Kanhao (Alex) Zhao

All Publications

• Brain-gut crosstalk associated with brain ageing in young and mid-life adults: a multicohort cross-sectional study. EBioMedicine Zhao, K., Vignolle, G. A., Labus, J. S., Mayer, E. A., Vaughan, A., Dy, M., Vora, P., Hung, M. W., Vossel, K., Gill, C., Del Rio, D., Stanton, C., Ross, R. P., Cryan, J. F., Kaddurah-Daouk, R., Zhang, Y., Church, A. 2026: 106468

  Abstract

  Brain age deviation is a promising neuroimaging biomarker of brain health, but its relevance in young and mid-life adults and its biological underpinnings remain insufficiently characterised. We aimed to test whether a functional-connectivity-derived brain ageing index (BAI) captures reproducible variability in early brain ageing and whether it is associated with cognitive-affective function and gut-derived biological signatures.We analysed resting-state fMRI from a discovery cohort (n = 674) with validation in a replication cohort (n = 444) and an independent cohort (n = 344). Whole-brain functional connectivity was computed using a 100-region Schaefer parcellation, and Bayesian ridge regression was used to predict chronological age; BAI was defined as the age-bias-corrected residual (predicted brain age minus chronological age). We tested associations between BAI and cognitive and affective measures across cohorts. In the independent cohort, we applied multi-view sparse partial least squares to integrate stool metagenomic and metabolomic profiles with BAI, and performed KEGG pathway enrichment analyses on features with non-zero weights.Predicted brain age correlated with chronological age across cohorts (r = 0.50-0.59). Higher BAI was consistently associated with connectivity patterns involving posterior cingulate/praecuneus and medial frontal regions, poorer cognitive performance, particularly working memory and executive function, and greater depressive symptoms. Multi-omics integration identified microbial taxa and stool metabolites, including ceramides, 24-hydroxycholesterol, dicarboxylic acids, and inverse associations with estetrol, linked to BAI. Enrichment analyses suggested involvement of neuroimmune, vascular, synaptic, and mitochondrial pathways.A connectivity-derived BAI captures reproducible variability in early brain ageing and links large-scale brain network organisation to gut-derived biological signatures. These findings suggest that BAI captures individual variability associated with brain health-related phenotypes and support the potential association of peripheral brain-gut biological pathways in early brain ageing.National Institutes of Health, National Institute on Ageing.

  View details for DOI 10.1016/j.ebiom.2026.106468

  View details for PubMedID 42716835

• Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD. Journal of affective disorders Arteaga, A., Tong, X., Zhao, K., Carlisle, N. B., Oathes, D. J., Fonzo, G. A., Keller, C. J., Zhang, Y. 2025: 119483

  Abstract

  Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes remains underexplored.Participants with treatment resistant depression from the TDBRAIN dataset underwent either high frequency rTMS (10 Hz) at the left dorsolateral prefrontal cortex (DLPFC) (Protocol 1, n = 44) or low frequency rTMS (1 Hz) at the right DLPFC (Protocol 2, n = 73). Pre-treatment electroencephalograms (EEG) was collected, and changes in Beck Depression Inventory were measured post-treatment. EEG oscillations were decomposed into multiband intrinsic mode functions (IMF) and integrated under a latent space predictive modeling framework to identify signatures for predicting treatment outcomes.Multiband signatures significantly predicted rTMS outcomes (Protocol 1: r = 0.40, p < 0.01; Protocol 2: r = 0.26, p < 0.05). Key spatial patterns linked to treatment outcomes were identified, revealing three main oscillations: IMF-Alpha, IMF-Beta, and the residual signal. In Protocol 1, critical regions included the left frontal and parietal regions for IMF-Alpha, left frontal-central and right parietal regions for IMF-Beta, and bi-hemispheric central and left parietal-occipital regions for residual signals. In Protocol 2, critical regions involved the left frontal and parietal regions for IMF-Alpha, left frontal-central region IMF-Beta, and right frontal, left frontal-central, midline central, and left parietal-occipital regions for residual signals. These oscillatory features also showed correlations with specific personality measures, suggesting their potential clinical relevance.Our findings demonstrate the promise of machine learning-driven multiband EEG signatures for personalized MDD treatment prediction, offering a translatable pathway for improved patient outcomes.

  View details for DOI 10.1016/j.jad.2025.119483

  View details for PubMedID 40441660

• Early prediction of dementia using fMRI data with a graph convolutional network approach. Journal of neural engineering Han, S., Sun, Z., Zhao, K., Duan, F., Caiafa, C. F., Zhang, Y., Solé-Casals, J. 2024; 21 (1)

  Abstract

  Objective. Alzheimer's disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs).Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI.Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification.Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at:https://github.com/Shuning-Han/FC-based-GCN.

  View details for DOI 10.1088/1741-2552/ad1e22

  View details for PubMedID 38215493

• A generalizable functional connectivity signature characterizes brain dysfunction and links to rTMS treatment response in cocaine use disorder. medRxiv : the preprint server for health sciences Zhao, K., Fonzo, G. A., Xie, H., Oathes, D. J., Keller, C. J., Carlisle, N., Etkin, A., Garza-Villarreal, E. A., Zhang, Y. 2023

  Abstract

  Cocaine use disorder (CUD) is a prevalent substance abuse disorder, and repetitive transcranial magnetic stimulation (rTMS) has shown potential in reducing cocaine cravings. However, a robust and replicable biomarker for CUD phenotyping is lacking, and the association between CUD brain phenotypes and treatment response remains unclear. Our study successfully established a cross-validated functional connectivity signature for accurate CUD phenotyping, using resting-state functional magnetic resonance imaging from a discovery cohort, and demonstrated its generalizability in an independent replication cohort. We identified phenotyping FCs involving increased connectivity between the visual network and dorsal attention network, and between the frontoparietal control network and ventral attention network, as well as decreased connectivity between the default mode network and limbic network in CUD patients compared to healthy controls. These abnormal connections correlated significantly with other drug use history and cognitive dysfunctions, e.g., non-planning impulsivity. We further confirmed the prognostic potential of the identified discriminative FCs for rTMS treatment response in CUD patients and found that the treatment-predictive FCs mainly involved the frontoparietal control and default mode networks. Our findings provide new insights into the neurobiological mechanisms of CUD and the association between CUD phenotypes and rTMS treatment response, offering promising targets for future therapeutic development.

  View details for DOI 10.1101/2023.04.21.23288948

  View details for PubMedID 37162878

  View details for PubMedCentralID PMC10168499

• A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. NeuroImage Zhao, K., Duka, B., Xie, H., Oathes, D. J., Calhoun, V., Zhang, Y. 2022; 246: 118774

  Abstract

  The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

  View details for DOI 10.1016/j.neuroimage.2021.118774

  View details for PubMedID 34861391

  View details for PubMedCentralID PMC10569447

• Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy FRONTIERS IN CARDIOVASCULAR MEDICINE Sharma, U. C., Zhao, K., Mentkowski, K., Sonkawade, S. D., Karthikeyan, B., Lang, J. K., Ying, L. 2021; 8: 726943

  Abstract

  Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.

  View details for DOI 10.3389/fcvm.2021.726943

  View details for Web of Science ID 000699802200001

  View details for PubMedID 34589528

  View details for PubMedCentralID PMC8473636