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Yu, F. F.

Publications and source records attributed to Yu, F. F..

9 recordsLinked to original sources
bioRxiv2026

Somatosensory gating dysfunction is masked by cognitive variability in cognitively impaired individuals

Disruptions in somatosensory processing have been observed in cognitive impairment (CI), suggesting that alterations in sensory processing may emerge earlier during cognitive decline than previously recognized. Somatosensory gating (SG) is an automatic inhibitory mechanism that protects neural resources by suppressing responses to redundant, non-behaviorally relevant stimuli. Prior work has demonstrated exaggerated gamma SG and response amplitudes in the primary somatosensory cortex (S1) of individuals with Alzheimers disease-confirmed pathology, and these effects were masked by variability in attention/executive function performance. However, whether similar relationships are present during earlier stages of cognitive decline, such as CI, remains unclear. Herein, 63 cognitively healthy older adults (CH; mean age = 59.9 {+/-} 8.6 years) and 32 individuals with CI (mean age = 62.4 {+/-} 8.8 years) underwent magnetoencephalography (MEG) while completing a paired-pulse SG paradigm designed to probe inhibitory sensory processing. MEG oscillatory responses were source-imaged using a beamformer. Time series data were extracted from the peak voxel to quantify oscillatory dynamics and SG. Neuropsychological testing was conducted to assess attention/executive function. After controlling for attention/executive function variance, exaggerated gamma SG was observed in adults with CI compared with CH adults (p < 0.05). Additionally, adults with CI exhibited increased beta peak frequency following the second stimulation (p < 0.01) and a group-by-age interaction for theta SG in S1 (p < 0.05). Together, these results suggest somatosensory abnormalities are present in earlier stages of cognitive decline and highlight a dynamic interaction between sensory processing and cognitive systems during this decline.

neuroscience↗
bioRxiv2026

Reduced Somatosensory Oscillatory Dynamics and Inhibition in Moderate-to-Severe Nociceptive Pain

Nociceptive pain is the most common pain condition, and moderate-to-severe nociceptive pain substantially impacts daily functioning, constituting a significant public health burden. Despite this, most studies investigating the neural mechanisms underlying somatosensory processing and inhibition have focused on other pain conditions (e.g., neuropathic, nociplastic, or mixed pain). Thus, the extent to which neural aberrancies detected in these other populations extend to or differentiate from nociceptive pain conditions remains largely unknown. In this study, 29 individuals with moderate-to-severe nociceptive pain (MSNP) and 47 pain-free (PF) controls underwent magnetoencephalography (MEG) alongside a paired-pulse somatosensory stimulation paradigm to examine somatosensory cortical processing and functional inhibition. Pain status and intensity were determined using validated pain questionnaires, painDETECT and PROMIS-29, respectively. MEG oscillatory responses were source localized via a beamformer to the primary somatosensory cortex (S1) and time series data were extracted from the peak voxel to quantify the dynamics of somatosensory gating (SG; index of cortical inhibitory processing), oscillatory response power, and spontaneous power. We found that adults with MSNP exhibit aberrant theta SG in contralateral S1 compared to PF controls, reflecting reduced functional inhibition of innocuous stimulus processing in this region. Additionally, individuals with MSNP demonstrated exaggerated gamma responses but blunted alpha responses in contralateral S1 to innocuous stimulation. Finally, individuals with MSNP were characterized by weaker spontaneous alpha in contralateral S1 that scaled with self-reported pain intensity. Together, these findings suggest that experiencing MSNP is associated with disrupted somatosensory and cortical inhibitory processing.

neuroscience↗
bioRxiv2026

Age-Related Alterations in Multispectral Somatosensory Gating: Evidence for Partial Compensation in Attentional Performance

Healthy cognitive aging involves selective changes, with relative preservation of some domains and decline in others, particularly attention, inhibitory control, and executive function. Somatosensory gating (SG) refers to the brains ability to suppress neural responses to redundant tactile input, conserving resources for relevant stimuli and reflects pre-attentive and inhibitory mechanisms. Prior region-of-interest studies have shown age-related reductions in gamma SG within contralateral primary somatosensory cortex (S1), and modulation of theta, alpha, and beta SG by attention in young adults. However, whole-brain, multispectral age effects remain unclear. In this study, 63 middle-to-older aged adults (38 females; mean age = 59.9 {+/-} 8.6 years) underwent magnetoencephalography during a paired-pulse somatosensory paradigm. SG was quantified as attenuation of the neural response to the second stimulus relative to the first. Time-frequency analyses identified theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz), and gamma (30-90 Hz) oscillatory responses, and band-specific voxel-wise whole-brain gating maps assessed age-related effects. Attention/executive function was also measured. Results showed significant age-related increases in gamma SG in the contralateral supplementary motor area. Mediation analyses suggested this increase partially offsets age-related declines in attention/executive function, consistent with a partial compensatory mechanism. Additionally, theta SG in contralateral S1 increased with age. These findings demonstrate frequency- and region-specific age-related alterations in SG, suggesting that older adults may recruit enhanced inhibitory mechanisms, particularly in higher-order sensorimotor regions, to support cognitive function.

neuroscience↗
bioRxiv2021

Single-orientation Quantitative Susceptibility Mapping identifies the Ventral Intermediate Nucleus of the Thalamus

IntroductionThe ventral intermediate nucleus (VIM) represents the primary target in the treatment of tremor. Accurate localization is extremely important given its proximity to other thalamic nuclei. We utilized single orientation quantitative susceptibility mapping (QSM) at 3T to directly visualize the VIM. MethodsFour adult volunteers, one adult cadaver, and an essential tremor patient were scanned on a 3T MRI using a multi-echo gradient echo sequence. QSM images were generated using the improved sparse linear equation and least-squares (iLSQR) algorithm. Two adult subjects underwent multiple head orientation imaging for multi-orientation QSM reconstruction. The VIM was prospectively identified with direct visualization as well as indirect landmark-based localization. ResultsThe bilateral VIM was consistently identified as a hypointense structure within the lateral thalamus, appearing similar on multi-orientation and single-orientation QSM, corresponding to the myelinated dentatorubrothalamic tract (DRTT). The indirect method resulted in a comparatively inferomedial localization, at times missing the VIM and DRTT. ConclusionSingle-orientation QSM offers a clinically feasible, non-invasive imaging-based approach to directly localize the VIM.

neuroscience↗
bioRxiv2021

Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps

PurposeA major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. MethodsThree adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. ResultsSubject #1s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. ConclusionWe have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.

bioengineering↗
bioRxiv2020

A Novel Fully Automated MRI-Based Deep Learning Method For Classification Of 1p/19q Co-Deletion Status In Brain Gliomas

BackgroundOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network. MethodsMulti-parametric brain MRI and corresponding genomic information were obtained for 368 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 1p/19 co-deletions were present in 130 subjects. 238 subjects were non co-deleted. A T2w image only network (1p/19q-net) was developed to perform 1p/19q co-deletion status classification and simultaneous single-label tumor segmentation using 3D-Dense-UNets. Threefold cross-validation was performed to generalize the network performance. ROC analysis was also performed. Dice-scores were computed to determine tumor segmentation accuracy. Results1p/19q-net demonstrated a mean cross validation accuracy of 93.46% across the 3 folds (93.4%, 94.35%, and 92.62%, standard dev=0.8) in predicting 1p/19q co-deletion status with a sensitivity and specificity of 0.90 {+/-}0.003 and 0.95 {+/-}0.01, respectively and a mean AUC of 0.95 {+/-}0.01. The whole tumor segmentation mean Dice-score was 0.80 {+/-} 0.007. ConclusionWe demonstrate high 1p/19q co-deletion classification accuracy using only T2-weighted MR images. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment. Keypoints1. 1p/19 co-deletion status is an important genetic marker for gliomas. 2. We developed a non-invasive, MRI based, highly accurate deep-learning method for the determination of 1p/19q co-deletion status that only utilizes T2 weighted MR images IMPORTANCE OF THE STUDYOne of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Currently, the only reliable way to determine 1p/19q mutation status requires analysis of glioma tissue obtained either via an invasive brain biopsy or following open surgical resection. The ability to non-invasively determine 1p/19q co-deletion status has significant implications in determining therapy and predicting prognosis. We developed a highly accurate, deep learning network that utilizes only T2-weighted MR images and outperforms previously published imagebased methods. The high classification accuracy of our T2w image only network (1p/19q-net) in predicting 1p/19q co-deletion status marks an important step towards image-based stratification of brain gliomas. Imminent clinical translation is feasible because T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.

neuroscience↗
bioRxiv2020

Brain Tumor IDH, 1p/19q, and MGMT Molecular Classification Using MRI-based Deep Learning: Effect of Motion and Motion Correction

Deep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For IDH classification, an accuracy of 99% was achieved, representing a new benchmark in non-invasive image-based IDH classification and exceeding the original performance of the network. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization. STATEMENT OF SIGNIFICANCEDeep learning networks have shown promise for predicting molecular profiles of gliomas using MR images. We demonstrate that patient motion artifact, which is frequently encountered in the clinic, can significantly impair the performance of these algorithms. The application of robust motion correction algorithms can restore the performance of these networks, rivaling tissue-based characterization.

neuroscience↗
bioRxiv2020

MRI-BASED DEEP LEARNING METHOD FOR DETERMINING METHYLATION STATUS OF THE O6 METHYLGUANINE DNA METHYLTRANSFERASE PROMOTER OUTPERFORMS TISSUE BASED METHODS IN BRAIN GLIOMAS

PURPOSEMethylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) promoter results in epigenetic silencing of the MGMT enzyme and confers an improved prognosis and treatment response in gliomas. The purpose of this study was to develop a deep-learning network for determining the methylation status of the MGMT Promoter in gliomas using T2-w magnetic resonance images only. METHODSBrain MRI and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). 163 subjects had a methylated MGMT promoter. A T2-w image only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single label tumor segmentation. The network was trained using 3D-Dense-UNets. Three-fold cross-validation was performed to generalize the networks performance. Dice-scores were computed to determine tumor segmentation accuracy. RESULTSMGMT-net demonstrated a mean cross validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, standard dev=0.66) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% {+/-}0.04 and 91.66% {+/-}2.06, respectively and a mean AUC of 0.93 {+/-}0.01. The whole tumor segmentation mean Dice-score was 0.82 {+/-} 0.008. CONCLUSIONWe demonstrate high classification accuracy in predicting the methylation status of the MGMT promoter using only T2-w MR images that surpasses the sensitivity, specificity, and accuracy of invasive histological methods such as pyrosequencing, methylation-specific PCR, and immunofluorescence methods. This represents an important milestone toward using MRI to predict glioma histology, prognosis, and response to treatment.

neuroscience↗