Late infantile epileptic encephalopathy: A distinct developmental and epileptic encephalopathy syndrome

Objective: Within the spectrum of developmental and epileptic encephalopathy (DEE), there are a group of infants with features that are distinct from the well-recognized syndromes of early infantile developmental and epileptic encephalopathy (EIDEE), infantile epileptic spasm syndrome (IESS), and Lennox-Gastaut syndrome (LGS). We refer to this condition as late infantile epileptic encephalopathy (LIEE). Our objective is to highlight the characteristics of this group by analyzing patients who exhibit prototypical features. Methods: From July 2022 to May 2023, we searched for LIEE features in pediatric patients who underwent epilepsy follow-up at the University of Chicago Comer Children's Hospital. Results: Out of 850 patients evaluated, thirty patients (3.5%) were identified with LIEE based on electroclinical characteristics. These patients had an average onset of epilepsy at 6.8 months and an average onset of LIEE features at 18.1 months. The epilepsy etiology was most commonly genetic and metabolic (50%), followed by congenital cortical malformations (23%), acquired structural abnormalities (20%), and unknown (7%). The predominant seizure types were myoclonic-tonic (70%), spasm-tonic (50%), epileptic spasms (47%), tonic (43%), and myoclonic (43%) seizures. All patients reported a history of either spasm-tonic or myoclonic-tonic seizures in addition to other types. All patients had EEGs showing discontinuity, electro-decrements, or both along with diffuse slowing, background voltages between 100-300 μV, and superimposed multifocal, diffuse epileptiform discharges. Every patient, except one, fulfilled the definition of drug-resistant epilepsy and all reported either moderate to severe developmental delay. Significance: Late infantile epileptic encephalopathy (LIEE) is characterized by several unique clinical and electrographic features. Typically, LIEE manifests in patients during the second year of life and occurs before two years of age, hence late-infantile onset. The condition is commonly observed in infants with symptomatic epilepsy. Myoclonic-tonic and spasm-tonic seizures are the quintessential seizure types. The inter-ictal EEG exhibits more organization and lower voltages than seen with hypsarhythmia and lacks the defining EEG characteristics of EIDEE, IESS, or LGS. We propose that LIEE is a distinct electroclinical syndrome within the spectrum of developmental and epileptic encephalopathies

Mental Fatigue: Examining Cognitive Performance and Driving Behavior in Young Adults

Mental fatigue causes an increase in task-based EEG theta and alpha power and a decrease in performance (for a review, see Tran et al., 2020). However, little is known about the emergence of mental fatigue in resting state EEG recordings and whether the progression of mental fatigue over time is influenced by individual differences. The current dissertation examined the utility of resting state EEG as a measure of mental fatigue by testing whether EEG power changed in young adults over the course of a cognitively demanding battery of tasks. The current dissertation also tested how this measure of mental fatigue interacted with individual differences in ADHD symptomology to predict performance on one of the cognitive tasks as well as performance in a driving simulator. Resting state EEG was recorded at four intervals, before and after the three cognitively demanding tasks. Driving outcomes were collected at a separate visit to a driving simulator lab. Results indicated that resting state EEG theta and alpha power significantly decreased over time, but this association was not influenced by levels of ADHD symptomology. There was no evidence that resting state EEG power changes over time predicted cognitive or driving performance, even when ADHD symptomology was included. The current findings present preliminary evidence that resting state EEG power can be used as a marker of mental fatigue and provide unique insight into how mental fatigue develops by including an initial measurement of neural readiness before individuals engage in a cognitively demanding task

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Peer reviewe

NON INVASIVE INVESTIGATION OF SENSORIMOTOR CONTROL FOR FUTURE DEVELOPMENT OF BRAIN-MACHINE-INTERFACE (BMI)

My thesis focuses on describing novel functional connectivity properties of the sensorimotor system that are of potential interest in the field of brain-machine interface. In particular, I have investigated how the connectivity changes as a consequence of either pathologic conditions or spontaneous fluctuations of the brain's internal state. An ad-hoc electronic device has been developed to implement the appropriate experimental settings. First, the functional communication among sensorimotor primary nodes was investigated in multiple sclerosis patients afflicted by persistent fatigue. I selected this condition, for which there is no effective pharmacological treatment, since existing literature links this type of fatigue to the motor control system. In this study, electroencephalographic (EEG) and electromyographic (EMG) traces were acquired together with the pressure exerted on a bulb during an isometric hand grip. The results showed a higher frequency connection between central and peripheral nervous systems (CMC) and an overcorrection of the exerted movement in fatigued multiple sclerosis patients. In fact, even though any fatigue-dependent brain and muscular oscillatory activity alterations were absent, their connectivity worked at higher frequencies as fatigue increased, explaining 67% of the fatigue scale (MFIS) variance (p=.002). In other terms, the functional communication within the central-peripheral nervous systems, namely involving primary sensorimotor areas, was sensitive to tiny alterations in neural connectivity leading to fatigue, well before the appearance of impairments in single nodes of the network. The second study was about connectivity intended as propagation of information and studied in dependence on spontaneous fluctuations of the sensorimotor system triggered by an external stimulus. Knowledge of the propagation mechanisms and of their changes is essential to extract significant information from single trials. The EEG traces were acquired during transcranial magnetic stimulation (TMS) to yield to a deeper knowledge about the response to an external stimulation while the cortico-spinal system passes through different states. The results showed that spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. As probed by TMS, this behavior indicates that the effective connectivity within bilateral circuits orchestrating hand control are dynamically modulated in time even in resting state. The third investigation assessed the plastic changes in the sensorimotor system after stroke induced by 3 months of robotic rehabilitation in chronic phase. A functional source extraction procedure was applied on the acquired EEG data, enabling the investigation of the functional connectivity between homologous areas in the resting state. The most significant result was that the clinical ameliorations were associated to a ‘normalization’ of the functional connectivity between homologous areas. In fact, the brain connectivity did not necessarily increase or decrease, but it settled within a ‘physiological’ range of connectivity. These studies strengthen our knowledge about the behavioral role of the functional connectivity among neuronal networks’ nodes, which will be essential in future developments of enhanced rehabilitative interventions, including brain-machine interfaces. The presented research also moves the definition of new indices of clinical state evaluation relevant for compensating interventions, a step forward

Relationship of Intelligence to Cortical Arousal During Verbal and Spatial Tasks

Psycholog

Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédic

Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)