Biology subjects

Satterthwaite, T. D.

Publications and source records attributed to Satterthwaite, T. D..

At least 109 records · Page 6Linked to original sources

bioRxiv2021

Network controllability mediates the relationship between rigid structure and flexible dynamics

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brains structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain regions functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes boundary controllability, suggesting that a regions strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity. AUTHOR SUMMARYPrecisely how the relatively rigid white matter wiring of the human brain gives rise to a diverse repertoire of functional neural dynamics is not well understood. In this work, we combined tools from network science and control theory to address this question. Capitalizing on a large developmental cohort, we demonstrated that the ability of a brain region to flexibly change its functional module allegiance over time (i.e., its modular flexibility), was positively correlated with its proportion of anatomical edges projecting to multiple cognitive networks (i.e., its structural participation coefficient). Moreover, this relationship was strongly mediated by the regions boundary controllability, a metric capturing its capacity to integrate information across multiple cognitive domains.

neuroscience↗

bioRxiv2021

Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

neuroscience↗

bioRxiv2021

FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is a data storage specification for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are not designed for use on cloud-based systems such as Flywheel. To address these challenges, we developed "FlywheelTools", a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

bioinformatics↗

bioRxiv2020

Multi-Modal Biomarkers of Cerebral Edema in Low Resolution MRI

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate to low resolution images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria were imaged using low-field 0.35 Tesla MRI. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to cerebral malaria.

neuroscience↗

bioRxiv2020

Altered functional brain dynamics in chromosome 22q11.2 deletion syndrome during facial affect processing

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a multisystem disorder associated with multiple congenital anomalies, variable medical features, and neurodevelopmental differences resulting in diverse psychiatric phenotypes, including marked deficits in facial memory and social cognition. Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks, but time-varying interactions between brain areas during facial affect processing have not yet been studied in 22q11.2DS. We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with 22q11.2DS and 58 age-, race-, and sex-matched healthy controls. Delayed frontal-motor feedback signals were diminished in individuals with 22q11.2DS, as were delayed emotional memory signals engaging amygdala, hippocampus, and entorhinal cortex. Early task-related engagement of motor and visual cortices and salience-related insular activation were relatively preserved in 22q11.2DS. Insular activation was associated with task performance within the 22q11.2DS sample. Differences in cortical surface area, but not cortical thickness, showed spatial alignment with an activation pattern associated with face processing. These findings suggest that relative to matched controls, primary visual processing and insular function are relatively intact in individuals with 22q11.22DS, while motor feedback, face processing, and emotional memory processes are more affected. Such insights may help inform potential interventional targets and enhance the specificity of neuroimaging indices of cognitive dysfunction in 22q11.2DS.

neuroscience↗

bioRxiv2020

A simple permutation-based test of intermodal correspondence

Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this paper we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.

neuroscience↗

bioRxiv2020

QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI

Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as "glass-box" methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

bioinformatics↗

bioRxiv2020

Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure

Psychopathology is rooted in neurodevelopment. However, clinical and biological heterogeneity, together with a focus on case-control approaches, have made it difficult to link dimensions of psychopathology to abnormalities of neurodevelopment. Here, using the Philadelphia Neurodevelopmental Cohort, we built normative models of cortical volume and tested whether deviations from these models better predicted psychiatric symptoms compared to raw cortical volume. Specifically, drawing on the p-factor hypothesis, we distilled 117 clinical symptom measures into six orthogonal psychopathology dimensions: overall psychopathology, anxious-misery, externalizing disorders, fear, positive psychotic symptoms, and negative psychotic symptoms. We found that multivariate patterns of deviations yielded improved out-of-sample prediction of psychopathology dimensions compared to multivariate patterns of raw cortical volume. We also found that correlations between overall psychopathology and deviations in ventromedial prefrontal, inferior temporal, dorsal anterior cingulate, and insular cortices were stronger than those observed for specific dimensions of psychopathology (e.g., anxious-misery). Notably, these same regions are consistently implicated in a range of putatively distinct disorders. Finally, we performed conventional case-control comparisons of deviations in a group of individuals with depression and a group with attention-deficit hyperactivity disorder (ADHD). We observed spatially overlapping effects between these groups that diminished when controlling for overall psychopathology. Together, our results suggest that modeling cortical brain features as deviations from normative neurodevelopment improves prediction of psychiatric symptoms in out-of-sample testing, and that p-factor models of psychopathology may assist in separating biomarkers that are disorder-general from those that are disorder-specific.

neuroscience↗

bioRxiv2020

Efficient Coding in the Economics of Human Brain Connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond the conventional network efficiency metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity. Author SummaryMacroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression.

neuroscience↗

bioRxiv2019

Sex differences in Variability of Brain Structure Across the Lifespan

Several brain disorders exhibit sex differences in onset, presentation, and prevalence. Increased understanding of the neurobiology of sex-based differences across the lifespan can provide insight into potential disease risk and protective mechanisms. We focused on sex-related differences in variability, which may be indicative of both disease vulnerability and resilience. In n=3,069 participants, from 8-95 years of age, we first analyzed the variance ratio in females vs. males of cortical surface area and global and subcortical volumes for discrete brain regions, and found widespread greater variability in males. In contrast, variance in cortical thickness was similar for males and females. Multivariate analysis that accounts for structural covariance supported variance ratio findings. Findings were present from early life and stable with age. We then examined variability among brain regions by sex. We found significant age-by-sex interactions across neuroimaging metrics, whereby in very early life males had reduced among-region variability compared to females, while in very late life this was reversed. Overall, our findings of greater regional variability but less among-region variability in males in early life may aid our understanding of sex-based risk for neurodevelopmental disorders. In contrast, our findings in late life may provide a potential sex-based risk mechanism for dementia.

neuroscience↗

bioRxiv2019

Convergent neural representations of acute nociceptive pain in healthy volunteers: A large-scale fMRI meta-analysis

Characterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute pain.\n\nHIGHLIGHTSO_LIPain stimulation recruits a core set of pain-related brain regions.\nC_LIO_LIThis core set includes thalamus, SII, insula and mid-cingulate cortex.\nC_LIO_LIThese regions were recruited regardless of stimulus modality and stimulus location.\nC_LI

neuroscience↗

bioRxiv2019

Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

bioinformatics↗

bioRxiv2019

Neurocognitive and Functional Heterogeneity in Depressed Youth

OBJECTIVEDepression is a common psychiatric illness that often begins in youth, and is associated with cognitive symptoms. However, there is significant variability in the cognitive burden, likely reflecting biological heterogeneity. This study sought to identify neurocognitive subtypes in a large sample of depressed youth, and evaluated the neural signatures of these subtypes.\n\nMETHODSParticipants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (n=368, TD=200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A semi-supervised machine-learning algorithm, HYDRA (Heterogeneity through Discriminative Analysis), was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task.\n\nRESULTSHYDRA identified three neurocognitive subtypes in the depressed group. Overall, Subtype 1 had better performance than TD comparators across many cognitive tasks (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), whereas Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition.\n\nCONCLUSIONSUsing a data-driven approach, three neurocognitive subtypes of depression were identified that differed in neural signatures despite similar clinical psychopathology. These data suggest disparate mechanisms of cognitive vulnerability and resilience in depression, which may inform the identification of biomarkers for prognosis and treatment response.

neuroscience↗

bioRxiv2019

Development of structure-function coupling in human brain networks during youth

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as working memory. However, it remains unclear how white matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure-function coupling using diffusion-weighted imaging and n-back fMRI data in a sample of 727 individuals (ages 8-23 years). We found that spatial variability in structure-function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure-function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n=294). Moreover, structure-function coupling in rostrolateral prefrontal cortex was associated with executive performance, and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

neuroscience↗

bioRxiv2019

Individual Variation in Control Network Topography Supports Executive Function in Youth

The spatial distribution of large-scale functional networks on the anatomic cortex differs between individuals, and is particularly variable in networks responsible for executive function. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing upon advances in machine learning and a large sample of youth (n=693, ages 8-23y) imaged with 27 minutes of high-quality fMRI data, we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of an unseen individuals brain maturity. Furthermore, the cortical representation of executive networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering both the plasticity and diversity of functional neuroanatomy during development, and suggest advances in personalized therapeutics.

neuroscience↗

bioRxiv2019

Multimodal parameters of brain structure and function related to sex differences in cognitive performance.

The Parieto-Frontal Integration Theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional MRI measures of amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic and cerebellar regions showed comparable effects, hence PFIT needs expansion into an Extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining low resting energy state.

neuroscience↗

bioRxiv2019

Multi-Scale Network Regression for Brain-Phenotype Associations

Complex brain networks are increasingly characterized at different scales, including global summary statistics, community connectivity, and individual edges. While research relating brain networks to demographic and behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale, thus failing to incorporate rich information present at other scales. Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant association between functional connectivity patterns with brain development, sex differences, and motion-related artifacts. Notably, compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge- and community-level information, MSNR has the potential to yield novel insights into brain-behavior relationships.

neuroscience↗

bioRxiv2019

Neurostructural Heterogeneity in Youth with Internalizing Symptoms

Internalizing disorders such as anxiety and depression are the most common psychiatric disorders, frequently begin in youth, and exhibit marked heterogeneity in treatment response and clinical course. It is increasingly recognized that symptom-based classification approaches to internalizing disorders do not align with underlying neurobiology. An alternative to classifying psychopathology based on clinical symptoms is to identify neurobiologically-informed subtypes based on brain imaging data. We used a recently developed semi-supervised machine learning method (HYDRA) to delineate patterns of neurobiological heterogeneity within youth with internalizing symptoms using structural imaging data collected at 3T from a large community-based sample of 1,141 youth. Using volume and cortical thickness, cross-validation methods indicated a highly stable solution (ARI=.66; permutation-based pfdr < .001) and identified two subtypes of internalizing youth. Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both Subtype 2 and typically developing youth. Using resting-state fMRI and diffusion images not considered during clustering, we found that Subtype 1 also showed reduced amplitudes of low-frequency fluctuations in fronto-limbic regions at rest, as well as reduced fractional anisotropy in white matter tracts such as the parahippocampal cingulum bundle and the uncinate fasciculus. In contrast, Subtype 2 showed intact cognitive performance, greater volume, cortical thickness, and amplitudes during rest compared to Subtype 1 and typically developing youth, despite still showing clinically significant levels of psychopathology. Identification of biologically-grounded subtypes of internalizing disorders may assist in targeting early interventions and assessing longitudinal prognosis.

neuroscience↗