Image Segmentation, Deep Learning Architectures & Predictive Modeling to Advance Neuroscience

Albert Montillo - Deep Learning for Precision Health Lab

There are three project options in the Montillo Lab:

1) Segmentation of deep brain structures impacted by neurodegenerative disorders; Project Mentors: Son Nguyen & Alex Treacher

The putative initial site impacted in multiple neurodegenerative diseases, including movement disorders such as Parkinson’s disease and atypical parkinsonian disorders, are deep seated structures in the mid brain. These structures include the substantia nigra pars compacta and pars reticulata, red nucleus, globus pallidus, and putamen among others. 3D MRI can reveal these structures however there are no publically available pipelines to automatically label these structures on individual patient scans. This presents a significant obstacle to quantifying their degradation in disease with measures from neurite density, neuronal demylination, iron concentration and thereby forming a biomarker of disease progression. In this project we will use 3D MRI from the NIH/NINDS PDBP dataset, manually labeled with the support of collaborating neuroradiologists at UTSW, to develop a fully automated neuroanatomical structure labeling 3D deep learning algorithm. Quantitative segmentation results will be computed through standardized metrics against neuroradiologist, and externally validated a second dataset from UTSW AIRC. Student will work closely with PI, PhD student and neuroradiologst and first author publication is anticipated within 6-8mo.

For more information about this project email Son Nguyen or Alex Treacher.

2) Novel deep learning architectures for decoding 4D brain activation; Project Mentors: Son Nguyen & Kevin Nguyen

Patterns of brain activity can be measured non-invasively in 4D using fMRI, EEG and MEG among other modalities. In mental disorders such as schizophrenia, bipolar disorder and schizoaffective disorder, this data is being used to identify biomarkers capable of objectively informing the diagnoses of these conditions.  The standard approach to forming a predictive model entails preprocessing of the brain activity data which entails the selection of many image processing parameters such as the choice of atlas to divide the brain into a predetermined set of regions, smoothing kernels to smooth regional signals. The extracted inter-regional brain connectivity is then provided as input to a machine learning algorithm which is trained to decode the connectivity into a diagnosis. While deep learning models could be utilized here, they have also shown ability to directly learn a hierarchy of preprocessing kernels that directly act on the raw data which are optimal for a given classification task and can outperform such hand crafted features. Accordingly, the goal of this project is to develop novel deep learning architectures that directly operate on the raw brain activity data. This will entail layers to integrate temporal information and layers that integrate this information spatially in a multiresolution manner. The extensive, multisite BSNIP 1 dataset will be used to develop the models and predict the mental disorder diagnosed by board-certified psychiatrists. The model will be externally validated upon the multi-site BSNIP 2 database, and through leave-one-site out cross validation. Student will work closely with PI, Postdoc and psychiatrists from UTSW and collaborating sites. First author publication is anticipated within 6-8mo.

For more information about this project email Son Nguyen or Kevin Nguyen.

3) Interpretability of tensor decomposition methods of brain connectivity measures predictive of treatment response in depression; Project Mentors: Kevin Nguyen & Cooper Mellema

In major depression disorder, treatments include antidepressants and psychotherapy. There are >20 antidepressants to choose from and selecting which is best for each patient currently entails a trial-and-error process, leaving 40% of patients without an effective treatment for a year or more. it is widely believed that patterns of brain activity in pre-treatment EEG and MRI contain information that may be used to predict the treatment response profile of individual patients. However, these brain activity patterns are extremely high dimensional (e.g. 120K voxels by 180 time frames), while the number of subjects in a given study is typically on the order of 300. Therefore the development of dimensionality reduction methods are essential to fully exploit this information. Tensor decomposition methods are an attractive approach and include methods such as probabilistic PCA, probabilistic CCA, Non-negative matrix factorization. Which yield the optimal predictive power whilst also yielding interpretable factorization is yet to be discovered. Accordingly, this project will compare and optimize the methods to predict the recovery slopes in the largest randomized placebo controlled clinical trial of antidepressants, EMBARC from UTSW. Results will be externally validated on UTSW’s D2K dataset. Student will work closely with PI, MD/PhD students and psychiatrists from UTSW and collaborating sites. First author publication is anticipated within 6-8mo.

For more information about this project email Kevin Nguyen or Cooper Mellema.