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Maldjian, J. A.

Publications and source records attributed to Maldjian, J. A..

10 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↗
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↗
bioRxiv2019

BrainNET: Inference of brain network topology using Machine Learning

ObjectiveTo develop a new fMRI network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. MethodsBrainNET is based on Extremely Randomized Trees (ERT) to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open source simulated fMRI data of fifty subjects in twenty-eight different simulations under various confounding conditions with known ground truth was used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available Attention-deficit/hyperactivity disorder (ADHD) dataset including 134 Typically Developing Children (mean age: 12.03, males: 83), 75 ADHD Inattentive (mean age: 11.46, males: 56) and 93 ADHD Combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. ResultsBrainNET demonstrated excellent performance across all simulations and varying confounders in identifying true presence of connections. In the ADHD dataset, BrainNET was able to identify significant changes (p< 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups was identified using correlation and PC.

bioengineering↗
bioRxiv2019

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm (xgboost) with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrasted this method can achieve excellent performance with DICE scores of 0.898, 0.784, 0.779 for whole tumor, tumor core and enhancing tumor respectively and accuracy 34.5 % for survuval prediction.

neuroscience↗
bioRxiv2019

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

neuroscience↗