Interaction between Neuroanatomical and Psychological Changes after Mindfulness-Based Training

Several cross-sectional studies have documented neuroanatomical changes in individuals with a long history of meditation, while a few evidences are available about the interaction between neuroanatomical and psychological changes even during brief exposure to meditation. Here we analyzed several morphometric indexes at both cortical and subcortical brain level, as well as multiple psychological dimensions, before and after a brief -8 weeks- Mindfulness Based Stress Reduction (MBSR) training program, in a group of 23 meditation naïve-subjects compared to age-gender matched subjects. We found a significant cortical thickness increase in the right insula and the somatosensory cortex of MBSR trainees, coupled with a significant reduction of several psychological indices related to worry, state anxiety, depression and alexithymia. Most importantly, an interesting correlation between the increase in right insula thickness and the decrease in alexithymia levels during the MBSR training were observed. Moreover, a multivariate pattern classification approach allowed to identify a cluster of regions more responsive to MBSR training across subjects. Taken together, these findings documented the significant impact of a brief MBSR training on brain structures, as well as stressing the idea of MBSR as a valuable tool for alexithymia modulation, also originally providing a plausible neurobiological evidence of a major role of right insula into mediating the observed psychological changes

Suitability of electroencephalography in brain death determination: a monocentric, 10-year retrospective, observational investigation of 428 cases

Background We aimed to verify the usefulness of electroencephalographic (EEG) activity recording (that is mandatory according to the Italian law), in addition to two clinical evaluations spaced 6 h, among the procedures of brain death determination (BDD) in adult individuals. Methods The study is a monocentric, retrospective analysis of all BDDs performed in the last 10 years at Policlinico Le Scotte in Siena (Italy). Results Of the 428 cases revised (mean age 67.6 ± 15.03 years; range 24–92 years), 225 were males and 203 females. In total, 212 out of 428 patients (49.5%) were donors. None of the BDD procedures were interrupted due to the reappearance of EEG activity (neither for clinical reasons) at any sampling time, with the exception of one case that was considered a false negative at critical reinspection of the EEG. In 6/428 cases (1.4%), a cardiac arrest occurred during the 6 h between the first and second evaluation, thus missing the opportunity to take organs from these patients because the BDD procedure was not completed. Conclusions Once the initial clinical examination before convening the BDD Commission has ascertained the absence of brainstem reflexes and of spontaneous breathing, and these clinical findings are supported by a flat EEG recording, the repetition of a 30-min EEG twice over a 6 h period seems not to add additional useful information to clinical findings. Current data, if confirmed in other centers and possibly in prospective studies, may help to promote a scientific and bioethical debate in Italy, as well as in other countries where the EEG is still mandatory, for eventually pdating the procedures of BDD. © 2022, The Author(s)

Psychological and Brain Connectivity Changes Following Trauma-Focused CBT and EMDR Treatment in Single-Episode PTSD Patients

Among the different therapeutic alternatives for post-traumatic stress disorder (PTSD), Trauma-Focused Cognitive-Behavioral Therapy (TF-CBT) and Eye Movement Desensitization and Reprocessing (EMDR) Therapy have shown promising results in helping patients cope with PTSD symptoms. However, given the different theoretical and methodological substrate of TF-CBT and EMDR, a potentially different impact on the brain for the two interventions could be hypothesized, as well as an interaction between trauma-specific PTSD symptomatology and response to a given psychotherapy. In this study, we monitored psychological and spontaneous functional connectivity fMRI patterns in two groups of PTSD patients who suffered by the same traumatic event (i.e., natural disaster), before and after a cycle of psychotherapy sessions based on TF-CBT and EMDR. Thirty-seven (37) PTSD patients were enrolled from a larger sample of people exposed to a single, acute psychological stress (i.e., 2002 earthquake in San Giuliano di Puglia, Italy). Patients were randomly assigned to TF-CBT (n = 14) or EMDR (n = 17) psychotherapy. Clinical assessment was performed using the Clinician-Administered PTSD Scale (CAPS), the Davidson Trauma Scale (DTS) and the Work and Social Adjustment Scale (WSAS), both at baseline and after treatment. All patients underwent a fMRI data acquisition session before and after treatment, aimed at characterizing their functional connectivity (FC) profile at rest, as well as potential connectivity changes associated with the clinical impact of psychotherapy. Both EMDR and TF-CBT induced statistically significant changes in clinical scores, with no difference in the clinical impact of the two treatments. Specific changes in FC correlated with the improvement at the different clinical scores, and differently for EMDR and TF-CBT. However, a similarity in the connectivity changes associated with changes in CAPS in both groups was also observed. Specifically, changes at CAPS in the entire sample correlated with an (i) increase in connectivity between the bilateral superior medial frontal gyrus and right temporal pole, and a (ii) decrease in connectivity between left cuneus and left temporal pole. Results point to a similar, beneficial psychological impact of EMDR and TF-CBT for treatment of natural-disaster PTSD patients. Neuroimaging data suggest a similar neurophysiological substrate for clinical improvement following EMDR and TF-CBT, involving changes affecting bilateral temporal pole connectivity

EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings

Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis.

Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics

Cerebro-cerebellar functional connectivity profile of an epilepsy patient with periventricular nodular heterotopia

Periventricular nodular heterotopia (PNH) is a rare malformation of cortical development often associated with drug resistant focal onset epilepsy. The link between nodules and neocortex have been demonstrated with depth electrodes investigations showing that seizures may arise from both structures. In the last years fMRI resting-state (fMRI-RS) have received a surge in interest due to its capability to track non-invasively physiological and pathological relevant differences in brain network organization. We performed a cerebro-cerebellar voxel-wise and region-of-interest resting state fMRI (RS-fMRI) functional connectivity analysis in a seizure-free epilepsy patient with a PNH in the right temporal horn. Our finding confirms a spontaneous synchronization between PNH and its surrounding cortex, specifically in the inferior temporal, fusiform and occipital gyrus. We also found a significant connectivity with bilateral cerebellum, more intense and widespread on the PNH cerebellar contralateral lobule. RS-fMRI confirmed its potential as a promising tool for non-invasive mapping of cortical and subcortical brain functional organization

Algorithms for epilepsy monitoring

Epilepsy is a neurological disorder (Engel and Pedley, 2008), whose main clinical sign are the so-called “epileptic seizures, " having a significant social and sanitary impact, both due to its quite high incidence and chronicity. In fact, it is estimated that the lifetime prevalence of epilepsy is around 7.60 per 1000 persons, whereas its incidence rate appears to be of 61.44 per 100, 000 person years (Fiest et al., 2017). Seizures are unpredictable events so disability caused by epilepsy can be significant, due to uncertainty and consequences of the fits

A Patient-specific Approach for Short-term Epileptic Seizures Prediction through the Analysis of EEG synchronization

Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide. Objective: This work proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. Methods: We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. Results: We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. Conclusion and significance: The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use

Functional Connectivity and Genetic Profile of a “Double-Cortex”-Like Malformation

Laminar heterotopia is a rare condition consisting in an extra layer of gray matter under properly migrated cortex; it configures an atypical presentation of periventricular nodular heterotopia (PNH) or a double cortex (DC) syndrome. We conducted an original functional MRI (fMRI) analysis in a drug-resistant epilepsy patient with “double-cortex”-like malformation to reveal her functional connectivity (FC) as well as a wide genetic analysis to identify possible genetic substrates. Heterotopias were segmented into region of interests (ROIs), whose voxel-wise FC was compared to that of (i) its normally migrated counterpart, (ii) its contralateral homologous, and (iii) those of 30 age-matched healthy controls. Extensive genetic analysis was conducted to screen cortical malformations-associated genes. Compared to healthy controls, both laminar heterotopias and the overlying cortex showed significant reduction of FC with the contralateral hemisphere. Two heterozygous variants of uncertain clinical significance were found, involving autosomal recessive disease-causing genes, FAT4 and COL18A1. This first FC analysis of a unique case of “double-cortex”-like malformation revealed a hemispheric connectivity segregation both in the laminar cortex as in the correctly migrated one, with a new pattern of genes’ mutations. Our study suggests the altered FC could have an electrophysiological and functional impact on large-scale brain networks, and the involvement of not yet identified genes in “double-cortex”-like malformation with a possible role of rare variants in recessive genes as pathogenic cofactors