Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

Background: Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored.

New Method: In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework.

Result: The difference between PD and HC mainly exists at frequency band 0.04-0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale.

Comparison With Existing Methods: Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients.

Conclusion: SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered.