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NAL News:

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


(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.

(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.

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

(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.

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

(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. 

(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 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. 

(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.

(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.

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

(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.

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

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

(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 Congrats Tanweer Rashid for the excellent work.

(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.

(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.

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

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

(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.

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

(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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