Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy.

To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology for the relative quantification of cerebral blood flow of 74 cortical and sub-cortical structures was employed. The optimal number of clusters (K) was estimated using a modified v-fold cross-validation for the use of K means algorithm. The two regions of interest (ROIs) that represent the hypoperfused and hyperperfused areas were identified. To select the structures related to the SUDEP-7 inventory score, a data mining method that computes an automatic feature selection was used. During the interictal and ictal state, the hyperperfused ROIs in the largest part of patients were the bilateral rectus gyrus, putamen as well as globus pallidus ipsilateral to the seizure onset zone. The hypoperfused ROIs included the red nucleus, substantia nigra, medulla, and entorhinal area. The findings indicated that the nearly invariability in the perfusion pattern during the interictal to ictal transition observed in the ipsi-lateral putamen F = 12.60, p = 0.03, entorhinal area F = 25.80, p = 0.01, and temporal middle gyrus F = 12.60, p = 0.03 is a potential biomarker of SUDEP risk. The results presented in this paper allowed identifying hypo- and hyperperfused brain regions during the ictal and interictal state potentially related to SUDEP risk stratification.

Common Ictal and Interictal Perfusion Patterns: A Window into the Epileptogenic Network and SUDEP Mechanism in Drug-Resistant Focal Epilepsy.

BACKGROUND: Focal epilepsies have been described as a network disease. Noninvasive investigative techniques have been used to characterize epileptogenic networks. OBJECTIVE: This study aimed to describe ictal and interictal cortical and subcortical perfusion patterns using single- photon emission computed tomography (SPECT) in patients with drug-resistant epilepsy (DRE). METHODS: Thirty-five interictal-ictal SPECT scans were obtained from 15 patients with DRE. A methodology was developed to get a relative perfusion index (PI) of 74 cortical and sub-cortical brain structures. K-means algorithm, together with modified v-fold cross-validation, was used to identify the two regions of interest (ROIs) that represent hypoperfused and hyperperfused areas. RESULTS: In common with the individual analysis, the statistical analysis evidenced that the hyperperfusion ROIs resulting from group analysis during interictal and ictal involved mainly the cingulate gyrus, cuneus, lingual gyrus, and gyrus rectus as well as the putamen. ROIs hypoperfused included the red nucleus, the substantia nigra, and the medulla. The medians of the group analysis of the hypoperfusion and hyperperfusion ROIs were 0.601-0.565 and 1.133-1.119 for the ictal and interictal states, correspondingly. A group of mostly cortical structures involved in the hyperperfused ROIs in both interictal and ictal states showed no change or negative change in the transition from interictal to ictal state (mean change of -0.002). On the other hand, the brain stem, basal ganglia, red nucleus, and thalamus revealed a mean global change of 0.19, indicating a mild increase in the PI. However, some of these structures (red nucleus, substantia nigra, and medulla oblongata) remained hypoperfused during the interictal to ictal transition. CONCLUSION: The methodology employed made it possible to identify common cortical and subcortical perfusion patterns not directly linked to epileptogenicity, for a better epileptogenic network and sudden unexpected death (SUDEP) mechanism in DRE.

Person identification through faces and voices: an ERP study.

Different models have been proposed to explain how identity is extracted from faces and voices and how these two sensory systems interact. The neural loci of audio-visual interactions have been studied using neuroimaging techniques; however, the time course of these interactions is not well established. Here, we use event related potentials (ERPs) to study the temporal dynamics of the interaction of face and voice processing modules. We presented to the subjects either faces alone (F), voices alone (V) or faces and voices together (FV) in a familiarity detection task. Responses obtained for FV were compared with the sum of the responses obtained for F plus responses for V, for familiar and unfamiliar stimuli. This comparison shows differences in amplitude for different latencies, indicating cross-modal interactions. For unfamiliar stimuli, this interaction began very early (around 200ms) and was restricted to the time window corresponding to the face N170 component. For familiar stimuli, the interaction was longer, began earlier and remained until after the N170 component. These results indicate that the interaction between faces and voices occurs from the initial stages of processing and continues as the person identification process goes on. This study is the first electrophysiological evidence of cross-modal interaction of faces and voices during the recognition of acquaintances' identities. It suggests that the assessment of person familiarity can result in direct information sharing between voice and face sensory modules from the early processing stages, before access to the person identity nodes.