Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI --
  Integrating Grey and White Matter Information
Tanmayee Samantaray Jitender Saini Pramod Kumar Pal Bithiah Grace Jaganathan Vijaya V Saradhi Gupta CN
Thresholding of networks has long posed a challenge in brain connectivity
analysis. Weighted networks are typically binarized using threshold measures to
facilitate network analysis. Previous studies on MRI-based brain networks have
predominantly utilized density or sparsity-based thresholding techniques,
optimized within specific ranges derived from network metrics such as path
length, clustering coefficient, and small-world index. Thus, determination of a
single threshold value for facilitating comparative analysis of networks
remains elusive. To address this, our study introduces Mutual K-Nearest
Neighbor (MKNN)-based thresholding for brain network analysis. Here, nearest
neighbor selection is based on the highest correlation between features of
brain regions. Construction of brain networks was accomplished by computing
Pearson correlations between grey matter volume and white matter volume for
each pair of brain regions. Structural MRI data from 180 Parkinsons patients
and 70 controls from the NIMHANS, India were analyzed. Subtypes within
Parkinsons disease were identified based on grey and white matter volume
atrophy using source-based morphometric decomposition. The loading coefficients
were correlated with clinical features to discern clinical relationship with
the deciphered subtypes. Our data-mining approach revealed: Subtype A (N = 51,
intermediate type), Subtype B (N = 57, mild-severe type with mild motor
symptoms), and Subtype AB (N = 36, most-severe type with predominance in motor
impairment). Subtype-specific weighted matrices were binarized using MKNN-based
thresholding for brain network analysis. Permutation tests on network metrics
of resulting bipartite graphs demonstrated significant group differences in
betweenness centrality and participation coefficient. The identified hubs were
specific to each subtype, with some hubs conserved across different subtypes.