# A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation
## 3 Results
### Cluster Analysis
statistical analyses showed that the age, distribution of sex and education level were not significantly different between the two clusters of PD patients (\(p>0.05\)). The ANCOVA, with age, sex and education level in covariates, showed that the total MMSE score, TO, OS, AC, Rec, W, CE and D were significantly different between the two clusters (\(p<0.001\)). The number of clusters found on PSP patients' data was 6 and the Silhouette index was low (0.387) when the preference value was equal to the median of the similarity matrix. In particular, we found 2 bigger clusters with 15 and 11 PSP participants, 2 clusters were comprised of 8 PSP patients, 1 cluster was comprised of 5 PSP patients and 1 cluster comprised of 1 PSP patient. We found an improvement of the Silhouette index (0.677) and a total of 2 clusters when the preference value was equal to the minimum of the similarity matrix. The exemplars' demographic values and eleven MMSE subscale scores (as a vector: [TO, OS, Reg, AC, Rec, N, SR, P, W, CE, D]) were:  
* Cluster #1 PSP: male, age 57, education 17, MMSE subscales = [5, 5, 3, 2, 1, 2, 1, 3, 0, 1, 0];
* Cluster #2 PSP: male, age 78, education 13, MMSE subscales = [5, 5, 3, 5, 2, 2, 1, 3, 0, 1, 0];  
The first cluster was comprised of 26 PSP patients (12 females) with a mean age of 70.3 \(\pm\) 8.17, a mean education level of 6.88 \(\pm\) 4.71 and a total MMSE mean score of 17.4 \(\pm\) 4.31 (TO 3.19 \(\pm\) 1.52, OS 3.27 \(\pm\) 1.28, Reg 2.77 \(\pm\) 0.65, AC 0.96 \(\pm\) 0.77, Rec 1.23 \(\pm\) 0.95, N 1.88 \(\pm\) 0.43, SR 0.73 \(\pm\) 0.45, P 2.69 \(\pm\) 0.62, W 0.19 \(\pm\) 0.40, CE 0.46 \(\pm\) 0.51, D 0.15 \(\pm\) 0.37). The second cluster was comprised of 22 PSP patients (10 females) with a mean age of \(70.0\pm 8.69\), a mean education level of 7.95 \(\pm\) 5.47 and a total MMSE mean score of 24.9 \(\pm\) 2.88 (TO 4.64 \(\pm\) 0.59, OS 4.45 \(\pm\) 0.74, Reg 2.95 \(\pm\) 0.21, AC 3.77 \(\pm\) 1.34, Rec 1.86 \(\pm\) 0.71, N 2 \(\pm\) 0, SR 0.91 \(\pm\) 0.29, P 2.86 \(\pm\) 0.47, W 0.41 \(\pm\) 0.50, CE 0.59 \(\pm\) 0.50, D 0.36 \(\pm\) 0.49). See Figure 4 for the radar plots of the mean values of MMSE subscales of each of the two clusters of PSP patients found. The statistical analyses revealed that the age, distribution of sex and education level were not significantly different between the two clusters of PSP patients (\(p>0.05\)). The ANCOVA, with age, sex and education level in covariates, showed that the total MMSE score, TO, OS, AC and Rec were significantly different between the two clusters (\(p<0.001\)). Figure 3: Radar plots of the mean of the eleven MMSE subscales per cluster of Parkinson’s disease (PD) patients. In gray and dotted lines the maximum value that each MMSE subscale could reach: Max(TO) = 5, Max(COS) = 5, Max(Reg) = 3, Max(AC) = 5, Max(Rec) = 3, Max(N) = 2, Max(SR) = 1, Max(P) = 3, Max(W) = 1, Max(CE) = 1, Max(D) = 1. Abbreviations: TO = temporal orientation; OS = orientation in space, Reg = registration of three words; AC = attention and calculation; Rec = recall of three words; N = object naming; SR = sentence repetition; P = praxis; W = writing a sentence; CE = reading a sentence and close your eyes; D = copy a drawing. Lastly, we applied our algorithm on the whole cohort of CTRL, PD and PSP patients for investigating the inter-diagnostic cognitive profiles. We found a huge number of clusters (16) and a poor Silhouette index (0.237) when the preference value was equal to the median of the similarity matrix. The Silhouette index was better (0.601), and we found 4 clusters when the preference value was the minimum of the similarity matrix.