characterizing the Relationship Between White Matter Lesions and Depression in Patients With Multiple Sclerosis Baller, E., Sweeney, E., Bar-Or, A., Cieslak, M., Covitz, S., Detre, J., Elahi, A., Manning, A., Markowitz, C., Martin, M., Perrone, C., Rautman, V., Robert-Fitzgerald, T., Schindler, M., Siddiqi, S., Thomas, S., Fox, M., Shinohara, R., Satterthwaite, T. ELSEVIER SCIENCE INC. 2023: S117

  View details for Web of Science ID 000993018500279

• Intrinsic activity development unfolds along a sensorimotor-association cortical axis in youth. Nature neuroscience Sydnor, V. J., Larsen, B., Seidlitz, J., Adebimpe, A., Alexander-Bloch, A. F., Bassett, D. S., Bertolero, M. A., Cieslak, M., Covitz, S., Fan, Y., Gur, R. E., Gur, R. C., Mackey, A. P., Moore, T. M., Roalf, D. R., Shinohara, R. T., Satterthwaite, T. D. 2023; 26 (4): 638-649

  Abstract

  Animal studies of neurodevelopment have shown that recordings of intrinsic cortical activity evolve from synchronized and high amplitude to sparse and low amplitude as plasticity declines and the cortex matures. Leveraging resting-state functional MRI (fMRI) data from 1,033 youths (ages 8-23 years), we find that this stereotyped refinement of intrinsic activity occurs during human development and provides evidence for a cortical gradient of neurodevelopmental change. Declines in the amplitude of intrinsic fMRI activity were initiated heterochronously across regions and were coupled to the maturation of intracortical myelin, a developmental plasticity regulator. Spatiotemporal variability in regional developmental trajectories was organized along a hierarchical, sensorimotor-association cortical axis from ages 8 to 18. The sensorimotor-association axis furthermore captured variation in associations between youths' neighborhood environments and intrinsic fMRI activity; associations suggest that the effects of environmental disadvantage on the maturing brain diverge most across this axis during midadolescence. These results uncover a hierarchical neurodevelopmental axis and offer insight into the progression of cortical plasticity in humans.

  View details for DOI 10.1038/s41593-023-01282-y

  View details for PubMedID 36973514

  View details for PubMedCentralID PMC10406167

• Development of top-down cortical propagations in youth. Neuron Pines, A., Keller, A. S., Larsen, B., Bertolero, M., Ashourvan, A., Bassett, D. S., Cieslak, M., Covitz, S., Fan, Y., Feczko, E., Houghton, A., Rueter, A. R., Saggar, M., Shafiei, G., Tapera, T. M., Vogel, J., Weinstein, S. M., Shinohara, R. T., Williams, L. M., Fair, D. A., Satterthwaite, T. D. 2023

  Abstract

  Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

  View details for DOI 10.1016/j.neuron.2023.01.014

  View details for PubMedID 36803653

• Depression as a Disease of White Matter Network Disruption: Characterizing the Relationship Between White Matter Lesions and Depression in Patients With Multiple Sclerosis Baller, E., Cieslak, M., Robert-Fitzgerald, T., Covitz, S., Martin, M., Schindler, M., Baror, A., Elahi, A., Fox, M., Manning, A., Markowitz, C., Mirkovic, N., Perrone, C., Rautman, V., Reid, D., Schultz, G., Siddiqi, S., Thomas, S., Detre, J., Shinohara, R., Satterthwaite, T. SPRINGERNATURE. 2022: 228-229

  View details for Web of Science ID 000897934700443

• An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific data Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M., Avelar-Pereira, B., Roy, E., Sydnor, V. J., Yeatman, J. D., Fibr Community Science Consortium, Satterthwaite, T. D., Rokem, A., Abbott, N. J., Anderson, J. A., Gagana, B., Bleile, M., Bloomfield, P. S., Bottom, V., Bourque, J., Boyle, R., Brynildsen, J. K., Calarco, N., Castrellon, J. J., Chaku, N., Chen, B., Chopra, S., Coffey, E. B., Colenbier, N., Cox, D. J., Crippen, J. E., Crouse, J. J., David, S., Leener, B. D., Delap, G., Deng, Z., Dugre, J. R., Eklund, A., Ellis, K., Ered, A., Farmer, H., Faskowitz, J., Finch, J. E., Flandin, G., Flounders, M. W., Fonville, L., Frandsen, S. B., Garic, D., Garrido-Vasquez, P., Gonzalez-Escamilla, G., Grogans, S. E., Grotheer, M., Gruskin, D. C., Guberman, G. I., Haggerty, E. B., Hahn, Y., Hall, E. H., Hanson, J. L., Harel, Y., Vieira, B. H., Hettwer, M. D., Hobday, H., Horien, C., Huang, F., Huque, Z. M., James, A. R., Kahhale, I., Kamhout, S. L., Keller, A. S., Khera, H. S., Kiar, G., Kirk, P. A., Kohl, S. H., Korenic, S. A., Korponay, C., Kozlowski, A. K., Kraljevic, N., Lazari, A., Leavitt, M. J., Li, Z., Liberati, G., Lorenc, E. S., Lossin, A. J., Lotter, L. D., Lydon-Staley, D. M., Madan, C. R., Magielse, N., Marusak, H. A., Mayor, J., McGowan, A. L., Mehta, K. P., Meisler, S. L., Michael, C., Mitchell, M. E., Morand-Beaulieu, S., Newman, B. T., Nielsen, J. A., O'Mara, S. M., Ojha, A., Omary, A., Ozarslan, E., Parkes, L., Peterson, M., Pines, A. R., Pisanu, C., Rich, R. R., Sahoo, A. K., Samara, A., Sayed, F., Schneider, J. T., Shaffer, L. S., Shatalina, E., Sims, S. A., Sinclair, S., Song, J. W., Hogrogian, G. S., Tooley, U. A., Tripathi, V., Turker, H. B., Valk, S. L., Wall, M. B., Walther, C. K., Wang, Y., Wegmann, B., Welton, T., Wiesman, A. I., Wiesman, A. G., Wiesman, M., Winters, D. E., Yuan, R., Zacharek, S. J., Zajner, C., Zakharov, I., Zammarchi, G., Zhou, D., Zimmerman, B., Zoner, K. 2022; 9 (1): 616

  Abstract

  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. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC=0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

  View details for DOI 10.1038/s41597-022-01695-7

  View details for PubMedID 36224186

• ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nature methods Adebimpe, A., Bertolero, M., Dolui, S., Cieslak, M., Murtha, K., Baller, E. B., Boeve, B., Boxer, A., Butler, E. R., Cook, P., Colcombe, S., Covitz, S., Davatzikos, C., Davila, D. G., Elliott, M. A., Flounders, M. W., Franco, A. R., Gur, R. E., Gur, R. C., Jaber, B., McMillian, C., ALLFTD Consortium, Milham, M., Mutsaerts, H. J., Oathes, D. J., Olm, C. A., Phillips, J. S., Tackett, W., Roalf, D. R., Rosen, H., Tapera, T. M., Tisdall, M. D., Zhou, D., Esteban, O., Poldrack, R. A., Detre, J. A., Satterthwaite, T. D., Apostolova, L., Appleby, B., Barmada, S., Bordelon, Y., Botha, H., Boxer, A. L., Bozoki, A., Brushaber, D., Clark, D., Coppola, G., Darby, R., Dickson, D., Domoto-Reilly, K., Faber, K., Fagan, A., Fields, J. A., Foroud, T., Forsberg, L. K., Geschwind, D., Goldman, J., Galasko, D. R., Gavrilova, R., Gendron, T., Graff-Radford, J., Graff-Radford, N., Grant, I. M., Grossman, M., Hall, M., Huang, E., Heuer, H. W., Hsiung, G. R., Huey, E. D., Irwin, D., Jones, D. T., Kantarci, K., Kaufer, D., Kerwin, D., Knopman, D., Kornak, J., Kramer, J., Kremers, W., Lapid, M., Lago, A. L., Leger, G., Ljubenkov, P., Litvan, I., Lucente, D., Mackenzie, I. R., Masdeu, J. C., McGinnis, S., Mendez, M., Mester, C., Miller, B. L., Onyike, C., Pascual, M. B., Petrucelli, L., Pressman, P., Rademakers, R., Ramanan, V., Ramos, E. M., Rao, M., Rascovsky, K., Rankin, K. P., Ritter, A., Roberson, E. D., Rojas-Martinez, J., Rosen, H. J., Savica, R., Seeley, W., Syrjanen, J., Staffaroni, A. M., Tartaglia, M. C., Taylor, J., VandeVrede, L., Weintraub, S., Wong, B., Wszolek, Z. 2022; 19 (6): 683-686

  Abstract

  Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively 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.

  View details for DOI 10.1038/s41592-022-01458-7

  View details for PubMedID 35689029