Angela Laganà

Slow Repetitive TMS for Drug-resistant Epilepsy: Clinical and EEG Findings of a Placebo-controlled Trial

Summary:  Purpose: To assess the effectiveness of slow repetitive transcranial magnetic stimulation (rTMS) as an adjunctive treatment for drug‐resistant epilepsy.

Prevalence and Characteristics of Epilepsy in the Aeolian Islands

Summary:  Purpose: To estimate the prevalence and define the clinical characteristics of epileptic disorders in the 13,431 residents of the Sicilian Aeolian archipelago, on June 1, 1999.

Identification of three distinct groups of patients with both epilepsy and psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) can be observed in patients with or without epilepsy (mixed and pure PNES). Patients with mixed PNES are usually considered to be a homogeneous group characterized by the coexistent epilepsy. Our study found that patients with mixed PNES were not homogeneous, but could be divided into three groups based on epilepsy type, mental level, comorbid psychiatric disorders, and history of traumatic experiences. Group 1 patients have pharmacoresistant epilepsy, normal cognition, and comorbid anxiety and/or depressive disorders. Here, PNES etiology is the epilepsy-related problems. In group 2 patients, the epilepsy is associated with mental retardation and dependent personality traits. PNES etiology is represented by the reduction or cessation of seizures. The PNES allow patients to continue receiving attention from caregivers. Group 3 patients have epilepsy, normal cognition, comorbid cluster B personality disorders and anxiety disorders, and psychic trauma. Here, PNES etiology is not related to the epilepsy, but to the psychic trauma.

The semiology of psychogenic nonepileptic seizures revisited: Can video alone predict the diagnosis? Preliminary data from a prospective feasibility study

To investigate if, when, and to what extent visual information contained in a video‐recorded event allows experienced epileptologists to predict the diagnosis of psychogenic nonepileptic seizures (PNES) without the aid of electroencephalography (EEG).

Epileptic seizures in subjects previously affected by disorders of arousal

Six patients (3M, 3F, mean age 17.3 yrs) presenting different types of evolution from disorders of arousal to epilepsy are described. All subjects during their childhood had been diagnosed in a sleep center as affected by sleep-walking (three cases) and night terrors (the other three). Successively they developed nocturnal events different from those previously exhibited and consisting of clear epileptic seizures, generalized tonic-clonic in one case and complex partial in five cases. Nocturnal monitoring allowed recognition of clear interictal paroxysmal activity in three patients, while ictal events were recorded in the remaining three. Anticonvulsant treatment (carbamazepine in five patients, phenytoin in one patient) led to the resolution of the ictal events in all cases. The fact that both disorders of arousal and epilepsy are strictly related to sleep and often share common features such as age range of onset and precipitating factors, suggests the existence of common functional substrates identifiable in constitutional maturative and biologic factors. The possible occurrence of seizures in subjects exhibiting parasomnia during their childhood has to be considered in patients with familial history of epilepsy and in all doubtful cases.

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.

Vagoglossopharyngeal neuralgia: a rare case of sincope responding to pregabalin

Glossopharyngeal neuralgia (GF) is a rare disease with a 1-year incidence of 0.7/100 000 (1). This condition is characterized by needling, intermittent and lancing pain in the distribution of ninth cranial nerve, induced by swallowing, chewing or touching. In 2% of cases, GF is associated with syncope or convulsive syncope, namely vagoglossopharyngeal neuralgia. Although GF is generally idiopathic, it has been reported with vascular malformation, compressive masses and demyelinating disease (2). As with trigeminal neuralgia, elective therapy consists of older anticonvulsants and, in particular, carbamazepine. Lamotrigine and gabapentin, however, have recently been shown to be efficacious in GF treatment (3, 4). In addition to these medications, pregabalin has been reported to be helpful in a case of typical GF (5). We report a case of GF with severe loss of consciousness, which has been successfully and rapidly treated with pregabalin.

In response: Towards a quantitative assessment of psychogenic nonepileptic seizures

None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Sara Gasparini Edoardo Ferlazzo Chiara Sueri Vittoria Cianci Umberto Aguglia u.aguglia@tin.it Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy; and Regional Epilepsy Center, Bianchi Melacrino Morelli Hospital, Reggio Calabria, Italy