The Spectrum of Neurological and Sensory Abnormalities in Gaucher Disease Patients: A Multidisciplinary Study (SENOPRO).

Gaucher disease (GD) has been increasingly recognized as a continuum of phenotypes with variable neurological and sensory involvement. No study has yet specifically explored the spectrum of neuropsychiatric and sensory abnormalities in GD patients through a multidisciplinary approach. Abnormalities involving the nervous system, including sensory abnormalities, cognitive disturbances, and psychiatric comorbidities, have been identified in GD1 and GD3 patients. In this prospective study, named SENOPRO, we performed neurological, neuroradiological, neuropsychological, ophthalmological, and hearing assessments in 22 GD patients: 19 GD1 and 3 GD3. First, we highlighted a high rate of parkinsonian motor and non-motor symptoms (including high rates of excessive daytime sleepiness), especially in GD1 patients harboring severe glucocerebrosidase variants. Secondly, neuropsychological evaluations revealed a high prevalence of cognitive impairment and psychiatric disturbances, both in patients initially classified as GD1 and GD3. Thirdly, hippocampal brain volume reduction was associated with impaired short- and long-term performance in an episodic memory test. Fourthly, audiometric assessment showed an impaired speech perception in noise in the majority of patients, indicative of an impaired central processing of hearing, associated with high rates of slight hearing loss both in GD1 and GD3 patients. Finally, relevant structural and functional abnormalities along the visual system were found both in GD1 and GD3 patients by means of visual evoked potentials and optical coherence tomography. Overall, our findings support the concept of GD as a spectrum of disease subtypes, and support the importance of in-depth periodic monitoring of cognitive and motor performances, mood, sleep patterns, and sensory abnormalities in all patients with GD, independently from the patient's initial classification.

The Admission Experience Survey Italian Version (I-AES): A factor analytic study on a sample of 156 acute psychiatric in-patients.

Coercive treatments are often regarded as an inevitable and yet highly debated feature of psychiatric care. Perceived coercion is often reported by patients involuntarily committed as well as their voluntary counterparts. The Admission Experience Survey (AES) is a reliable tool for measuring perceived coercion in mental hospital admission. We developed the Italian AES (I-AES) through translation back-translation and administered it to 156 acutely hospitalized patients (48% women, 69% voluntarily committed) in two university hospitals in Rome (Policlinico Umberto I, Sant'Andrea Hospital). A principal component analysis (PCA) with equamax rotation was conducted. The I-AES showed good internal consistency (Cronbach's alpha = 0.90); Guttmann split-half reliability coefficient was 0.90. AES total score significantly differed between voluntary and involuntary committed patients (5.08 ±â€¯4.1 vs. 8.1 ±â€¯4.9, p < .05). PCA disclosed a three-factor solution explaining 59.3 of the variance. Some discrepancies were found between the factor structure of the I-AES and the original version. I-AES total score was positively associated with numbers of previous involuntarily hospitalization (r = 0.20, p < .05) and psychiatric symptoms' severity (r = 0.22, p < .02). I-AES and its proposed new factor structure proved to be reliable to assess perceived coercion in mental hospital admission. Consequently, it may represent a helpful instrument for the study and reduction of patients' levels of perceived coercion.

The Detection of Malingering: A New Tool to Identify Made-Up Depression.

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.