Advanced MRI morphologic study shows no atrophy in healthy individuals with hippocampal hyperintensity

Clinical and Instrumental Characterization of Patients With Late-Onset Epilepsy

Epilepsy is classically considered a childhood disease. However, it represents the third most frequent neurological condition in the elderly, following stroke, and dementia. With the progressive aging of the general population, the number of patients with Late-Onset Epilepsy (LOE) is constantly growing, with important economic and social consequences, in particular for the more developed countries where the percentage of elderly people is higher. The most common causes of LOE are structural, mainly secondary to cerebrovascular or infectious diseases, brain tumors, trauma, and metabolic or toxic conditions. Moreover, there is a growing body of evidence linking LOE with neurodegenerative diseases, particularly Alzheimer's disease (AD). However, despite a thorough characterization, the causes of LOE remain unknown in a considerable portion of patients, thus termed as Late-Onset Epilepsy of Unknown origin (LOEU). In order to identify the possible causes of the disease, with an important impact in terms of treatment and prognosis, LOE patients should always undergo an exhaustive phenotypic characterization. In this work, we provide a detailed review of the main clinical and instrumental techniques for the adequate characterization of LOE patients in the clinical practice. This work aims to provide an easy and effective tool that supports routine activity of the clinicians facing LOE.

The impact of sexual abuse on psychopathology of patients with psychogenic nonepileptic seizures

OBJECTIVES

In the present study, we evaluated if the presence of sexual abuse in the clinical history of patients with psychogenic non-epileptic seizures (PNES) is associated with a different psychopathological profile.

MATERIALS AND METHODS

In a consecutive population of 63 PNES patients, we compared two demographically and clinically matched groups of patients with (no. 15) and without (no. 48) a history of sexual abuse using a comprehensive psychopathological assessment (Beck Depression Inventory, Hamilton Anxiety Rating Scale, Dissociative Experience Scale, Somatoform Dissociation Questionnaire, and Toronto Alexithymia Scale).

RESULTS

We found that the group of patients reporting sexual abuse is characterized by higher scores on Dissociative Experience Scale (p = 0.003) and Beck Depression Inventory (p = 0.001) with respect to the other group. No significant statistical differences in Hamilton Anxiety Rating Scale (p = 0.103), Toronto Alexithymia Scale (p = 0.137), and Somatoform Dissociation Questionnaire (p = 0.486) were captured. Moreover, we found that the negative effect on dissociate symptoms was also hampered by the increasing of seizure frequency.

CONCLUSIONS

This study reinforces the importance of traumatic screening in the clinical spectrum of PNES in order to implement and improve specific therapeutic strategies.

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and fifty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after feature selection, was able to perform an individual classification of PNES from controls with a mean accuracy of 74.5%, revealing that brain regions influencing classification accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). This study provides Class II evidence that the considerable clinical and neurobiological heterogeneity observed in individuals with PNES might be overcome by ML algorithms trained on surface-based magnetic resonance imaging (MRI) data.

3-T magnetic resonance imaging simultaneous automated multimodal approach improves detection of ambiguous visual hippocampal sclerosis

BACKGROUND AND PURPOSE

To evaluate if an automatic magnetic resonance imaging (MRI) processing system may improve detection of hippocampal sclerosis (Hs) in patients with mesial temporal lobe epilepsy (MTLE).

METHODS

Eighty consecutive patients with a diagnosis of MTLE and 20 age- and sex-matched controls were prospectively recruited and included in our study. The entire group had 3-T MRI visual assessment of Hs analysed by two blinded imaging epilepsy experts. Logistic regression was used to evaluate the performances of neuroradiologists and multimodal analysis.

RESULTS

The multimodal automated tool gave no evidence of Hs in all 20 controls and classified the 80 MTLE patients as follows: normal MRI (54/80), left Hs (14/80), right Hs (11/80) and bilateral Hs (1/80). Of note, this multimodal automated tool was always concordant with the side of MTLE, as determined by a comprehensive electroclinical evaluation. In comparison with standard visual assessment, the multimodal automated tool resolved five ambiguous cases, being able to lateralize Hs in four patients and detecting one case of bilateral Hs. Moreover, comparing the performances of the three logistic regression models, the multimodal approach overcame performances obtained with a single image modality for both the hemispheres, reaching a global accuracy value of 0.97 for the right and 0.98 for the left hemisphere.

CONCLUSIONS

Multimodal quantitative automated MRI is a reliable and useful tool to depict and lateralize Hs in patients with MTLE, and may help to lateralize the side of MTLE especially in subtle and uncertain cases.