Biology subjects

Hemmerling, K. J.

Publications and source records attributed to Hemmerling, K. J..

7 recordsLinked to original sources

bioRxiv2026

Multimodal MRI Clustering Reveals an Elevated Free-Water Biotype in Autism with Increased Social Symptoms

Background: Autism spectrum disorder (ASD) is neurobiologically heterogeneous, and group comparisons may mask effects confined to subgroups. We tested whether distinct biotypes exist within ASD using unsupervised clustering of multimodal MRI. Methods: We analyzed 149 participants with ASD and 129 typically developing (TD) controls from the Autism Brain Imaging Data Exchange II (6 sites; age 5 to 64 years). Site-harmonized features comprised free water (FW), tissue anisotropy and diffusivity, diffusion tensor image analysis along the perivascular space (DTI-ALPS), cerebrospinal fluid (CSF) volumes and exploratory resting-state regional homogeneity. ASD participants were clustered with k-means using global (7 features) and regional (17 features) sets, with robustness tested by permutation and two further algorithms. TD participants were projected post hoc to test specificity. A composite imaging score was related to Social Responsiveness Scale (SRS) subscales, symptom severity and IQ using partial correlations with false discovery rate correction. Results: Clustering identified an elevated free-water (EFW) biotype comprising approximately 10% of ASD participants, defined by markedly increased FW (d = +4.91 versus remaining ASD participants), reduced tissue anisotropy, reduced DTI-ALPS and enlarged ventricles. It was recovered across feature sets and algorithms (permutation p < .0001) and departed from TD norms, unlike the remaining ASD participants. The composite score correlated with social-perceptual and communicative SRS subscales, most strongly social awareness (r = +0.253, q = .026), but not with symptom severity or IQ. FW related to social motivation in children and adolescents (r = +0.287, q = .037) but not adults. Limitations: Each person was scanned once, so we cannot tell whether these brain differences cause social difficulties or how they change with age. The subgroup was small, clinical data were incomplete, the sample was mostly male with a wide age range, and the findings await confirmation in an independent group. Free water can reflect inflammation, swelling or tissue loss, so its biological meaning remains uncertain. Conclusions: We identify an ASD subgroup defined by extracellular fluid excess and altered white matter that is associated with greater social difficulties. If replicated, this signature may help stratify autistic individuals in mechanistic studies and clinical trials.

neuroscience↗

bioRxiv2025

Data-driven denoising in spinal cord fMRI with principal component analysis

Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 9 regressors across four fMRI datasets: motor task (n=26), breathing task (n=27), and resting state (n=15 and n=10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), though the effectiveness may be dependent upon the acquisition parameters. However, group-level activation maps did not show a clear benefit from including SpinalCompCor regressors. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated. In general, denoising with SpinalCompCor regressors in place of physiological recording-derived regressors is only recommended when the latter are unavailable, as SpinalCompCor may not consistently reproduce recording-based denoising across datasets or acquisitions.

neuroscience↗

bioRxiv2025

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

neuroscience↗

bioRxiv2024

Quantitative mapping of cerebrovascular reactivity amplitude and delay with breath-hold BOLD fMRI when end-tidal CO2 quality is low

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically useful measure of cerebrovascular health. CVR is often measured using a breath-hold task to modulate blood CO2 levels during an fMRI scan. Measuring end-tidal CO2 (PETCO2) with a nasal cannula during the task allows CVR amplitude to be calculated in standard units (vascular response per unit change in CO2, or %BOLD/mmHg) and CVR delay to be calculated in seconds. The use of standard units allows for normative CVR ranges to be established and for CVR comparisons to be made across subjects and scan sessions. Although breath holding can be successfully performed by diverse patient populations, obtaining accurate PETCO2 measurements requires additional task compliance; specifically, participants must breathe exclusively through their nose and exhale immediately before and after each breath hold. Meeting these requirements is challenging, even in healthy participants, and this has limited the translational potential of breath-hold fMRI for CVR mapping. Previous work has focused on using alternative regressors such as respiration volume per time (RVT), derived from respiration-belt measurements, to map CVR. Because measuring RVT does not require additional task compliance from participants, it is a more feasible measure than PETCO2. However, using RVT does not produce CVR amplitude in standard units. In this work, we explored how to achieve CVR amplitude maps, in standard units, and CVR delay maps, when breath-hold task PETCO2 data quality is low. First, we evaluated whether RVT could be scaled to units of mmHg using a subset of PETCO2 data of sufficiently high quality. Second, we explored whether a PETCO2 timeseries predicted from RVT using deep learning allows for more accurate CVR measurements. Using a dense-mapping breath-hold fMRI dataset, we showed that both rescaled RVT and rescaled, predicted PETCO2 can be used to produce maps of CVR amplitude in standard units and CVR delay with strong absolute agreement to ground-truth maps. The rescaled, predicted PETCO2 regressor resulted in superior accuracy for both CVR amplitude and delay. In an individual with regions of increased CVR delay due to Moyamoya disease, the predicted PETCO2 regressor also provided greater sensitivity to pathology than RVT. Ultimately, this work will increase the clinical applicability of CVR in populations exhibiting decreased task compliance.

neuroscience↗

bioRxiv2024

MRI mapping of hemodynamics in the human spinal cord

Impaired spinal cord vascular function contributes to numerous neurological pathologies, making it important to be able to noninvasively characterize these changes. Here, we propose a functional magnetic resonance imaging (fMRI)-based method to map spinal cord vascular reactivity (SCVR). We used a hypercapnic breath-holding task to evoke a systemic vasodilatory response during concurrent blood oxygenation level-dependent fMRI. SCVR amplitude and hemodynamic delay were mapped at the group level as proof-of-concept of the approach, and in two highly-sampled participants to probe feasibility/stability of individual SCVR mapping. Across the group and individuals, a strong ventral SCVR amplitude was initially observed without accounting for local regional variation in the timing of the vasodilatory response. Shifted breathing traces were used to account for temporal differences in the vasodilatory response across the cord, producing maps of SCVR delay. These maps demonstrate distinct gray matter regions concordant with territories of arterial supply. The SCVR fMRI methods described here enable robust mapping of spatiotemporal hemodynamic properties of the human spinal cord. This noninvasive approach has the potential to provide early insight into pathology-driven vascular changes in the cord, which may precede and predict future irreversible tissue damage and guide the treatment of several neurological pathologies involving the spine.

physiology↗

bioRxiv2023

Spatial distribution of hand-grasp motor task activity in spinal cord functional magnetic resonance imaging

Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.

neuroscience↗

bioRxiv2022

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation

Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each raters masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each raters co-registered data. Agreement with the reference SC mask was associated with both rater (F(7,140) = 32.12, P < 2x10-16,{eta} 2 = 0.29) and dataset (F(20,140) = 20.58, P < 2x10-16,{eta} 2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p<0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8,160) = 23.57, P < 2x10-16,{eta} 2 = 0.24; dataset: F(20,160) = 22.00, P < 2x10-16,{eta} 2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.

neuroscience↗