NAL News:

(2023) We are excited to share that our lab has published a pivotal paper in JAMA Network Open (IF~14) on the structural brain changes in type 1 diabetes (T1D). Our study, part of the extensive Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) cohort, has revealed insightful connections between T1D and indicators of brain aging.

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(2023) Congrats to Anoop for his talk during the MLCN 2023 workshop at MICCAI in Vancouver. Anoop described the DeepAIM network to detect White Matter Hyperintensities in postmortem MRI scans.

 

(2023) A sincere congratulations to Mariam Mojtabai for being awarded the prestigious STAND 32 fellowship, to support her thesis project. This award underscores her dedication, talent, and groundbreaking work in postmortem MRI the whole team has been driving forward. We are all exceptionally proud!

 

(2023) Praise is for Di Wang, who secured the travel award for the TACCSTER 2023 conference. This award is a recognition of the powerful potential of his research and promises to be a great breakthrough in explainable AI research.

 

(2023) Congrats to Nicolas for his paper being published in Medical Image Analysis (IF=10.9), in which we introduce novel approaches for fMRI data harmonization.

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(2023) Congratulations to Di Wang for presenting at the TACCSTER 2023 conference in Austin.

 

(2023) A huge round of applause for Tanweer Rashid and Di Wang who received the Best Poster Awards at the CHARGE meeting. Their outstanding presentations highlighted not just our lab's research but also their skill in elucidating complex ideas.

 

(2023) Our faculty member Nicolas Honnorat has received the Owens award to use fMRI networks to understand the spread of AD pathologies in the brain This is a significant award, which was very competitive with only  ~10% success chance.

Congrats Nicolas, very well-deserved, keep up the great work.

 

(2023) Well done, Anoop, Di, Tanweer, and Nicolas for giving talks during Neuroepiomics 2023 ging, without the early manifestations typically associated with Alzheimer's disease (AD). This research utilized advanced MRI techniques and machine learning to predict brain age and quantify AD-like atrophy, providing valuable findings that deepen our understanding of T1D's impact on brain health.

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(2023) Dr. Habes has been featured in an interview with WGEM, showcasing our latest research breakthroughs. This notable recognition highlights our lab's dedication to pioneering advancements in our field

https://www.wgem.com/2023/11/01/ai-locates-brain-lesions-seconds/

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(2023) Congrats Karl for his paper being published in the Journal of Alzheimer’s Disease (JAD)

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(2023) We are proud to announce that our lab's Principal Investigator Dr. Habes was recently interviewed by Texas Public Radio, highlighting our team's groundbreaking research and achievements. This recognition underscores our commitment to advancing scientific knowledge and innovation

https://www.tpr.org/podcast/petrie-dish/2023-10-28/science-medicine-using-ai-for-brain-health-diagnoses

 

(2023) Our Principal Investigator was recently spotlighted in an interview with SA Express News, discussing our lab's innovative research and contributions to the field. This acknowledgment serves as a testament to our team's hard work and dedication

https://www.expressnews.com/business/article/artificial-intelligence-increasing-role-san-18274516.php

 

(2023) Dr. Habes was recently featured in an engaging interview with our school's media office, highlighting the impactful research and developments emerging from our team. This recognition reflects our lab's commitment to scientific excellence and innovation.

https://news.uthscsa.edu/ut-health-san-antonio-develops-tool-that-counts-brain-lesions-in-seconds/

 

(2023) Congrats, Sokratis et al. for accepting our paper “Assessment of risks and significance of enlarged perivascular spaces by whole-brain investigation: The Multi-Ethnic Study of Atherosclerosis” in Jama Network Open (IF~14). This is an excellent, high-impact paper in which Sokratis et al. show the clinical relevance of enlarged perivascular spaces, often underappreciated lesions. It also demonstrates the great utility of our deep learning tools in multi-site settings.

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(2023) Our paper in the MESA cohort “Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA” was published in the Journal of Alzheimer’s Disease: the journal of Alzheimer’s Association. In this paper we leveraged our deep learning networks to detect cerebral microbleeds in >1000 MR scans.

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(2023) Congrats, Tanweer et al. for accepting our paper entitled “Intensive vs. Standard Blood Pressure Control and Regional Changes in Cerebral Small Vessel Disease Biomarkers” in Jama Network Open (IF~14). This is a landmark and high-impact paper in which Tanweer et al. show the improvement in small vessel disease in hypertension patients who receive intensive hypertension treatment.

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(2022) Di Wang has successfully passed his PhD candidacy exam.

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(2022) Our paper with Christine Dintica on Long-term depressive symptoms and midlife brain age is now online.

 

(2022) Our lab was funded by NIH with R01 grant to derive brain aging indices in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

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(2022) Our paper with Jon Toledo introduced the SPARE-TAU index as stronger predictor of progression in cognitively normal individuals compared to MRI and CSF.

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(2022) We had a wonderful workshop “Machine learning in neuroimaging” at the MICCAI 2022, with great plenary talk fromThomas Yeo.

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(2022) Congrats for Christine Dintica for our paper published in Alzheimer’s and dementia, in which we show that Elevated blood pressure is associated with advanced brain aging in mid-life.

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(2022) We had a highly interactive and populated symposium during the OHBM2022 in Glasgow: Charting brain variability in health and disease using normative models.

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(2022) Mohamad Habes was selected by NIH to lead the RECOVER PACS adult brain MRI reading center together with Ilya Nasrallah of Penn Radiology and Mark Goldberg of UTHSA, across US sites to understand COVID19 impact on the brain. 

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(2022) Hangfan Liu presented his framework Collaborative Clustering Based on Adaptive Laplace Modeling for Neuroimaging Data Analysis during the 2022 IEEE International Symposium on Circuits and Systems (ISCAS).

 

(2022) Our paper compared heterogeneity in AD representation on MRI and Tau PET with advanced data-driven AI methods, is now online http://shorturl.at/gqyR7. We found typical and atypical AD patterns with different risk. Amazing team work with Jon Toledo and Hangfan Liu.

 

(2022) Elyas Fadee and Tanweer Rashid, talked about our lab research within the South Texas ADRC on postmortem MRI and small vessel disease, during the ASNR22. 

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(2022) Hangfan’s new method called “ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data” has been accepted for publication in IEEE Transactions on Emerging Topics in Computational Intelligence, congrats Hangfan for this landmark paper.

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(2021) Hangfan Liu has presented his work “Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds” in MICCAI 2021 in Strasbourg, France, congrats Hangfan for the great work.

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(2021) We had an excellent workshop “Machine learning in neuroimaging” at the MICCAI 2021, with great plenary talk from Paul Thomoson.

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(2021) Christina Dintica, PhD, a postdoctoral scholar in the UCSF Department of Psychiatry and member of the Kristine Yaffe Lab, has been awarded an Alzheimer's Association Research Fellowship! Under the mentorship of Dr. Yaffe and UT Health San Antonio's Dr. Mohamad Habes.

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(2021) Jon Toledo and Tanweer Rashied presented the SPARE-tau work at the AAIC2021.

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(2021) Our AI-dementia project, funded by the SAMF was featured in the UTHSA news.

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(2021) Tanweer’s paper DeepMIR detecting cerebral microbleeds and iron deposits using MRI, advanced quantitative methods and Deep Learning has been accepted in Scientific Reports https://rdcu.be/co79B. Congrats Tanweer Rashid for the excellent work.

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(2021) We have received the San Antonio Medical Foundation (SAMF) grant in collaboration with UTSA (Dhireesha Kudithipudi) and the VA (Adetoun Musa) to apply artificial intelligence to MRI and EEG data and subtype dementia patterns.

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(2020) We published our work "The Brain Chart of Aging MachineLearning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans" in Alzheimer’s and dementia, the journal of Alzheimer’s Association.

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(2020) We presented our workshop “Machine learning in neuroimaging”  at the MICCAI 2020.

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(2020) Our paper comparing multi atlas based segmentation (MUSE) to FreeSurfer is online in Neuroimage.

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(2020) We had a great featured research session with people excited about AI and its emerging role in uncovering dementia at the virtual AAIC2020.

 

(2020) Tanweer Rashid presented his work on deep learning for cerebral microbleeds and iron deposits detection in MRI.

 

(2020) Our paper Biological Psychiatry was featured in the UTHSA news.

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(2020) Hangfan Liu presented his work on detecting microbleeds in MRI scans.

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(2020) Our paper is now online in Biological Psychiatry ” Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods”, summarizing precision medicine approaches to subtyping Alzheimer's disease with advanced machine learning methods, applied on neuroimaging and other phenotyping data, for future care!

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