Josiah Brown Poster Abstract


Lauren E. Uhr
Nader Pouratian, MD, PhD
Evangelia Tsolaki, MSc, PhD
Diffusion Tensor Imaging Correlates of Parkinson’s Disease and Depression

Purpose: Mood disorders, such as depression, are associated with Parkinson’s disease although the pathophysiology is unknown. Abnormalities of the subcallosal cingulate (SCC) region are well documented in depression and are shown to relate with antidepressant response [1,2]. The goal of this study is to utilize SCC structural connectivity to better understand the neural substrates of depression in Parkinson’s disease.

Methods: MR diffusion data from forty Parkinson’s disease patients who received deep brain stimulation were evaluated in this study. FSL probabilistic tractography was performed to delineate structural connectivity of the anatomical SCC area across the whole brain and with subject-specific target regions of interest (ROIs) including the bilateral medial prefrontal cortex (mPFC), ipsilateral ventral striatum (Vst), and ipsilateral dorsal anterior cingulate cortex (ACC). The probability of SCC connectivity to each target ROI was investigated in relation to Beck Depression Inventory-II (BDI-II) mood scores and Unified Parkinson’s Disease Rating Scale (UPDRS) Part III motor function using Pearson and Spearman correlation tests.

Results: The probability of SCC ipsilateral connectivity to the left Vst (M= 0.22, STD= 0.13) was found to be negatively correlated (p=0.012) with the UPDRS motor scores (M= 35.50, STD= 17.02), while a positive trend (p=0.10) was observed with BDI-II mood scores (M= 10.70, STD= 7.49).

Conclusion: These findings suggest that increased probability of connectivity between the SCC and Vst in the left hemisphere is associated with increased depression severity and better motor function. These results support the role of Vst in the reward processing pathway, a function known to be aberrant in both depression and Parkinson’s disease.


  1. Drevets WC, Savitz J, Trimble M.  The subgenual anterior cingulate cortex in mood disorders.  CNS Spectr. 2008;13(8):663-681.
  2. Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C et al. The prediction of individual response to electroconvulsive therapy by structural MRI. JAMA Psychiatry 2016; 73(6):557-564.