Brain oscillatory patterns in mild cognitive impairment due to Alzheimer’s and Parkinson’s disease: an exploratory high-density EEG study

Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis

A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.

Frequency-Dependent Microstate Characteristics for Mild Cognitive Impairment in Parkinson's Disease

Cognitive impairment is typically reflected in the time and frequency variations of electroencephalography (EEG). Integrating time-domain and frequency-domain analysis methods is essential to better understand and assess cognitive ability. Timely identification of cognitive levels in early Parkinson's disease (ePD) patients can help mitigate the risk of future dementia. For the investigation of the brain activity and states related to cognitive levels, this study recruited forty ePD patients for EEG microstate analysis, including 13 with mild cognitive impairment (MCI) and 27 without MCI (control group). To determine the specific frequency band on which the microstate analysis relies, a deep learning framework was employed to discern the frequency dependence of the cognitive level in ePD patients. The input to the convolutional neural network consisted of the power spectral density of multi-channel multi-point EEG signals. The visualization technique of gradient-weighted class activation mapping was utilized to extract the optimal frequency band for identifying MCI samples. Within this frequency band, microstate analysis was conducted and correlated with the Montreal Cognitive Assessment (MoCA) Scale. The deep neural network revealed significant differences in the 1-11.5Hz spectrum of the ePD-MCI group compared to the control group. In this characteristic frequency band, ePD-MCI patients exhibited a pattern of global microstate disorder. The coverage rate and occurrence frequency of microstate A and D increased significantly and were both negatively correlated with the MoCA scale. Meanwhile, the coverage, frequency and duration of microstate C decreased significantly and were positively correlated with the MoCA scale. Our work unveils abnormal microstate characteristics in ePD-MCI based on time-frequency fusion, enhancing our understanding of cognitively related brain dynamics and providing electrophysiological markers for ePD-MCI recognition.

A comparative study of posterior cingulate metabolism in patients with mild cognitive impairment due to Parkinson's disease or Alzheimer's disease

Few comparative studies have assessed metabolic brain changes in cognitive impairment among neurodegenerative disorders, and the posterior cingulate cortex (PCC) is a metabolically active brain region with high involvement in multiple cognitive processes. Therefore, in this study, metabolic abnormalities of the PCC were compared in patients with mild cognitive impairment (MCI) due to Parkinson's disease (PD) or Alzheimer's disease (AD), as examined by proton magnetic resonance spectroscopy (1H-MRS). Thirty-eight patients with idiopathic PD, including 20 with mild cognitive impairment (PDMCI) and 18 with normal cognitive function (PDN), 18 patients with probable mild cognitive impairment (ADMCI), and 25 healthy elderly controls (HCs) were recruited and underwent PCC 1H-MRS scans. Compared with HCs, patients with PDMCI exhibited significantly reduced concentrations of N-acetyl aspartate (NAA), total NAA (tNAA), choline (Cho), glutathione (GSH), glutamate + glutamine (Glx) and total creatine (tCr), while ADMCI cases exhibited significantly elevated levels of myo-inositol (Ins) and Ins/tCr ratio, as well as reduced NAA/Ins ratio. No significant metabolic changes were detected in PDN subjects. Compared with ADMCI, reduced NAA, Ins and tCr concentrations were detected in PDMCI. Besides, ROC curve analysis revealed that tCr concentration could differentiate PDMCI from PDN with an AUC of 0.71, and NAA/Ins ratio could differentiate patients with MCI from controls with normal cognitive function with an AUC of 0.74. Patients with PDMCI and ADMCI exhibited distinct PCC metabolic 1H-MRS profiles. The findings suggested cognitively normal PD patients with low NAA and tCr in the PCC might be at risk of preclinical PDMCI, and Ins and/or NAA/MI ratio in the PCC should be reconsidered a possible biomarker of preclinical MCI in clinical practice. So, comparing PCC's 1H-MRS profiles of cognitive impairment among neurodegenerative illnesses may provide useful information for better defining the disease process and elucidate possible treatment mechanisms.

Cognitive deficit in post-acute COVID-19: an opportunity for EEG evaluation?

BACKGROUND AND PURPOSE

Among the most common post-COVID symptoms, many patients experienced subjective cognitive deficit, commonly named "brain fog," that might be present also in those individuals without severe acute COVID-19 respiratory involvement. Some studies have investigated some of the mechanisms that might be associated with the brain fog with objective techniques including transcranial magnetic stimulation and neuroimaging.

METHODS

The aim of this study was to investigate the presence of electroencephalographic (EEG) alterations in people with post-COVID self-reported cognitive deficit.

RESULTS

Out of the 90 patients attending the post-COVID neurology ambulatory service, twenty patients presenting brain fog at least 4 weeks after acute non-severe COVID-19 infection, and without previous history of epilepsy, were investigated with 19-channel EEG, Montreal Cognitive Assessment (MoCA), and magnetic resonance imaging (MRI). EEG was found altered in 65% of the sample, among which 69% presented a slowing activity and 31% were characterized by epileptic discharges principally in the frontal areas. None of the patients showed DWI MRI lesions.

CONCLUSIONS

These findings highlight the usefulness of EEG analysis to objectively describe possible neurophysiological abnormalities in post-COVID patients presenting subjective cognitive deficit.

Safety, Tolerability, Pharmacokinetics and Initial Pharmacodynamics of a Subcommissural Organ-Spondin-Derived Peptide: A Randomized, Placebo-Controlled, Double-Blind, Single Ascending Dose First-in-Human Study

INTRODUCTION

This randomized, double-blind, placebo-controlled study in healthy volunteers assessed the safety, tolerability, and pharmacokinetics of single ascending doses of intravenously administered NX210-a linear peptide derived from subcommissural organ-spondin-and explored the effects on blood/urine biomarkers and cerebral activity.

METHODS

Participants in five cohorts (n = 8 each) were randomized to receive a single intravenous dose of NX210 (n = 6 each) (0.4, 1.25, 2.5, 5, and 10 mg/kg) or placebo (n = 2 each); in total, 10 and 29 participants received placebo and NX210, respectively. Blood samples were collected for pharmacokinetics within 180 min post dosing. Plasma and urine were collected from participants (cohorts: 2.5, 5, and 10 mg/kg) for biomarker analysis and electroencephalography (EEG) recordings within 48 h post dosing. Safety/tolerability and pharmacokinetic data were assessed before ascending to the next dose.

RESULTS

The study included 39 participants. All dosages were safe and well tolerated. All treatment-emergent adverse events (n = 17) were of mild severity and resolved spontaneously (except one with unknown outcome). Twelve treatment-emergent adverse events (70.6%) were deemed drug related; seven of those (58.3%) concerned nervous system disorders (dizziness, headache, and somnolence). The pharmacokinetic analysis indicated a short half-life in plasma (6-20 min), high apparent volume of distribution (1870-4120 L), and rapid clearance (7440-16,400 L/h). In plasma, tryptophan and homocysteine showed dose-related increase and decrease, respectively. No drug dose effect was found for the glutamate or glutamine plasma biomarkers. Nevertheless, decreased blood glutamate and increased glutamine were observed in participants treated with NX210 versus placebo. EEG showed a statistically significant decrease in beta and gamma bands and a dose-dependent increasing trend in alpha bands. Pharmacodynamics effects were sustained for several hours (plasma) or 48 h (urine and EEG).

CONCLUSION

NX210 is safe and well tolerated and may exert beneficial effects on the central nervous system, particularly in terms of cognitive processing.