Exploring the Phenomenology of Suicide

Phenomenology studies conscious experience as experienced from the subjective or first‐person point of view. This paper was developed with the aim of shedding light on the phenomenology of suicide; that is, to focus on suicide as a phenomenon affecting a unique individual with unique motives for the suicidal act. To explore this topic, the author looks back at the past centuries to understand why suicide was thought to be confined to psychiatric illness and to document the bias in studies supporting this notion. One major step forward in the conceptualization of suicide as a psychological disorder was provided by Edwin Shneidman, who focused on the pain of negative emotions. Such a radical approach is laudable in an era where diagnostic criterria and the need to cure are more important than understanding what is not working at the emotional level.     

Neuropsychological assessment could distinguish among different clinical phenotypes of progressive supranuclear palsy: A Machine Learning approach

Progressive supranuclear palsy (PSP) is a rare, rapidly progressive neurodegenerative disease. Richardson’s syndrome (PSP-RS) and predominant parkinsonism (PSP-P) are characterized by wide range of cognitive and behavioural disturbances, but these variants show similar cognitive pattern of alterations, leading difficult differential diagnosis. For this reason, we explored with an Artificial Intelligence approach, whether cognitive impairment could differentiate the phenotypes. Forty Parkinson's disease (PD) patients, 25 PSP-P, 40 PSP-RS, and 34 controls were enrolled following the consensus criteria diagnosis. Participants were evaluated with neuropsychological battery for cognitive domains. Random Forest models were used for exploring the discriminant power of the cognitive tests in distinguishing among the four groups. The classifiers for distinguishing diseases from controls reached high accuracies (86% for PD, 95% for PSP-P, 99% for PSP-RS). Regarding the differential diagnosis, PD was discriminated from PSP-P with 91% (important variables: HAMA, MMSE, JLO, RAVLT_I, BDI-II) and from PSP-RS with 92% (important variables: COWAT, JLO, FAB). PSP-P was distinguished from PSP-RS with 84% (important variables: JLO, WCFST, RAVLT_I, Digit span_F). This study revealed that PSP-P, PSP-RS and PD had peculiar cognitive deficits compared with healthy subjects, from which they were discriminated with optimal accuracies. Moreover, high accuracies were reached also in differential diagnosis. Most importantly, Machine Learning resulted to be useful to the clinical neuropsychologist in choosing the most appropriate neuropsychological tests for the cognitive evaluation of PSP patients.