U0001F4D8 Publications

Here are some key projects and publications I’ve worked on, with brief explanations and links to the full articles.

Thesis

- PhD thesis: MRI-Based Texture Analysis for Characterizing Cerebral Degeneration Progression in Amyotrophic Lateral Sclerosis

University of Alberta, 2024

Summary: Amyotrophic lateral sclerosis (ALS) is a highly heterogeneous, progressive neurodegenerative disease with a median survival of three years from the onset of symptoms. As a multi-system disease, ALS is not limited to the motor system and includes degeneration of other cerebral regions, including frontotemporal areas. The complex heterogeneity of the disease is linked to a multitude of contributing factors, such as polygenic profiles, the site of initial symptoms, the rates of disease progression, and the progressive spread of pathological processes. To achieve a deeper understanding of the disease and take a step forward toward personalized medicine, magnetic resonance imaging (MRI) provides the potential to capture the cerebral neurodegenerative process, in vivo.

The overall objective of this thesis is to leverage MRI-based texture analysis, an image processing technique that quantifies intuitive features of an image, to delineate the progression of cerebral degeneration in ALS. Through a series of aims, the thesis seeks to utilize texture analysis on structural MR images to assess changes in gray and white matter structures based on a model of disease progression, detect longitudinal cerebral changes and evaluate the clinical classification of patients, analyze degeneration within specific neural tracts, and classify ALS patients based on cerebral degeneration patterns.

Journal Articles

1. Data-driven disease subgrouping in ALS: a multicenter cerebral functional connectivity study

Journal of Neurology, 2026 · Co-Author

Summary: This multicenter study used functional connectivity data from the Canadian ALS Neuroimaging Consortium (CALSNIC) to identify distinct ALS patient subgroups based on cerebral functional connectivity patterns. The work advances our understanding of disease heterogeneity and supports precision medicine approaches in ALS.

2. An individual-level MRI approach reveals corticospinal tract degeneration and its heterogeneity in ALS

Annals of Neurology, 2025 · Co-Author · (submitted)

Summary: Using an individual-level analysis of MRI data, this study reveals heterogeneity in corticospinal tract degeneration across ALS patients — an important step toward personalized imaging-based stratification in clinical trials. The approach moves beyond group-level statistics to capture patient-specific patterns of neurodegeneration.

3. Progressive and short-interval changes observed in the corticospinal tract and corpus callosum of ALS patients: A texture analysis study

American Journal of Neuroradiology, 2025 · First Author

Summary:

This study demonstrates that texture analysis of routine MRI could serve as a practical, accessible biomarker for detecting and monitoring ALS progression over short clinical intervals in multicenter settings.

4. T1-weighted MRI texture analysis in amyotrophic lateral sclerosis patients stratified by the D50 progression model

Brain Communications, 2024 · First Author

Summary: This study explores the potential of texture analysis on T1-weighted MRI images to reveal unique insights into the diverse progression patterns of ALS. We pushed the limits of texture analysis on 1.5 Tesla T1-weighted scans, stratifying patients by disease severity and stage using the D50 disease progression model. This model, which captures the sigmoidal trajectory of functional decline, allowed us to differentiate ALS progression with greater precision. Our findings indicate that texture analysis could be a promising biomarker for staging ALS.

5. Mismatch between clinically defined classification of ALS stage and the burden of cerebral pathology

Journal of Neurology, 2024 · First Author

Summary: In our recent article, my colleagues and I embarked on a detailed investigation of the clinical stages of amyotrophic lateral sclerosis (ALS) and the extent of cerebral pathology detected by texture analysis of T1-weighted brain images. The study demonstrated that the traditional classification of ALS patients into early and advanced stages does not accurately reflect the level of neurodegeneration, and that imaging should be incorporated into disease stratification in clinical trials.

6. Accurate personalized survival prediction for amyotrophic lateral sclerosis patients

Scientific Reports, 2023 · Co-Author

Summary: This article introduces a machine-learned tool designed to accurately predict the survival time of ALS patients, a critical factor in strategizing future treatments and care. The tool uniquely integrates clinical features with cortical thickness data derived from brain MR images, offering a novel method to estimate the time until a composite respiratory failure event in ALS patients. The article presents the prediction as individual survival distributions (ISDs), which provide a probabilistic view of survival at various future time points for each patient.

7. Effects of MRI scanner manufacturers in classification tasks with deep learning models

Scientific Reports, 2023 · Co-Author

Summary: This study explores the challenges of domain shift in deep learning applications for medical imaging, specifically focusing on multi-center MRI data from different scanner manufacturers (GE, Philips, and Siemens). Please check it out for further details.

Conference Abstracts

Podium Presentations

Poster Presentations