Current
a. Repetitive Head Impact
Participants exposed to repeated head impacts (RHI) such as those involved in American football, professional boxing, etc. are at a higher risk of developing a brain condition called chronic traumatic encephalopathy (CTE) that leads to dementia. However, currently, the only definitive way of diagnosing CTE is through neuropathological testing. Our lab has focused its efforts on utilizing the combination of multimodal MRI and machine-learning techniques to identify participants at-risk for developing CTE. We use the clinical presentation and a battery of neuropsychological testing to evaluate whether the participant presents symptoms of cognitive decline when compared to a participant of the same sex, education, age, and other demographical factors. We developed our technique with a cohort of active professional fighters and found that seven MRI-derived features predicted fighters having clinical cognitive impairment with ~75% accuracy, both at baseline and follow-up using only the weights identified at baseline. We are currently in the process of increasing our predictive ability by adding other MRI measures such as resting-state fMRI connectivity changes and blood changes using deep-learning methodologies, as these changes have independently predicted cognitive decline due to RHI.
b. Parkinson’s disease with cognitive impairment
Approximately 50-80% of Parkinson’s disease (PD) patients develop PD-dementia (PDD) within twelve years of diagnosis. However, no reliable method yet exists to predict PDD. Identifying pathophysiology-based biomarkers that could identify PD patients at high risk for PDD reliably is critical for better understanding the pathophysiological processes underlining PDD. We have invested our efforts in approaching the identification of predictive biomarkers of PDD through multimodal imaging utilizing both MRI-derived measures (diffusion-weighted MRI (dMRI), resting-state functional MRI (rsfMRI), and T1-MRI measures) and non-imaging measures such as demographics (Eg: age, sex), clinical (Eg: disease duration and severity), genetics (Eg: LRRK2), and CSF-measures (Eg: Total Tau, β-Amyloid, α-synuclein) since each of these measures have shown to independently predict PDD. Our current analyses show that advanced dMRI measures may be more predictive of mild cognitive impairment (MCI) in PD than the conventional single tensor dMRI measures. Our initial results have also shown that dopamine affects the functional connectivity in PD-MCI differently than PD with normal cognition. A deeper understanding of these effects along with developing novel tools for analyzing rsfMRI and developing a deep-learning model by fusing these modalities to identify reproducible biomarkers of PDD are currently underway.
c. Parkinson’s disease with freezing of gait
Freezing-of-gait (FOG) is defined as a brief, episodic absence or marked reduction of the forward progression of feet despite the intention to walk. PD-FOG is considered one of the most debilitating motor symptoms affecting almost 50% of patients with PD and is one of the most common causes of falls and subsequent morbidity and mortality. Several competing hypotheses have been proposed to understand PD-FOG. However, these models are incomplete and only partially explain the phenotype of PD-FOG. In general, FOG has been attributed to overloading across neural networks in an attempt to compensate for reduced motor function which may lead to the inability to “set-shift” among the different neural networks. We utilized rsfMRI data and graph theoretical approaches and found that the PD-FOG patients showed a noticeable reorganization of hub regions. However, we could not prove or disprove any of the commonly accepted hypotheses for PD-FOG. We are currently developing novel computational models using deep-learning approaches to analyze the MRI data for PD-FOG to identify predictive biomarkers of PD-FOG.
d. Reproducibility of diffusion MRI across scanners and protocols
dMRI is currently the only tool that can help visualize the white matter tracts and quantify the changes therein with pathology. However, dMRI can be acquired using a different set of protocols (with various spatial and angular resolutions) on various scanners (Siemens v Philips) with different directional encoding (64 directions v 32 directions) either with a single-shell or a multishell acquisition. These variabilities in acquiring the data can induce unintended artifacts and can make the results obtained from dMRI non-reproducible. We are focusing our efforts to harmonize the data across scanners and protocols using deep-learning techniques and will make our algorithm available for public scrutiny, and invite collaborations with the community to achieve this goal.
