Sydney Covitz

All Publications

• 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

• 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

• 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

• Author Correction: 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., Satterthwaite, T. D., Rokem, A. 2023; 10 (1): 247

  View details for DOI 10.1038/s41597-023-02137-8

  View details for PubMedID 37117243

• 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

• Author Correction: 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., Tamnes, C. K., 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): 709

  View details for DOI 10.1038/s41597-022-01816-2

  View details for PubMedID 36396653

• Publisher Correction: 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): 665

  View details for DOI 10.1038/s41597-022-01770-z

  View details for PubMedID 36316349

• 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