Biology subjects

Shinohara, R. T.

Publications and source records attributed to Shinohara, R. T..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Deconfounded Dimension Reduction via Partial Embeddings

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

neuroscience↗

bioRxiv2022

Myelination and excitation-inhibition balance synergistically shape structure-function coupling across the human cortex

Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulated that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employed atlas-and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings were consistent across processing pipelines: 1) increased myelination and lower EI-ratio associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 2) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurred when traversing from granular to agranular cortical regions. Collectively, our work delivers a novel framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders.

neuroscience↗

bioRxiv2022

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

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

neuroscience↗

bioRxiv2022

Resting fMRI-guided TMS evokes subgenual anterior cingulate response in depression

Depression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, it has not yet been confirmed that rsFC-guided TMS coil placement leads to TMS modulation of the sgACC. For each participant (N=115, 34 depressed patients), a peak rsFC cortical hotspot for the sgACC and control targets were prospectively identified. Single pulses of TMS interleaved with fMRI readouts were then administered to these targets and established significant downstream fMRI BOLD responses in the sgACC. We then marked an association between TMS-evoked BOLD responses in the sgACC and rsFC between the stimulation site and sgACC. This effect was qualified by a difference between healthy and patient participants: only in depressed patients, positively connected sites of stimulation led to the strongest evoked responses in the sgACC. Our results highlight rsFC-based targeting as a viable strategy to causally modulate sgACC subcortical targets and further suggest that cortical sites with high positive rsFC to the sgACC might represent an alternative target for the treatment of depression.

neuroscience↗

bioRxiv2022

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

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

neuroscience↗

bioRxiv2022

ModelArray: a memory-efficient R package for statistical analysis of fixel data

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 data exist, currently available tools are memory intensive, difficult to scale to large datasets, and support only a limited number of statistical models. Here we introduce ModelArray, a memory-efficient R package for mass-univariate statistical analysis of fixel data. With only several lines of code, even large fixel datasets can be analyzed using a standard personal computer. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n=938). ModelArray required far less memory than existing tools and 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 an efficient and flexible platform for statistical analysis of fixel data. HIGHLIGHTSO_LIModelArray is an R package for mass-univariate statistical analysis of fixel data C_LIO_LIModelArray is memory-efficient even for large-scale datasets C_LIO_LIModelArray supports linear and nonlinear modeling and is extensible to more models C_LIO_LIModelArray facilitates easy statistical analysis of large-scale fixel data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/499631v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1946469org.highwire.dtl.DTLVardef@14c3076org.highwire.dtl.DTLVardef@10197beorg.highwire.dtl.DTLVardef@19170cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Development of Top-Down Cortical Propagations in Youth

Hierarchical processing requires activity propagating between higher and lower-order cortical areas. However, studies of brain development have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leveraged advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n=388). We found that propagations robustly ascend and descend the cortical hierarchy, and that top-down propagations become both more prevalent with cognitive control demands and with development in youth.

neuroscience↗

bioRxiv2022

Transcriptional Cartography Integrates Multiscale Biology of the Human Cortex

The cerebral cortex underlies many of our unique strengths and vulnerabilities - but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

neuroscience↗

bioRxiv2022

Asymmetric Signaling Across the Hierarchy of Cytoarchitecture within the Human Connectome

Cortical variations in cytoarchitecture form a sensory-fugal axis that systematically shapes regional profiles of extrinsic connectivity. Additionally, this axis is thought to guide signal propagation and integration across the cortical hierarchy. While human neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order connectome topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy indicating that bottom-up transitions were easier to complete compared to top-down transitions. Supporting analyses demonstrated that this asymmetry was underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Finally, we found that this asymmetry correlated with changes in intrinsic neuronal timescales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

neuroscience↗

bioRxiv2022

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

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

neuroscience↗

bioRxiv2022

Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

Background/PurposeMulticenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at 4 sites. Materials and MethodsImages were acquired on Siemens or Philips scanners at 3-tesla. Automated white matter lesion detection and whole brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites. ResultsRandom effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric modeling indicated that site explained > 50% of the variation for most estimated volumes. This expanded to > 75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intra-scanner differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact. ConclusionDifferences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating inter-scanner differences show promise in reducing biases in multi-center studies of MS.

neuroscience↗

bioRxiv2022

Similarity-Based Multimodal Regression

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distancebased method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations in multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

neuroscience↗

bioRxiv2022

CoCoA: Conditional Correlation Models with Association Size

SO_SCPLOWUMMARYC_SCPLOWMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

bioinformatics↗

bioRxiv2022

Voxel-wise Intermodal Coupling Analysis of Two or More Modalities using Local Covariance Decomposition

When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.

neuroscience↗

bioRxiv2022

Penalized Decomposition Using Residuals (PeDecURe) for Nuisance Variable Adjustment in Multivariate Pattern Analysis

SO_SCPLOWUMMARYC_SCPLOWIn neuroimaging studies, multivariate methods provide a framework for studying associations between complex patterns distributed throughout the brain and neurological, psychiatric, and behavioral phenotypes. However, mitigating the influence of nuisance variables, such as confounders, remains a critical challenge in multivariate pattern analysis (MVPA). In studies of Alzheimers Disease, for example, imbalance in disease rates across age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly adjusted for, nuisance variables can obscure interpretations and preclude the generalizability of findings from neuroimaging studies. Motivated by this critical issue, in this work we examine the impact of nuisance variables on features extracted from image decomposition methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new MVPA method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between residualized imaging features and a variable of interest (e.g., Alzheimers diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between residualized imaging features and those variables. Using features estimated using PeDecURes first direction of variation, we train an accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower partial correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by MVPA in the context of neuroimaging, it is broadly applicable to datasets where the dimensionality or complexity of the covariance structure calls for novel methods to handle sources of nuisance variation.

neuroscience↗

bioRxiv2021

Harmonizing Functional Connectivity Reduces Scanner Effects in Community Detection

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

neuroscience↗

bioRxiv2021

White Matter Signals Reflect Information Transmission Between Brain Regions During Seizures

White matter supports critical brain functions such as learning and memory, modulates the distribution of action potentials, and transmits neural information between brain regions. Notably, neuronal cell bodies exist in deeper white matter tissue, neurotransmitter vesicles are released directly in white matter, and white matter blood-oxygenation level dependent (BOLD) signals are detectable across a range of different tasks--all appearing to reflect a dynamic, active tissue from which recorded signals can reveal meaningful information about the brain. Yet, the signals within white matter have largely been ignored. Here, we elucidate the properties of white matter signals using intracranial EEG in a bipolar montage. We show that such signals capture the communication between brain regions and differentiate pathophysiologies of epilepsy. In direct contradiction to past assumptions that white matter functional signals provide little value, we show that white matter recordings can elucidate brain function and pathophysiology. Broadly, white matter functional recordings acquired through implantable devices may provide a wealth of currently untapped knowledge about the neurobiology of disease.

neuroscience↗

bioRxiv2021

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in association cortex that has been implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. Capitalizing on a large sample with 27-minutes of high-quality functional MRI data (n=790, ages 8-23 years) and advances in machine learning, we examined associations between functional topography and four correlated dimensions of psychopathology (fear, psychosis, externalizing, anxious-misery) as well as an overall psychopathology factor. We found that functional topography significantly predicted individual differences in dimensions of psychopathology, driven mainly by robust associations between topography and overall psychopathology. Reduced cortical representations of association networks were among the most important features of the model. Our results emphasize the value of considering systematic differences in functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

neuroscience↗