Kristin Murtha

University of Pennsylvania

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Theodore D. Satterthwaite

University of Pennsylvania

Azeez Adebimpe

Lifespan

David R. Roalf

University of Pennsylvania

Matthew Cieslak

California University of Pennsylvania

Russell T. Shinohara

California University of Pennsylvania

Diego Davila

University of California, San Diego

Raquel E. Gur

California University of Pennsylvania

Tinashe M. Tapera

University of Pennsylvania

Ruben C. Gur

California University of Pennsylvania

Adam Pines

University of Pennsylvania

Cooperative Institutions

University of Pennsylvania

California University of Pennsylvania

Lifespan

Children's Hospital of Philadelphia

University of California, Los Angeles

University of Pennsylvania Health System

University of California, San Francisco

VIB-UAntwerp Center for Molecular Neurology

University of Antwerp

Penn Center for AIDS Research

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Dissociable multi-scale patterns of development in personalized brain networks

Nature Communications (2022)

Adam Pines Bart Larsen Zaixu Cui Valerie J. Sydnor Maxwell A. Bertolero

Abstract The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8–23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Neurocognitive

10.1038/s41467-022-30244-4

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Citations (54)

ASLPrep: A Generalizable Platform for Processing of Arterial Spin Labeled MRI and Quantification of Regional Brain Perfusion

bioRxiv (Cold Spring Harbor Laboratory) (2021)

Azeez Adebimpe Maxwell A. Bertolero Sudipto Dolui Matthew Cieslak Kristin Murtha

ABSTRACT 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.

Arterial spin labeling

10.1101/2021.05.20.444998

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Citations (7)

ModelArray: An R package for statistical analysis of fixel-wise data

NeuroImage (2023)

Chenying Zhao Tinashe M. Tapera Joëlle Bagautdinova Josiane Bourque Sydney Covitz

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.

Univariate

Computational statistics

R package

Profiling (computer programming)

Statistical Analysis

Data Analysis

10.1016/j.neuroimage.2023.120037

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Citations (3)

Associations between neighborhood socioeconomic status, parental education, and executive system activation in youth

Cerebral Cortex (2022)

Kristin Murtha Bart Larsen Adam Pines Linden Parkes Tyler M. Moore

Socioeconomic status (SES) can impact cognitive performance, including working memory (WM). As executive systems that support WM undergo functional neurodevelopment during adolescence, environmental stressors at both individual and community levels may influence cognitive outcomes. Here, we sought to examine how SES at the neighborhood and family level impacts task-related activation of the executive system during adolescence and determine whether this effect mediates the relationship between SES and WM performance. To address these questions, we studied 1,150 youths (age 8-23) that completed a fractal n-back WM task during functional magnetic resonance imaging at 3T as part of the Philadelphia Neurodevelopmental Cohort. We found that both higher neighborhood SES and parental education were associated with greater activation of the executive system to WM load, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and precuneus. The association of neighborhood SES remained significant when controlling for task performance, or related factors like exposure to traumatic events. Furthermore, high-dimensional multivariate mediation analysis identified distinct patterns of brain activity within the executive system that significantly mediated the relationship between measures of SES and task performance. These findings underscore the importance of multilevel environmental factors in shaping executive system function and WM in youth.

Dorsolateral prefrontal cortex

10.1093/cercor/bhac120

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Citations (20)

Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity

Neuropsychopharmacology (2022)

Cedric Huchuan Xia Ian Barnett Tinashe M. Tapera Azeez Adebimpe Justin T. Baker

Optimal distinctiveness theory

Footprinting

Sleep

10.1038/s41386-022-01351-z

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Citations (11)

Neighborhood Socioeconomic Factors are Associated With Working Memory Performance and Executive System Activation in Youth

Biological Psychiatry (2021)

Kristin Murtha Bart Larsen Adam Pines Linden Parkes Tyler M. Moore

Association (psychology)

10.1016/j.biopsych.2021.02.898

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Citations (0)

P423. Association Between Neighborhood and Family Level Socioeconomic Status and Executive System Activation in Youth

Biological Psychiatry (2022)

Kristin Murtha Bart Larsen Adam Pines Linden Parkes Tyler M. Moore

Association (psychology)

Stressor

10.1016/j.biopsych.2022.02.659

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Accelerated Cortical Thinning within Structural Brain Networks is Associated with Irritability in Youth

bioRxiv (Cold Spring Harbor Laboratory) (2019)

Robert J. Jirsaraie Antonia N. Kaczkurkin Sage Rush Kayla Piiwia Azeez Adebimpe

ABSTRACT Background Irritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how trans-diagnostic symptoms of irritability relate to the development of structural brain networks. Methods All participants (n=144, 87 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 20.9 years, mean inter-scan interval: 5.1 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate ( q <0.05) was used to correct for multiple comparisons. Results Cross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within 9 networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex. Conclusions Collectively, these findings suggest that irritability is associated with widespread cortical thickness reductions and accelerated cortical thinning, particularly within frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

Irritability

10.1101/596346

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Citations (2)

Curation of BIDS (CuBIDS): a workflow and software package for streamlining reproducible curation of large BIDS datasets

bioRxiv (Cold Spring Harbor Laboratory) (2022)

Sydney Covitz Tinashe M. Tapera Azeez Adebimpe Aaron Alexander‐Bloch Maxwell A. Bertolero

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––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. HIGHLIGHTS CuBIDS is a workflow and software package for curating BIDS data. CuBIDS summarizes the heterogeneity in a BIDS dataset. CuBIDS prepares BIDS data for successful preprocessing pipeline runs. CuBIDS helps users perform metadata-based quality control.

Python

10.1101/2022.05.04.490620

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Citations (6)

Advantages of Multi-shell Diffusion for Studies of Brain Development in Youth

bioRxiv (Cold Spring Harbor Laboratory) (2019)

Adam Pines Matthew Cieslak Graham L. Baum Philip A. Cook Azeez Adebimpe

ABSTRACT Diffusion tensor imaging (DTI) has advanced our understanding of how brain microstructure evolves over development. However, the proliferation of multi-shell diffusion imaging sequences has coincided with notable advances in the modeling of neuronal diffusion patterns, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). The relative utility of these newer diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the relative vulnerability of these models to in-scanner motion has not been described. Accordingly, in a sample of 123 youth (ages 12-30) we evaluated DTI, NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales, including mean white matter values, voxelwise analyses, and tractography-based structural brain networks. Our results reveal that multi-shell diffusion imaging sequences can be leveraged to robustly characterize neurodevelopment, even within the framework of DTI. However, these metrics of diffusion are variably impacted by motion, highlighting the importance of modeling choices for studies of movement-prone populations. Our findings suggest that while traditional DTI is sensitive to neurodevelopmental trends, contemporary modeling techniques confer key advantages for neurodevelopmental inquiries.

Artifact (error)

Brain Development

Connectomics

10.1101/611590

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Citations (4)