Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

Functional brain networks: intra and inter-subject variability in healthy individuals and patients with neurological or neuropsychiatric diseases.

The projects of this thesis sits at the intersection between classical neuroscience and aspects related to engineering, signals’ and neuroimaging processing. Each of the three years has been dedicated to specific projects carried out on distinct datasets, groups of individuals/patients and methods, putting great emphasis on multidisciplinarity and international mobility. The studies carried out in Cagliari were based on EEG (electroencephalography), and the one conducted abroad was developed on functional magnetic resonance imaging (fMRI) data. The common thread of the project concerns variability and stability of individuals' features related primarily to functional connectivity and network, as well as to the periodic and aperiodic components of EEG power spectra, and their possible use for clinical purposes. In the first study (Fraschini et al., 2019) we aimed to investigate the impact of some of the most commonly used metrics to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-channel interaction. In the second study (Demuru et al., 2020) we performed a comparison between power spectral density and some widely used nodal network metrics, both at scalp and source level, with the aim of evaluating their possible association. The first first-authored study (Pani et al., 2020)was dedicated to investigate how the variability due to subject, session and task affects electroencephalogram(EEG) power, connectivity and network features estimated using source-reconstructed EEG time-series of healthy subjects. In the study carried out with the supervision of Prof. Fornito (https://doi.org/10.1016/j.pscychresns.2020.111202) during the experience at the Brain, Mind and Society Research Hub of Monash University, partial least square analysis has been applied on fMRI data of an healthy cohort to evaluate how different specific aspects of psychosis-like experiences related to functional connectivity. Due to the pandemic of Sars-Cov-2 it was impossible to continue recording the patients affected by neurological diseases (Parkinson’s, Diskynesia) involved in the study we planned for the third year, that should have replicated the design of the first first-authored one, with the aim of investigate how individual variability/stability of functional brain networks is affected by diseases. For the aforementioned reason, we carried out the last study on a dataset we finished to record in February 2020. The analysis has the aim of investigate whether it is possible by using 19 channels sleep scalp EEG to highlight differences in the brain of patients affected by non-rem parasomnias and sleep-related hypermotor epilepsy, when considering the periodic and aperiodic component of EEG power spectra

Making Waves in the Brain: What Are Oscillations, and Why Modulating Them Makes Sense for Brain Injury.

Traumatic brain injury (TBI) can result in persistent cognitive, behavioral and emotional deficits. However, the vast majority of patients are not chronically hospitalized; rather they have to manage their disabilities once they are discharged to home. Promoting recovery to pre-injury level is important from a patient care as well as a societal perspective. Electrical neuromodulation is one approach that has shown promise in alleviating symptoms associated with neurological disorders such as in Parkinson's disease (PD) and epilepsy. Consistent with this perspective, both animal and clinical studies have revealed that TBI alters physiological oscillatory rhythms. More recently several studies demonstrated that low frequency stimulation improves cognitive outcome in models of TBI. Specifically, stimulation of the septohippocampal circuit in the theta frequency entrained oscillations and improved spatial learning following TBI. In order to evaluate the potential of electrical deep brain stimulation for clinical translation we review the basic neurophysiology of oscillations, their role in cognition and how they are changed post-TBI. Furthermore, we highlight several factors for future pre-clinical and clinical studies to consider, with the hope that it will promote a hypothesis driven approach to subsequent experimental designs and ultimately successful translation to improve outcome in patients with TBI

Functional and effective connectivity during focal epileptic seizures [Poster]

The aim of this study was to investigate the dynamics of neuronal networks during focal seizures using dynamic imaging of coherent sources (DICS) (Gross et al. 2001) and renormalized partial directed coherence (RPDC) (Schelter et al. 2009). Ictal EEG recordings from a patient with drug resistant focal epilepsy, due to a focal cortical dysplasia (FCD) in the left parieto-occipital region, (shown by a high resolution 3-T MRI) were analyzed. DICS revealed the neuronal networks concomitant with the location of the FCD, shown by a high resolution 3-T MRI and areas of decreased metabolism shown by functional neuroimaging methods. The sources identified during the seizure onset and propagation phases were similar. Only the causality was different, showing that the strongest source, located in the occipito-temporal region, is most probably a pacemaker/seizure onset zone of the ictal neuronal networks in this case. The DICS analyses of pre-seizure phase showed the sources in the DMN areas of the brain. We can conclude that analyses of multiple habitual seizures of the same patients by the methods of DICS and RPDC gives us valuable information regarding the seizure onset zone and ictal networks. It can be a useful additive tool during the pre-epilepsy surgical investigations of the patients with drug resistant focal epilepsies

High-density EEG power topography and connectivity during confusional arousal.

Confusional arousal is the milder expression of a family of disorders known as Disorders of Arousal (DOA) from non-REM sleep. These disorders are characterized by recurrent abnormal behaviors that occur in a state of reduced awareness for the external environment. Despite frequent amnesia for the nocturnal events, when actively probed, patients are able to report vivid hallucinatory/dream-like mental imagery. Traditional (low-density) scalp and stereo-electroencephalographic (EEG) recordings previously showed a pathological admixture of slow oscillations typical of NREM sleep and wake-like fast-mixed frequencies during these phenomena. However, our knowledge about the specific neural EEG dynamics over the entire brain is limited. We collected 2 consecutive in-laboratory sleep recordings using high-density (hd)-EEG (256 vertex-referenced geodesic system) coupled with standard video-polysomnography (v-PSG) from a 12-year-old drug-naïve and otherwise healthy child with a long-lasting history of sleepwalking. Source power topography and functional connectivity were computed during 20 selected confusional arousal episodes (from -6 to +18 sec after motor onset), and during baseline slow wave sleep preceding each episode (from - 3 to -2 min before onset). We found a widespread increase in slow wave activity (SWA) theta, alpha, beta, gamma power, associated with a parallel decrease in the sigma range during behavioral episodes compared to baseline sleep. Bilateral Broadman area 7 and right Broadman areas 39 and 40 were relatively spared by the massive increase in SWA power. Functional SWA connectivity analysis revealed a drastic increase in the number and complexity of connections from baseline sleep to full-blown episodes, that mainly involved an increased out-flow from bilateral fronto-medial prefrontal cortex and left temporal lobe to other cortical regions. These effects could be appreciated in the 6 sec window preceding behavioral onset. Overall, our results support the idea that DOA are the expression of peculiar brain states, compatible with a partial re-emergence of consciousness

Chapter Sleep Spindles – As a Biomarker of Brain Function and Plasticity

Alternative & renewable energy sources & technolog

Sleep Spindles – As a Biomarker of Brain Function and Plasticity

Alternative & renewable energy sources & technolog