# A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation
## 4 Discussion  
In the present study, we proposed a novel method for discovering the natural structure of cognitive impairment in Parkinsonisms patients according to the eleven MMSE subscale scores. Our application of the affinity propagation clustering algorithm on the regression residuals revealed the presence of four clusters, where two clusters (#1 and #2) showed almost intact cognitive performance, one cluster (#4) had a moderate cognitive impairment, and the last cluster (#3) had a more extensive cognitive deficit. An important finding of our work was the existence of variable levels of cognitive impairment in PD and PSP patients, showing an intra- and inter-diagnostic heterogeneity of their cognitive profiles, never investigated accurately before now with an advanced unsupervised learning technique. Figure 6: Radar plots of the mean of the eleven MMSE subscales per cluster. In gray and dotted lines the maximum value that each MMSE subscale could reach: Max(TO) = 5, Max(OS) = 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. Clustering was widely applied in the neuropsychology realm [10] and a variety of algorithms was used [8; 9] for exploring for example, the cognitive dysfunction in Bipolar and Major Depression [11; 13] or affective and psychotic disorder [12; 14]. Interestingly, the findings of several works [11; 12; 13; 14; 15] showed distinct neurocognitive clusters independent from the diagnosis, as we found in our cohort of PD and PSP patients. Similarly to our results, [PERSON] et al. [11] and [PERSON] et al. [14] found one neuropsychological intact subgroup (Cluster #2 in our case), one to three \"intermediate\" clusters (Clusters #1 and #4 in our case) and one cluster with a significant cognitive deficit (Cluster #3 of the present study), by applying both the [PERSON]'s linkage with squared Euclidean distance. Cluster analysis with the [PERSON]'s method was also used by [PERSON] et al. [16], which found heterogeneous patterns in the neurocognitive profile of early-onset Alzheimer's patients, as indeed we found in our cohort of Parkinsonians patients. Although a plethora of works [18; 19; 20; 21; 22; 23; 24] investigated the heterogeneity among PD patients studying both motor and non-motor symptoms with clustering approaches, no one before us, explored the MMSE subscale scores, and moreover, no previous work studied the cognitive patterns of PSP patients with an unsupervised learning methodology. Indeed, a very recent work by our research group [36] already highlighted the importance of the machine learning for the evaluation of the neuropsychological and cognitive profile of PD and PSP subtypes, which could present overlapping cognitive deficits. It is worth of noting that, as far as we know, only one previous work applied clustering on the MMSE subscales [15]. In particular, [PERSON] et al. [15] used a two-step cluster analysis, comprised of a first phase consisting in pre-clustering and a second phase of a modified hierarchical agglomerative clustering [15] on the Korean version of the MMSE. The findings of [PERSON] et al. [15] showed that the cluster analysis of the MMSE could be considered a reliable screening tool for the cognitive impairment of the neurodegenerative diseases, especially when complex neuropsychological batteries are not feasible. Differently from [PERSON] et al. [15], we did not include in the cluster analysis the age and education levels, but we treated age, gender and education levels as confounding variables, for taking into account only the influence of the diagnosis on the cognitive profile of the participants.