Biology subjects

Rogers, B. P.

Publications and source records attributed to Rogers, B. P..

10 recordsLinked to original sources

bioRxiv2025

Subtyping psychotic disorders using a data-driven approach reveals divergent cortical and cellular signatures

Structural brain abnormalities in psychosis are well-replicated but heterogenous posing a barrier to uncovering the pathophysiology, etiology, and treatment of psychosis. To parse neurostructural heterogeneity and assess for the presence of anatomically-derived subtypes, we applied a data-driven method, similarity network fusion (SNF), to structural neuroimaging data in a broad cohort of individuals with psychosis (schizophrenia spectrum disorders (SSD) n=280; bipolar disorder with psychotic features (BD) n=101). SNF identified two transdiagnostic subtypes in psychosis (subtype 1: n=158 SSD, n=75 BD; subtype 2: n=122 SSD, n=26 BD) that exhibited divergent patterns of abnormal cortical surface area and subcortical volumes. Compared to controls (n=243), subtype 1 showed moderate enlargement of surface area in frontal and parietal areas and larger dorsal striatal volumes, whereas subtype 2 demonstrated markedly smaller surface areas in frontal and temporal areas and subcortical volumes, including hippocampus, amygdala, thalamus and ventral striatum. When comparing subtypes on clinical characteristics, subtype 2 had more severe negative symptoms, greater neuropsychological impairment, and lower estimated premorbid intellectual functioning compared to subtype 1. Integrating cell-type data imputed from gene expression in the Allen Human Brain Atlas revealed an association between interregional reductions in surface area and layer 5 glutamatergic neuron abundance, critical for corticostriatal network connectivity and cognitive function, whereas reductions in cortical thickness spatially coupled with glia cell and interneuron abundance, in subtype 2. These outcomes indicate that regional variations in surface area, linked to different cell-types than cortical thickness, may be an important biomarker for understanding the pathophysiological trajectories of psychotic disorders.

neuroscience↗

bioRxiv2025

Characterizing massa intermedia morphology in schizophrenia: associations with aging, neuropsychological functioning, and atypical hippocampal development

The massa intermedia (MI) is a midline structure connecting the left and right thalamus that typically develops during the 2nd trimester of pregnancy. Missing and smaller MI has been linked to neurodevelopmental disorders, including schizophrenia (SZ), and subtle deficits in cognition. However, findings are inconsistent and the association between MI and other anatomical variants linked to atypical brain development in SZ, including incomplete hippocampal inversion (IHI), is unclear. Presence/absence and morphology of the MI were ascertained on structural T1-weighted MRI images obtained at 3T in SZ (n = 223) and healthy individuals (n = 194) and compared between groups. Associations between MI morphology, cognitive function, and incomplete hippocampal inversion (IHI) were assessed. Prevalence of missing MI was 1.7% and did not differ between groups. MI was significantly smaller in SZ (p <.001). However, follow-up analyses revealed that smaller MI size in SZ was due to a significant Diagnosis x Age interaction characterized by a stronger negative age effect in SZ. IHI was significantly more common in individuals with missing MI. Neurocognition was not correlated with MI size when controlling for age and diagnosis. Stronger effects of age on MI size in SZ suggests that abnormal MI size measured in adulthood may not be a reliable static indicator of atypical neurodevelopment, but may reflect disease progression or accelerated aging. Missing MI was rare in our sample. Conversely, missing MI is associated with IHI suggesting a shared neurodevelopmental disruption in the 2nd trimester.

neuroscience↗

bioRxiv2024

Cerebellar-Prefrontal Connectivity Predicts Negative Symptom Severity Across the Psychosis Spectrum

BackgroundNegative symptom severity predicts functional outcome and quality life in people with psychosis. However, negative symptoms are poorly responsive to antipsychotic medication and existing literature has not converged on their neurobiological basis. Previous work in small schizophrenia samples has observed that lower cerebellar-prefrontal connectivity is associated with higher negative symptom severity and demonstrated in a separate neuromodulation experiment that increasing cerebellar-prefrontal connectivity reduced negative symptom severity. We sought to expand this finding to test associations between cerebellar-prefrontal connectivity with negative symptom severity and cognitive performance in a large, transdiagnostic sample of individuals with psychotic disorders. MethodsIn this study, 260 individuals with psychotic disorders underwent resting-state MRI and clinical characterization. Negative symptom severity was measured using the Positive and Negative Symptoms Scale, and cognitive performance was assessed with the Screen for Cognitive Impairment in Psychiatry. Using a previously identified cerebellar region as a seed, we performed seed to whole brain analyses and regressed connectivity against negative symptom severity, using age and sex as covariates. ResultsConsistent with prior work, we identified relationships between higher cerebellar-prefrontal connectivity and lower negative symptom severity (r=-0.17, p=.007). Higher cerebellar-prefrontal connectivity was also associated with better delayed verbal learning (r=.13, p=.034). ConclusionsOur results provide further evidence supporting the relationship between cerebellar-prefrontal connectivity and negative symptom severity and cognitive performance. Larger, randomized, sham-controlled neuromodulation studies should test if increasing cerebellar-prefrontal connectivity leads to reductions in negative symptoms in psychosis.

neuroscience↗

bioRxiv2023

A test of time-resolved functional MRI with subsecond event durations

The hemodynamic signal measured with functional neuroimaging (fMRI) is notoriously sluggish, making it challenging to draw inferences about brief mental events. Relative timing differences in the response, however, can distinguish between changes in the duration or intensity of underlying neural activity (Henson et al., 2002). Specifically, increases in stimulus duration are predicted to delay the response peak and increase its amplitude, whereas increases in stimulus intensity should affect only peak magnitude (Friston, 2005). Although these different relationships have been empirically demonstrated using stimulus durations of several seconds, it remains unclear whether similar effects can be reliably detected on the sub-second timescales typical of cognitive events. Here we tested these predictions using brief visual and auditory stimuli that varied in intensity and duration. In Experiment 1 (n=15), stimuli were presented at three durations (100, 300, and 900 ms) and three intensities in a slow event-related design with a rapid TR (625 ms). In Experiment 2 (n=14), the stimulus durations were extended (1000, 1200, and 1800 ms) to enhance signal strength. Fitting the observed fMRI signals to parameterised hemodynamic response functions revealed that changes in stimulus duration affected both peak latency and magnitude, whereas changes in intensity affected only peak magnitude. Reliable effects were observed for duration differences as small as 200 ms, particularly when the evoked responses were strong. These findings support the validity of existing hemodynamic models and identify conditions under which sub-second timing differences can be reliably detected with fMRI. They also support the use of time-resolved fMRI as a principled and practical tool for mental chronometry in cognitive neuroscience.

neuroscience↗

bioRxiv2020

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

PurposeDiffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. MethodsThe proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. ResultsRaw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. ConclusionThe proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

bioengineering↗

bioRxiv2020

Practical gradient non-linearity correction of multi-site diffusion weighted MRI with empirical field maps

BackgroundAchieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PurposeMagnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study TypeProspective observational study SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer Field Strength/Sequence3T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. AssessmentWe compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical TestsWilcoxon rank-sum test between uncorrected and corrected data. ResultsIn phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p<0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p<0.05). ConclusionOur method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

neuroscience↗

bioRxiv2019

Insula Functional Connectivity in Schizophrenia

The insula is structurally abnormal in schizophrenia, demonstrating robust reductions in gray matter volume, cortical thickness, and altered gyrification during prodromal, early and chronic stages of the illness. Despite compelling structural alterations, less is known about its functional connectivity, limited by studies considering the insula as a whole or only within the context of resting-state networks. There is evidence, however, from healthy subjects that the insula is comprised of sub-regions with distinct functional profiles, with dorsal anterior insula (dAI) involved in cognitive processing, ventral anterior insula (vAI) involved in affective processing, and posterior insula (PI) involved in somatosensory processing. The current study builds on this prior work and characterizes insula resting-state functional connectivity sub-region profiles in a large cohort of schizophrenia (N=191) and healthy (N=196) participants and hypothesizes specific associations between insula sub-region connectivity abnormalities and clinical characteristics related to their functional profiles. Functional dysconnectivity of the insula in schizophrenia is broadly characterized by reduced connectivity within insula sub-networks and hyper-connectivity with regions not normally connected with that sub-region, reflected in significantly greater similarity of dAI and PI connectivity profiles and significantly lower similarity of dAI and vAI connectivity profiles (p<.05). In schizophrenia, hypo-connectivity of dAI correlates with cognitive function (r=.18, p=.014), whereas hyper-connectivity between vAI and superior temporal sulcus correlates with negative symptoms (r=.27, p<.001). These findings reveal altered insula connectivity in all three sub-regions and converges with recent evidence of reduced differentiation of insula connectivity in schizophrenia, implicating functional dysconnectivity of the insula in cognitive and clinical symptoms.

neuroscience↗

bioRxiv2019

TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter Atlas

Reproducible identification of white matter tracts across subjects is essential for the study of structural connectivity of the human brain. The key challenges are anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the human/manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. The state-of-the-art for white matter atlas is the single population-averaged Johns Hopkins Eve atlas. A critical bottleneck with the Eve atlas framework is that manual labeling time is extensive and peripheral white matter regions are conservatively labeled. In this work, we developed protocols that will facilitate manual virtual dissection of white matter pathways, with the goals to be anatomically accurate, intuitive, reproducible, and act as an initial stage to build an amenable knowledge base of neuroanatomical regions. We analyzed reproducibility of the fiber bundles and variability of human raters using DICE correlation coefficient, intraclass correlation coefficient, and root mean squared error. The protocols at their initial stage have shown promising results on both typical 3T research acquisition Baltimore Longitudinal Study of Aging and high-acquisition quality Human Connectome Project datasets. The TractEM manual labeling protocols allow for reconstruction of reproducible subject-specific fiber bundles across the brain. The protocols and sample results have been made available in open source to improve generalizability and reliability in collaboration.

neuroscience↗

bioRxiv2019

Neural correlates of cardiac interoceptive accuracy across development: implications for social symptoms in autism spectrum disorders

BackgroundInteroception involves the processing of sensory information relevant to physiological functioning and is integral to building self-awareness, emotional states, and modulating social behaviors. With the role of interoception in emotional processing and social functioning, there is growing interest in characterizing interoception in autism spectrum disorder (ASD), yet, there are mixed results regarding cardiac interoceptive accuracy in ASD.\n\nMethodsIn this study, we explored the neural basis of cardiac interoception using an fMRI heartbeat counting task in order to assess neural correlates of primary interoception. We predicted that interoceptive-specific response in the insula, a \"hub\" for interoception, would be related to ASD symptomatology. We investigated the relationship of insula responses during cardiac interoceptive accuracy and a self/caregiver-reported autism-related symptom scale (Social Responsiveness Scale-2 (SRS)). Participants included 46 individuals with autism spectrum disorder (ASD) (age 8-54, mean= 19.43{+/-}10.68 years) and 54 individuals with typical development for comparison (TC, age 8-53, mean= 21.43{+/-}10.41 years).\n\nResultsWe found no significant difference in cardiac interoceptive accuracy or neural response to cardiac interoception in ASD. Several insula sudivisons had a curvilinear relationship to age, peaking in early adulthood. Interoceptive-specific insula response was associated with adult self-report SRS scores; this association differed by diagnostic group and self/other report.\n\nConclusionsThis work suggests that 1) there is no global deficit in interoception in ASD, but that integrating interoceptive cues with social information may distinguish individuals with ASD, and 2) there is a developmental trajectory for interoceptive processing in the insula that may be relevant for socio-emotional health.

neuroscience↗

bioRxiv2018

Validation of an automated segmentation algorithm for lower leg MR images, applied to sodium quantification

ObjectiveTo develop and validate an automated segmentation algorithm for the lower leg using a multi-parametric magnetic resonance imaging protocol.\n\nMethodsAn automated algorithm combining active contour and intensity-based thresholding methods was developed to identify skin and muscle regions from proton Dixon MR images of the lower leg. Tissue sodium concentration was then computed using contemporaneously acquired sodium images with calibrated phantoms in the field of view. Resulting sodium concentration measurements were compared to a gold standard manual segmentation in 126 scans.\n\nResultsMost cases had no observable errors in segmentation of muscle and skin. Six cases had minor errors that were not expected to affect quantification; in the worst, 126 mm2 (2%) of a muscle area of 8,042 mm2 was misclassified. In one case the algorithm failed to separate the tibia from the muscle compartment. Correlation between automated and manual measurements of sodium concentration was R2 = 0.84 for skin, R2 = 0.99 for muscle. Additionally, the RMSE was 2.4mM for skin and 0.5mM for muscle; the observed physiological range was 8.5 to 37.4mM.\n\nConclusionFor the purpose of estimating sodium concentrations in muscle and skin compartments, the automated segmentations provided equally accurate results compared to the more time-intensive manual segmentations. Sodium quantification serves as a biomarker for disease progression, which would assist with early diagnostic treatments. The proposed algorithm will improve workflow, reproducibility, and consistency in such studies.

physiology↗