Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach.

The aim of this study was to validate a novel classification for the diagnosis of PNESs. Fifty-five PNES video-EEG recordings were retrospectively analyzed by four epileptologists and one psychiatrist in a blind manner and classified into four distinct groups: Hypermotor (H), Akinetic (A), Focal Motor (FM), and with Subjective Symptoms (SS). Eleven signs and symptoms, which are frequently found in PNESs, were chosen for statistical validation of our classification. An artificial neural network (ANN) analyzed PNES video recordings based on the signs and symptoms mentioned above. By comparing results produced by the ANN with classifications given by examiners, we were able to understand whether such classification was objective and generalizable. Through accordance metrics based on signs and symptoms (range: 0-100%), we found that most of the seizures belonging to class A showed a high degree of accordance (mean±SD=73%±5%); a similar pattern was found for class SS (80% slightly lower accordance was reported for class H (58%±18%)), with a minimum of 30% in some cases. Low agreement arose from the FM group. Seizures were univocally assigned to a given class in 83.6% of seizures. The ANN classified PNESs in the same way as visual examination in 86.7%. Agreement between ANN classification and visual classification reached 83.3% (SD=17.8%) accordance for class H, 100% (SD=22%) for class A, 83.3% (SD=21.2%) for class SS, and 50% (SD=19.52%) for class FM. This is the first study in which the validity of a new PNES classification was established and reached in two different ways. Video-EEG evaluation needs to be performed by an experienced clinician, but later on, it may be fed into ANN analysis, whose feedback will provide guidance for differential diagnosis. Our analysis, supported by the ML approach, showed that this model of classification could be objectively performed by video-EEG examination.

Cognitive processess and cognitive reserve in multiple sclerosis.

Multiple Sclerosis (MS) is characterized by motor, cognitive, and neuropsychiatric symptoms, which can occur independently. While MS is traditionally considered an inflammatory disease of the white matter, degeneration of gray matter is increasingly recognized as an important contributor to the progressive cognitive decline. A protective factor against the progression of cognitive dysfunction in MS could be the cognitive reserve, defined as resistance to brain dysfunction. Aim of the present study is to evaluate the role of cognitive reserve for different aspects of cognitive dysfunction of patients with MS. We found that patients with MS and lower cognitive reserve have poorer neuropsychological performance and slower information speed processing. These findings support the notion that intellectual reserve may protect some aspects of cognitive function in patients with MS.