Biology subjects

Reddy, N. A.

Publications and source records attributed to Reddy, N. A..

9 recordsLinked to original sources

bioRxiv2026

Whole-brain precision functional mapping of a proximal upper-extremity motor task

Upper-extremity motor control has been widely studied with hand-task functional magnetic resonance imaging (fMRI) in healthy and disease states. However, proximal-arm motor tasks, such as at the shoulder, are uncommon during fMRI due to practical limitations, though they are critical to a complete understanding of upper-extremity motor control. Previous fMRI studies of proximal upper-extremity control have generally used unconstrained movements or a single low force or torque condition, leaving unknown how distributed supraspinal brain activity scales with increasing proximal motor output. Here, we implemented a custom, MR-safe device to test supraspinal brain activity during three levels of isometric shoulder abduction in healthy individuals using a whole-brain fMRI acquisition. We conducted subject-specific analysis with repeated sampling of the controlled shoulder abduction task within each individual (i.e., "precision mapping"), as the exact localization of shoulder motor activity is relatively understudied. Our analysis showed spatial variability in subject-specific motor activation compared to group-level localization. We found that shoulder abduction had a significant effect on motor activity in the primary motor cortex, supplementary motor area, dorsal premotor area, thalamus, putamen, and cerebellum. Across repeated fMRI runs, amplitude of motor activity was consistent across all tested regions. Overall, these findings provide an individualized, whole-brain torque-response characterization of the proximal upper-extremity motor system and establish an approach for examining how distributed supraspinal motor networks are recruited as mechanical demand increases. These whole-brain, subject-specific methods can be a critical tool for probing motor dysfunction in clinical populations.

neuroscience↗

bioRxiv2026

Evaluating Approaches for Inference Testing of Whole-Brain Densely Sampled Single-Subject Task fMRI Data

Task-based precision mapping has become a promising technique in functional MRI (fMRI) to robustly characterize and map an individuals unique activity patterns. These experiments consist of acquiring extensive imaging data in one participant, ultimately improving the sensitivity and specificity of individual-specific functional localization. Despite its advantages, studies have primarily focused on understanding individual-specific cortical activation, preventing a holistic view of a systems-level functional response, and to date, best approaches for the statistical analysis of controlled task-based, densely sampled, whole-brain data have not yet been fully established. Therefore, in this study, we collected whole-brain (i.e. covering cortex, cerebellum, and brainstem) multi-echo densely sampled data of the auditory system, a system with major subcortical components, and evaluated activation sensitivity as well as activation stability across data subsets of commonly-used whole-brain and region-specific inference testing approaches. The whole-brain approaches involved standard voxel-level and cluster-level inference schemes with varying statistical thresholds and a non-parametric permutation inference approach. The region-specific approaches involved an exploratory top % t-statistics methods and non-parametric permutation inference approaches. We found that a whole-brain voxel-level approach with a false discovery rate (FDR) correction (p<0.05) presented highest sensitivity across regions and subjects as well as most consistent detection of expected auditory regions, even with lower scan duration. In addition, we found that a region-specific top % t-statistic approach may be a useful exploratory functional localization tool and a complementary method to standard inference testing approaches.

bioengineering↗

bioRxiv2026

Stretch-Evoked Motor Responses in the Brainstem are Modulated by Task Instructions

Reliable noninvasive measurement of human brainstem activity during motor control remains challenging due to small anatomical structures and physiological noise, yet it is essential for understanding descending contributions to rapid feedback control. We used brainstem-optimized whole brain functional magnetic resonance imaging (fMRI) to examine whether task-dependent modulation of stretch-evoked motor responses in humans are associated with changes in activation within reticulospinal regions of the human brainstem. A behavioral validation experiment (N=10), in which participants were instructed to resist or yield to brief wrist perturbations, confirmed task-dependent modulation of long-latency responses (LLRs). In a separate imaging cohort (N=26), participants performed the same tasks during fMRI using an MRI-compatible robotic device and a multi-echo acquisition with physiological noise compensation. Imaging analyses revealed greater blood-oxygen-level-dependent signal during Resist compared to Yield while controlling for background contraction and proprioceptive input, with activation distributed bilaterally across the pons and medulla in regions consistent with major reticulospinal nuclei. Laterality analyses demonstrated a rostrocaudal gradient, with relatively ipsilateral-biased activation in the medulla shifting toward more contralateral patterns in the pons. These findings indicate that instruction-dependent modulation of stretch-evoked motor responses is associated with measurable changes in human brainstem activation, providing evidence that reticulospinal regions contribute to task-dependent feedback control.

neuroscience↗

bioRxiv2025

Mapping Whole-Brain Auditory Activation with 3T Multi-Echo fMRI at the Group and Individual-Subject Level

Magnetic resonance imaging (MRI) is a powerful and established tool to non-invasively probe the human auditory system. Varied blood oxygen level-dependent functional MRI (BOLD fMRI) acquisitions have been used to examine the functional roles of this system, but these acquisitions have substantial limitations, such as the need for specialized hardware and long acquisition times, and they typically rely on group averaging of activation patterns. In recent years, whole-brain multi-echo (ME) fMRI techniques have been used to reduce artifacts and scan times, map entire sensory systems, and improve sensitivity to neural activity in both resting-state and task fMRI data acquired at 3T. Combined with dense-sampling strategies, these ME techniques have facilitated "precision mapping" of neural activity in individual subjects. Thus, in this technical note we propose the use of a commonly available ME wholebrain acquisition and ME denoising approaches to examine the auditory system in both group and densely sampled single-subject datasets. Whole-brain and region-specific analyses were performed to identify auditory regions of activation. At the group level, auditory activation was identified bilaterally in cortical regions and unilaterally in cerebellar lobules VIIb/VIIIa with both analyses. Additionally, the region-specific analysis successfully identified unilateral activation in thalamic and brainstem regions. At the individual subject-level, precision mapping combined with ME denoising methods enhanced sensitivity, yielding bilateral activation in cortical, cerebellar, thalamic, and brainstem regions with both analyses. Lastly, we demonstrate the benefits of using multi-echo methods and a whole-brain precision mapping approach to better align an individuals functional response to their specific anatomy.

bioengineering↗

bioRxiv2025

Impact of multi-echo ICA modeling decisions on motor-task fMRI analysis

Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods using rejected ME-ICA components as regressors in subject-level modeling: Aggressive (simple inclusion of ME-ICA regressors), Moderate (excluding task-correlated ME-ICA regressors from the model), and Conservative (orthogonalization of ME-ICA regressors to the core model and accepted ME-ICA components). We applied these methods to data from healthy and multiple sclerosis populations that included performance of hand-grasp, shoulder-abduction, and ankle-flexion tasks. We found that when the amount of head motion and its correlation with the task was high and the expected task-evoked signal was relatively low, the Conservative method led to significantly higher activation, t-statistics, and test-retest similarity in motor regions compared to the Aggressive method. Future motor-task studies may wish to implement similar models to prevent loss of motor signal, while still mitigating the effects of task-correlated head motion.

bioengineering↗

bioRxiv2025

Tactile Pressure Evokes a Biphasic BOLD Response in Ipsilateral Primary Somatosensory Cortex

Tactile perception is fundamental to how we engage with and interpret our surroundings. While the contralateral primary somatosensory cortex (S1) is thought to be generally responsible for processing tactile information, simultaneous responses in the ipsi-lateral hemisphere have been observed. This work aims to characterize the blood-oxygen-level-dependent (BOLD) response pattern of ipsilateral S1 to unilateral tactile stimuli. In this study, tactile stimuli were applied using a custom pneumatically actuated stimulator designed and built in house (KalantaryArdebily et al., 2024). Three stimulus force levels were applied in an event-related design. As expected we observed an apparent difference in BOLD responses between the contralateral and ipsilateral hemispheres. We found differing response patterns between the Brodmanns areas (BA) within ipsilateral S1. Ipsilateral BA2 had a positive BOLD response similar to that of the contralateral hemisphere. In contrast, ipsilateral BA1 and BA3b appeared to have a biphasic response. That is, those regions had an initial negative response, followed by a secondary positive one. Using multi-echo analysis, we verified that this biphasic response pattern is BOLD-related. In addition, the secondary (positive) phase was more sensitive to tactile stimulus force than the initial "negative" BOLD phase. The ipsilateral BA1 and BA3b responses support a previous hypothesis of bilateral sensory gating (Blatow et al., 2007; Chung et al., 2014; Hamalainen et al., 2000; Kastrup et al., 2008; Klingner et al., 2016; Schafer et al., 2012; Tame et al., 2015). This study extends previous research reporting negative responses in ipsilateral S1, suggesting that the response is BOLD-related, biphasic, and that the previously overlooked secondary positive phase is actually more sensitive to stimulus intensity.

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

Simultaneous cortical, subcortical, and brainstem mapping of sensory activation

Non-painful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging (fMRI) studies have highlighted the value of whole-brain, systems-level investigation for examining pain processing. However, whole-brain fMRI studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, the differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo fMRI acquisition at 3T with multi-echo independent component analysis (ME-ICA) denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to non-painful brushing of the right hand, left hand, and right foot, and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we were able to differentiate the small, adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

neuroscience↗

bioRxiv2023

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinsons disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

neuroscience↗