Objective:

Myotonic dystrophies (DM) are neuromuscular conditions that cause widespread effects throughout the body. New therapeutics are being developed that attack the RNA mechanism underlying DM. Here, we address the urgent need of identifying DM neuroimaging biomarkers that can be used in trials. 

Background:

DTI shows decreased white matter connectivity in DM patients and MRI reveals decreased parietal, frontal, and temporal resting state metabolism. Additionally, DTI studies of DM have shown that the white matter abnormalities are diffuse as opposed to multi-focal.

Design/Methods:

Extracting important features from neuroimaging data is challenging due to the high feature dimension and low sample-size problem. This work utilizes a sparse matrix decomposition method and a manifold regularization approach to identify the most discriminative features and improves spatial and temporal resolutions of neuroimaging data. To this end, neuroimaging data of 46 DM1, 17 DM2 patients and 92 healthy controls were analyzed using a ten-fold-cross-validation technique. In addition to neuroimaging data, additional clinical assessments including Quebec Muscular Impairment Rating Scale and Quebec Daytime Sleepiness Scale were calculated for DM patients. Wavelet approximation and detailed coefficients were calculated to consider both long term and short-term fluctuations caused by the disease process.

Results:

Normalized sub-band energy calculated based on wavelet coefficients can characterize DM1 and DM2. Patients with DM1 have widespread white matter volume reductions that are correlated with distal to proximal progression of the muscular involvement in DM1. A significant difference can be seen between DM1 and healthy controls in terms of executive function, motor speed, processing speed, and memory.

Conclusions:

The proposed method can potentially be used to develop and validate key predictors of DM type, severity, and progression of DM central nervous system abnormalities using brain morphometry. Identified features can also potentially provide multidisciplinary biomarkers that help unravel our understanding of fatigue, sleepiness, and attention.