Biology subjects

Shinohara, R.

Publications and source records attributed to Shinohara, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies

Neuroimaging studies that pool data across clinical sites often suffer from site effects --- variability in imaging measures that arises from technical heterogeneity across sites as opposed to true biological signal. While numerous harmonization methods have been proposed to remove site effects from imaging features, less attention has been placed on how to incorporate harmonization models into common machine-learning pipelines. We demonstrate that current approaches for integration either suffer from data leakage, fail to fully remove site effects from the target data, or attenuate true biological associations during the harmonization process. To address these issues, we propose Multiple Imputation for Removing Technical Heterogeneity (MIRTH), which uses imputed outcomes to harmonize the test data. We benchmark MIRTH's performance using both simulated and real-world volumetric data from the Alzheimer's Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging, demonstrating that MIRTH can achieve high predictive accuracy while avoiding inflated performance when the outcome is imbalanced across clinical sites.

neuroscience↗

bioRxiv2026

A Test for Confounding in Coupling of Multimodal Neuroimaging Data

Multimodal neuroimaging studies often involve comparisons between brain maps. Recently, statistical methods have been proposed to quantify and assess spatial correspondence between two modalities. The simple permutation-based inter-modal correspondence (SPICE) test evaluates whether within-subject correspondence exceeds chance, where the null distribution is constructed by permuting subject labels for one modality. Despite its easy implementation and minimal spatial assumptions, the critical assumption underlying permutation analysis, that subjects are exchangeable under the null, may be violated when covariates such as age, sex, or disease status systematically alter brain map distributions. This violation can produce results analogous to Simpson's paradox, where apparent population-level correspondence reflects between-group differences rather than genuine within-subject coupling. We propose a formal U-statistic-based test for such covariate effects, enabling both diagnostic evaluation of assumption violations and scientific discovery. Using synthetic and semi-synthetic neuroimaging data, we demonstrate well-controlled Type I error and high statistical power. We apply our method to test for confounding effects due to age and sex using real data from two pairs of imaging modalities in the Philadelphia Neurodevelopmental Cohort. Our framework increases the rigor and interpretability of intermodal coupling analyses, with broad implications for neuroimaging studies in heterogeneous populations, especially in developmental, aging, and disease-focused research.

neuroscience↗

bioRxiv2026

Neuroanatomical Subtypes of Callous-Unemotional Traits in a Community Sample of Youth

ImportanceCallous Unemotional (CU) traits are associated with significant clinical and neurophysiological heterogeneity that may affect treatment effectiveness. ObjectiveTo uncover neuroanatomical subtypes of CU traits using weakly-supervised machine learning and assess whether emerging subtypes differ on relevant clinical, temperamental, and environmental constructs. Design, Setting, and ParticipantsImaging data was from the longitudinal Adolescent, Brain, Cognitive Development (ABCD) Study. Participants were 9-10 years old at baseline (M=9.925, 69.9% male) and included 222 children with CU traits and 234 typically developing controls matched on age, sex and income. Main Outcomes and MeasuresThe weakly-supervised heterogeneity through discriminative analysis (HYDRA) model was trained on grey matter (GM) volumes from 84 regions of interest (ROIs) and tested for reproducibility using cross-validation and permutation testing. Derived subtypes were compared cross-sectionally and prospectively on relevant clinical, temperamental, and environmental measures and subsequent GM volume at 2-year follow up. ResultsHYDRA revealed an optimal 2-subtype solution within children with CU traits. Subtypes showed comparable levels of aggression that were significantly higher than typically developing controls. At the same time, subtype 1 had larger GM volume, fewer internalizing symptoms, and less adversity exposure, while subtype 2 was characterized by smaller GM volume, more internalizing symptoms, and more adversity exposure. Conclusions and RelevanceThis study provides evidence of neuroanatomically distinct subtypes of CU traits characterized by different clinical and etiological profiles, with implications for diagnosis and treatment.

neuroscience↗

bioRxiv2026

Brain charts for neuroanatomical sex differences across the human lifespan

Population-level sex differences in human neuroanatomy have been highly debated, with prior literature limited by narrow age windows and assumptions about regions scaling with total brain size. Building on recent work charting brain structures across the lifespan, we use magnetic resonance imaging data from over 100,000 individuals (51.7% F) to model normative reference trajectories of 241 structural features capturing age-varying sex effects. Results show replicable age-varying sex differences in nearly all features, and sexs effects survive nonlinear correction for total brain size, with sex biases tending to increase across the lifespan. Notably, models are sensitive to atypicalities in six neuropsychiatric disorders. This work establishes and quantifies population-level sex biases in the largest-to-date study of sex differences in the human brain.

neuroscience↗

bioRxiv2026

Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC

Batch effect correction is a common and often necessary step in data analysis to reduce bias due to technical and experimental factors when combining multiple batches of data. The severity of the batch effects dictates the correction strategy; therefore, a careful assessment of each datasets batch effects is necessary. BatchQC is an R package that provides reproducible tools and visualizations for quantitatively and qualitatively addressing batch effects across a broad range of data types. BatchQC integrates with standardized Bioconductor data structures and features an object-oriented design, enabling the application of workflows that can freely evaluate and process data within and outside the package tools. Common batch evaluation methods, along with novel quantitative metrics, help determine the benefits of batch correction for each dataset and enable direct comparisons between methods. Here, we present BatchQC as the first comprehensive batch-correction R package, with independent tools, reproducible workflows, visualization, and novel statistics.

bioinformatics↗

bioRxiv2025

Conditional-longitudinal brain growth charts detect MRI changes with birth weight and psychopathology

ImportanceBrain maturation varies between individuals, particularly during dynamic developmental periods like adolescence. Directly assessing differences in longitudinal trajectories can reveal deviations from normative patterns. ObjectiveWe present novel conditional-longitudinal normative models that characterize variability in brain maturation. We utilize these models to examine whether differences in longitudinal trajectories are associated with birth weight (BW), gestational age (GA), and longitudinal psychopathology derived from behavioral assessments. DesignCross-sectional and conditional-longitudinal normative models were developed for brain volumes derived from the first two neuroimaging timepoints from the Adolescent Brain Cognitive Development (ABCD) Study. Conditional-longitudinal models index an individuals expected brain volume at follow-up conditioned on their baseline measurement. Models were fit with split-half cross-validation on demographically matched samples. SettingThe ABCD Study is a multi-site, population-based study ParticipantsParticipants were excluded based on imaging quality flags and missing data, leaving 10,830 at baseline and 7,262 at follow-up. ExposuresBW and GA were derived from parent-report questionnaires. General psychopathology scores were calculated using a bifactor model. Main Outcomes and MeasuresWe calculated cross-sectional and conditional-longitudinal centiles, respectively quantifying individual deviations in size and change between timepoints. Sensitivity analyses included covariates for parental income and education as well as current weight and height. ResultsThe sample was 10,830 at baseline (48.2% F,age 9-10y) and 7,262 at follow-up (46.6% F,age 11-13y). Conditional-longitudinal centiles were sensitive to individual differences in brain change between timepoints. Lower BW was associated with lower conditional-longitudinal centiles, suggesting larger decreases in brain volumes over time (27 regions pfdr<0.05, {beta}max=0.08). Lower conditional-longitudinal centiles were associated with greater increases in psychopathology scores, suggesting with increased psychopathology brain volumes show greater decrease (37 regions pfdr<0.05, {beta}max=0.06). Notably, changes in psychopathology were not related to brain size at either timepoint, indexed by cross-sectional centiles. Conclusions and RelevanceModels that capture individual-level deviations from expected growth trajectories, rather than static positions on a growth curve, are particularly informative for assessing developmental change. Novel conditional-longitudinal models address this gap in lifespan brain imaging. Using this framework, we demonstrate robust associations between individual trajectory deviations, perinatal adversity, and longitudinally assessed mental health symptoms. Condition-longitudinal models hold promise for applications across psychiatric neuroscience, from development to aging. Key Points QuestionHow do differences in brain maturation trajectories, quantified by novel conditional-longitudinal models, relate to perinatal factors and mental health in adolescence? FindingsIn this longitudinal analysis of neuroimaging data from the Adolescent Brain Cognitive Development (ABCD) Study, conditional-longitudinal normative models revealed that trajectories of brain maturation in adolescence are associated with birth weight, and with longitudinal changes in mental health. MeaningConditional longitudinal models detect inter-individual variability in brain maturation, which is related to both perinatal factors and concurrent changes in psychopathology.

neuroscience↗

bioRxiv2025

Volitional and forced running ability in mice lacking intact primary motor cortex

The coordination of various brain regions achieves both volitional and forced motor control, but the role of the primary motor cortex in proficient running motor control remains unclear. This study trained mice to run at high performance (>10,000 rotations per day or >2,700 rotations per hour) using a running wheel, and then assessed the effects of the removal of bilateral cortical areas including the primary motor cortex on volitional and forced running locomotion. The control sham-operated group revealed a quick recovery of volitional running, reaching half of the maximum daily rotation in 3.9 +/-2.6 days (n = 10). In contrast, the cortical injury group took significantly a longer period (7.0 +/-3.3 days, n = 15) to reach half of the maximum volitional daily rotation, but recovered to preoperative levels in about two weeks. Furthermore, even 3 days after surgery to remove cortical regions, the running time on a treadmill moving at 35.3 cm/sec, which is difficult for naive mice to run on, was not significantly different from that in the sham-operated group. These results suggest that the intact primary motor cortex is not necessarily required to execute trained fast-running locomotion, but rather contributes to the spontaneity of running in mice.

neuroscience↗

bioRxiv2024

dGAMLSS: An exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts

There is growing interest in estimating population reference ranges across age and sex to better identify atypical clinically-relevant measurements throughout the lifespan. For this task, the World Health Organization recommends using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) which can model non-linear growth trajectories under complex distributions that address the heterogeneity in human populations. Fitting GAMLSS models requires large, generalizable sample sizes, especially for accurate estimation of extreme quantiles, but obtaining such multi-site data can be challenging due to privacy concerns and practical considerations. In settings where patient data cannot be shared, privacy-preserving distributed algorithms for federated learning can be used, but no such algorithm exists for GAMLSS. We propose distributed GAMLSS (dGAMLSS), a distributed algorithm which can fit GAMLSS models across multiple sites without sharing patient-level data. We demonstrate the effectiveness of dGAMLSS in constructing population reference charts across clinical, genomics, and neuroimaging settings.

bioinformatics↗

bioRxiv2024

ComBatLS: A location- and scale-preserving method for multi-site image harmonization

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

neuroscience↗

bioRxiv2023

Evaluation of resampling-based inference for topological features of neuroimages

Many recent studies have demonstrated the inflated type 1 error rate of the original Gaussian random field (GRF) methods for inference of neuroimages and identified resampling (permutation and bootstrapping) methods that have better performance. There has been no evaluation of resampling procedures when using robust (sandwich) statistical images with different topological features (TF) used for neuroimaging inference. Here, we consider estimation of distributions TFs of a statistical image and evaluate resampling procedures that can be used when exchangeability is violated. We compare the methods using realistic simulations and study sex differences in life-span age-related changes in gray matter volume in the Nathan Kline Institute Rockland sample. We find that our proposed wild bootstrap and the commonly used permutation procedure perform well in sample sizes above 50 under realistic simulations with heteroskedasticity. The Rademacher wild bootstrap has fewer assumptions than the permutation and performs similarly in samples of 100 or more, so is valid in a broader range of conditions. We also evaluate the GRF-based pTFCE method and show that it has inflated error rates in samples less than 200. Our R package, pbj, is available on Github and allows the user to reproducibly implement various resampling-based group level neuroimage analyses.

neuroscience↗

bioRxiv2023

XCP-D: A Robust Pipeline for the post-processing of fMRI data

Functional neuroimaging is an essential tool for neuroscience research. Pre-processing pipelines produce standardized, minimally pre-processed data to support a range of potential analyses. However, post-processing is not similarly standardized. While several options for post-processing exist, they tend not to support output from disparate pre-processing pipelines, may have limited documentation, and may not follow BIDS best practices. Here we present XCP-D, which presents a solution to these issues. XCP-D is a collaborative effort between PennLINC at the University of Pennsylvania and the DCAN lab at the University at Minnesota. XCP-D uses an open development model on GitHub and incorporates continuous integration testing; it is distributed as a Docker container or Singularity image. XCP-D generates denoised BOLD images and functional derivatives from resting-state data in either NifTI or CIFTI files, following pre-processing with fMRIPrep, HCP, and ABCD-BIDS pipelines. Even prior to its official release, XCP-D has been downloaded >3,000 times from DockerHub. Together, XCP-D facilitates robust, scalable, and reproducible post-processing of fMRI data.

neuroscience↗

bioRxiv2023

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

neuroscience↗

bioRxiv2022

Suboptimal phenotypic reliability impedes reproducible human neuroscience

Summary ParagraphBiomarkers of behavior and psychiatric illness for cognitive and clinical neuroscience remain out of reach1-4. Suboptimal reliability of biological measurements, such as functional magnetic resonance imaging (fMRI), is increasingly cited as a primary culprit for discouragingly large sample size requirements and poor reproducibility of brain-based biomarker discovery1,5-7. In response, steps are being taken towards optimizing MRI reliability and increasing sample sizes8-11, though this will not be enough. Optimizing biological measurement reliability and increasing sample sizes are necessary but insufficient steps for biomarker discovery; this focus has overlooked the other side of the equation - the reliability of clinical and cognitive assessments - which are often suboptimal or unassessed. Through a combination of simulation analysis and empirical studies using neuroimaging data, we demonstrate that the joint reliability of both biological and clinical/cognitive phenotypic measurements must be optimized in order to ensure biomarkers are reproducible and accurate. Even with best-case scenario high reliability neuroimaging measurements and large sample sizes, we show that suboptimal reliability of phenotypic data (i.e., clinical diagnosis, behavioral and cognitive measurements) will continue to impede meaningful biomarker discovery for the field. Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements12-15, which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web- and smart-phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings)16-20. We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large-scale samples. Although the current study has been motivated by ongoing developments in neuroimaging, the prioritization of reliable phenotyping will revolutionize neurobiological and clinical endeavors that are focused on brain and behavior.

neuroscience↗

bioRxiv2022

BOSS: Beta-mixture Unsupervised Oligodendrocytes Segmentation System

To develop reparative therapies for multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and the target of damage in MS. In vivo two-photon fluorescence microscopy allows direct visualization of oligodendrocytes in transgenic mouse models, and promises a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking oligodendrocytes requires extensive human effort and is especially challenging in three-dimensional images. While several models exist for automatically annotating cells in two-dimensional images, few models exist to annotate cells in three-dimensional images and even fewer are designed for tracking cells in longitudinal imaging. Furthermore, the complexity of processes and myelin formed by individual oligodendrocytes can result in the failure of algorithms that are specifically designed for tracking cell bodies alone. Here, we propose a novel beta-mixture unsupervised oligodendrocyte segmentation system (BOSS) that can segment and track oligodendrocytes in three-dimensional images over time that requires minimal human input. We evaluated the performance of the BOSS model on a set of eight images obtained longitudinally. We showed that the BOSS model can segment and track oligodendrocytes similarly to a blinded human observer. Our method offers many advantages, as it does not require fully curated data, reduces computational time, and most importantly recapitulates cell dynamic patterns observed by manually tracking oligodendrocytes. Although BOSS was developed to apply to our studies on oligodendrocytes, we anticipate that it can be modified to study four-dimensional in vivo data of any brain cell.

neuroscience↗

bioRxiv2022

Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence

With the increasing availability of neuroimaging data from multiple modalities--each providing a different lens through which to study brain structure or function--new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

neuroscience↗

bioRxiv2021

Privacy-preserving harmonization via distributed ComBat

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimers Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

neuroscience↗

bioRxiv2021

Differential impact of transdiagnostic, dimensional psychopathology on multiple scales of functional connectivity

IntroductionDimensional psychopathology strives to associate different domains of cognitive dysfunction with brain circuitry. Connectivity patterns as measured by functional magnetic resonance imaging (fMRI) exist at multiple scales, with global networks of connectivity composed of microscale interactions between individual nodes. It remains unclear how separate dimensions of psychopathology might differentially impact these different scales of organization. MethodsPatients experiencing anxious misery symptomology (depression, anxiety and trauma; n = 192) were assessed for symptomology and received resting-state fMRI scans. Three modeling approaches (seed-based correlation analysis [SCA], support vector regression [SVR] and Brain Basis Set Modeling [BSS]), each relying on increasingly dense representations of functional connectivity patterns, were used to associate connectivity patterns with six different dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia and negative affect. Importantly, a full 50 patients were held-out in a testing dataset, leaving 142 patients as training data. ResultsDifferent symptom dimensions were best modeled by different scales of brain connectivity: anhedonia and anxiety sensitivity were best modeled with single connections (SCA), insomnia and anxious arousal by mesoscale patterns (SVR) and negative affect and ruminative thought by broad, cortex-spanning patterns (BBS). Dysfunction within the default mode network was implicated in all symptom dimensions that were best modeled by multivariate models. ConclusionThese results suggest that symptom dimensions differ in the degree to which they impact different scales of brain organization. In addition to advancing our basic understanding of transdiagnostic psychopathology, this has implications for the translation of basic research paradigms to human disorders.

neuroscience↗

bioRxiv2021

Prevalence of floating toe and its relationship with static postural stability in children: The Yamanashi adjunct study of the Japan Environment and Childrens Study (JECS-Y)

Floating toe (FT) is a frequently seen condition in which a toe is inadequately in contact with the ground. Although toes play an important role in stabilizing standing posture and walking, many aspects of the effects of FT on the body remain unclear. To our knowledge, there have been no reports about the relationship between FT and postural stability, especially in children. This study aimed to clarify the prevalence of FT and its relationship with static postural stability in children. Of the 400 children aged 8 years who participated in our cohort study, 396, who were examined for static postural stability, were included in this study. Postural stability and FT were assessed using a foot pressure plate. The sway path length of the center of pressure and the area of the ellipse defined as the size of the area marked by the center of pressure were measured as an evaluation of static postural stability. We calculated the "floating toe score (FT score: small FT score indicates insufficient ground contact of the toes)" using the image of the plantar footprint obtained at the postural stability measurement. The FT rate was very high at more than 90%, and the FT score in the eyes-closed condition was significantly higher than that in the eyes-open condition in both sexes. The FT score significantly correlated with the center of pressure path and area. Our results suggest that ground contact of the toes is not directly related to static postural stability in children, but it may function to stabilize the body when the condition becomes unstable, such as when the eyes are closed.

physiology↗