Clinical EEG and Neuroscience

Background: Quantitative electroencephalography (qEEG) data can facilitate the monitoring of treatment progress and the evaluation of therapeutic responses in patients with Major Depressive Disorder (MDD). This study aims to compare the qEEG data of MDD patients and healthy controls, both before and after treatment, to assess the effect of treatment response on neural activity. Methods: A total of 72 patients, aged 18–60, who had not used any psychopharmacological medication for at least two weeks, were included in the study. Based on a minimum 50% reduction in scores on the Hamilton Depression Rating Scale (HDRS-17) and Hamilton Anxiety Rating Scale (HARS), the patients were divided into two groups: responders (n = 51) and non-responders (n = 21). qEEG data were recorded before and after treatment. Results: Responders exhibited a significant shift in cortical activity—particularly in theta, alpha, and high-beta power—toward patterns resembling those observed in the healthy control group (improvement range: 15% to 67%). In contrast, non-responders showed minimal changes in cortical activity (improvement range: 38% to 46%). These findings suggest that while qEEG spectral data reflect marked neural changes in responders, no significant alterations occur in non-responders. Conclusion: The use of qEEG spectral analysis to monitor MDD patients provides valuable insights into treatment efficacy. The distinct patterns of cortical activity observed across most brain regions before treatment, compared to healthy individuals, highlight the potential of qEEG to predict treatment outcomes.

Sleep deprivation has become a severe public health problem in modern societies. Negative consequences of prolonged wakefulness on cognitive abilities have been demonstrated and working memory is one of the main cognitive functions that can be affected by sleep deprivation. This study aims to investigate the effects of sleep deprivation on working memory through EEG event-related oscillations. Thirty healthy young adult university students and graduates were included in this study (15 rested control – 15 sleep-deprived). A 2-back task was used to evaluate working memory, and both groups performed the task during EEG recording. The sleep-deprived (SD) group was required to stay awake for 24 h, and then the EEG session was conducted. The rested control (RC) subjects participated in the morning after a regular night's sleep. Event-related power and phase-locking analyses were applied, and delta (1–3.5 Hz), theta (4–6.5 Hz) and alpha (8–13 Hz) frequencies were investigated in the time-frequency domain. In the 2-back task, significantly prolonged reaction times were observed in the SD group. However, the decrease in accuracy rate was not significant. The EEG analyses revealed that the SD group had decreased frontocentral event-related delta and theta power responses after the presentation of stimuli. Moreover, task accuracy was positively correlated with the left frontocentral delta power in the SD group, and theta power in the RCs. Thus, we propose that the adverse effects of sleep deprivation on working memory can be observed through low-frequency oscillatory responses in the brain.

Objective: Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals’ lives. Early detection is crucial for improving treatment outcomes, but the diagnostic process remains complex due to the disorder's multifaceted nature. In recent years, EEG data have been increasingly investigated to detect neural patterns linked to schizophrenia. Methods: This study presents a deep learning framework that integrates both raw multi-channel EEG signals and their spectrograms. Our two-branch model processes these complementary data views to capture both temporal dynamics and frequency-specific features while employing depth-wise convolution to efficiently combine spatial dependencies across EEG channels. Results: The model was evaluated on two datasets, consisting of 84 and 28 subjects, achieving classification accuracies of 0.985 and 0.994, respectively. These results highlight the effectiveness of combining raw EEG signals with their time-frequency representations for precise and automated schizophrenia detection. Additionally, an ablation study assessed the contributions of different architectural components. Conclusions: The approach outperformed existing methods in the literature, underscoring the value of utilizing multi-view EEG data in schizophrenia detection. These promising results suggest that our framework could contribute to more effective diagnostic tools in clinical practice.

Objective: Substance use disorders (SUD) still represent a huge worldwide health problem, as, despite withdrawal, medication, social support and psychotherapy, the relapse rate (around 80% at one year following treatment) remains tremendously high. Therefore, an important challenge consists in finding new complementary add-on tools to enhance quality of care. Methods and Results: In this report we focus on new insights reported through the use of three electrophysiological tools (quantitative electroencephalography (EEG), QEEG; cognitive event-related potentials, ERPs; and neurofeedback) suggesting that their use might be helpful at the clinical level in the management of various forms of SUDs. Empirical evidence were presented. Conclusion: In light of encouraging results obtained highlighting how these electrophysiological tools may be used in the treatment of SUDs, further studies are needed in order to facilitate the implementation of such procedures in clinical care units.

Magnetic resonance imaging (MRI) sequences commonly used in simultaneous electroencephalogram (EEG)-MRI studies include blood oxygenation level-dependent (BOLD) and anatomical T₁-weighted MRI. Safety and electrode heating profiles for these sequences have been well-characterized. However, recent improvements in EEG design may allow for additional sequences to be performed with similar expectations of heating safety, which would expand the EEG-MRI infrastructure for quantitative physiological studies. We evaluated temperature changes ex vivo and in vivo over a wider range of preparation and readout modules with differing specific absorption rate (SAR). A 32-channel EEG cap was used at 3 T and ex vivo heating was assessed for 2D- and 3D-pseudo-continuous-arterial-spin-labeling, 2D-cine, 2D-phase-contrast, 2D T₂-Relaxation-Under-Spin-Tagging, 32-direction b = 1000 s/mm² and b = 2000 s/mm² 2D-diffusion tensor imaging, multiband-BOLD, 3D-T1 MPRAGE, 3D-FLAIR, and 3D-T2. Temperature was monitored with a fiberoptic probe system and plotted over six different electrodes, the amplifier, and battery pack. In vivo assessments were conducted in three participants with the same system. A further in vivo supplemental cohort (n = 10) was used to further evaluate qualitative self-reported heating. Device integrity was evaluated by the manufacturer following experiments. Peak temperature and maximum temperature increases were 23.0°C and 0.4°C respectively ex vivo, and 37.6°C and 0.7°C respectively in vivo. Temperatures did not approach the safety heating threshold of 40°C (defined as a conservative threshold based on manufacturer recommendations and burn injury data). Participants completed in vivo scans without adverse events. No manufacturer-reported device damage was identified. Overall, the tested scans induced heating below critical limits at the clinical field strength of 3 T.

Objectives: Neurotoxicity, encephalopathy, and seizures can occur following chimeric antigen receptor (CAR)-T cell therapy. Our aim was to assess what value electroencephalography (EEG) offers for people undergoing CAR-T treatment in clinical practice, including possible diagnostic, management, and prognostic roles. Methods: All patients developing CAR-T related neurotoxicity referred for EEG were eligible for inclusion. Reasons for EEG referral and qualitative EEG findings were analysed and reported. The relationship between objective quantitative EEG (QEEG) encephalopathy grade and clinical neurotoxicity (immune effector cell-associated neurotoxicity syndrome; ICANS) grade was determined. The prognostic ability of QEEG grade was assessed for survival and functional status. Results: Twenty-eight patients with 53 EEG recordings were included. Common reasons given on EEG referrals were possible seizure diagnosis (n = 38), reduced consciousness (n = 8), and superimposed cerebral infection (n = 4). Four focal seizures were detected on three (3/53; 5.7%) EEGs. There was a moderately positive correlation between QEEG grade and ICANS grade (r = + 0.41, p = .030). QEEG grade could not predict survival at 3 months (Area Under Curve; AUC = 0.673) or 6 months (AUC = 0.578), nor could it predict functional status at 1 month (r = + 0.40; p = .080), 3 months (r = + 0.19; p = .439), or time to return to baseline (r = + 0.32; p = .156). Conclusions: EEG was useful in seizure diagnosis. QEEG has a possible role as a specific biomarker of encephalopathy/neurotoxicity. EEG generated no tangible changes in patient management. QEEG was unable to prognosticate survival or functional status.

Evoked potential metrics extracted from an EEG exam can provide novel sources of information regarding brain function. While the P300 occurring around 300 ms post-stimulus has been extensively investigated in relation to mild cognitive impairment (MCI), with decreased amplitude and increased latency, the P200 response has not, particularly in an oddball-stimulus paradigm. This study compares the auditory P200 amplitudes between MCI (28 patients aged 74(8)) and non-MCI, (35 aged 72(4)). Data were collected in routine clinical evaluations where EEG with audio oddball ERPs were measured as part of a health screening exam from 2 clinics serving MCI patients and one clinic serving a non-MCI population as part of a wellness/preventative care program. We also investigated the disease course for 3 patients as case studies. The results revealed the P200 amplitudes to be significantly increased in the MCI compared to the non-MCI groups, alongside the expected reduction in P300, Trail Making, and reaction time. Moreover, the ratio of P200-to-P300 was also increased in the MCI groups even in cases where the P300 was strong. This trend continued for patients who were tracked from early-to-later stages in the case studies. While the pathophysiology of the P200 response in a 2-tone auditory oddball protocol is not well understood, this measure may help indicate signs of early MCI, particularly in cases where the P300 is still strong.

Background. This study aims to characterize the clinical phenotype of a family with two siblings exhibiting neurological manifestations, utilizing whole exome sequencing (WES) to identify potential pathogenic variants within the NRXN2 gene. Methods. A consanguineous family with two affected siblings displaying developmental delay, severe intellectual disability, epilepsy, and speech delay was examined. WES was performed on DNA samples from affected and unaffected family members, followed by a comprehensive bioinformatics analysis. In-silico tools were employed for variant interpretation and structural modeling of the NRXN2 protein. Clinical and genetic data were integrated to elucidate the potential impact of the identified variant. Results. WES revealed a novel homozygous missense variant (c.1475T>G, p.Leu492Arg) in the NRXN2 gene in both affected siblings. This variant was absent in healthy family members and public databases. In-silico analysis predicted a detrimental effect on protein function. Parental segregation confirmed heterozygous carrier status. The variant was classified as ‘Likely Pathogenic’ based on ACMG/AMP criteria. Conclusion. This study identifies a novel homozygous missense variant in NRXN2 associated with global developmental delay, severe intellectual disability, speech delay and epilepsy. The findings underscore the critical role of NRXN2 in neurodevelopment and highlight the potential implications of genetic variations within this gene in neurodevelopmental disorders. Further research and functional validation are warranted to deepen our understanding of NRXN2-related disorders and explore potential therapeutic interventions.

Holoprosencephaly is a congenital malformation of the central nervous system resulting from failure of the rostral neural tube to bifurcate into the two cerebral hemispheres. Deep brain structures including the thalamus, hypothalamus, and basal ganglia can also be affected to varying degrees. Here we present a patient with a rare de novo pathogenic variant in the PPP1R12A gene and the middle interhemispheric (MIH) variant of holoprosencephaly with hypersynchronous patterns on electroencephalography (EEG). The most prevalent abnormal pattern was abundant hypersynchronous rhythmic theta activity most prominent over the bilateral centro-parietal regions. There was also frequent hypersynchronous rhythmic beta activity and rhythmic alpha range activity, which occurred both synchronously and asynchronously. Finally, there were occasional periods of voltage attenuation interrupting hypersynchronous theta activity. While hypersynchronous theta activity and episodic attenuation have been previously described in alobar and semilobar variants of holoprosencephaly, our report is the first to describe these findings in a patient with the MIH variant as well as the first to describe EEG patterns in a patient with a pathogenic variant in the PPP1R12A gene mutations in which are associated with urogenital and/or brain malformation syndrome. Additionally, the hypersynchronous alpha activity is the first report of such an EEG pattern in holoprosencephaly. In order to develop a more complete understanding of EEG patterns in holoprosencephaly further study is needed but this is challenged by the relative rarity of the disease.