Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis

The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time-frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via t-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time-Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research.

Electroencephalogram criticality in cognitive impairment: a monitoring biomarker?

Critical states present scale-free dynamics, optimizing neuronal complexity and serving as a potential biomarker in cognitively impaired patients. We explored electroencephalogram (EEG) criticality in amnesic Mild Cognitive Impairment patients with clinical improvement in working memory, verbal memory, verbal fluency and overall executive functions after the completion of a 6-month prospective memory training. We compared "before" and "after" stationary resting-state EEG records of right-handed MCI patients (n = 17; 11 females), using the method of critical fluctuations and Haar wavelet analysis. Improvement of criticality indices was observed in most electrodes, with mean values being higher after prospective memory training. Significant criticality enhancement was found in the subgroup analysis of frontotemporal electrodes [mean dif: 0.10; Z = 7, p = 0.019]. In the isolated electrode signal analysis, significant post-intervention improvement was noted in pooled criticality indices of electrodes T6 [mean dif: 0.204; t(10) = -2.3, p = 0.044] and F4 [mean dif: 0.0194; t(10) = -2.82; p = 0.018]. EEG criticality agreed with clinical improvement, consisting a possible quantifiable and easy-to-obtain biomarker in MCI and Alzheimer's disease (AD), especially in patients under cognitive training/rehabilitation. We highlight the role of EEG in prognostication, monitoring and potentially early treatment optimization in MCI or AD patients. Further standardization of the methodology in larger patient cohorts could be valuable for AD theragnostics in patients receiving disease-modifying treatments by providing insights regarding synaptic brain plasticity.

Graphical Abstract

Critical states' scale-free dynamics optimize neuronal complexity, emerging as biomarkers in cognitive neuroscience. Applying the method of critical fluctuations and Haar wavelet analysis in stationary EEG time-series, we demonstrate criticality enhancement in the frontotemporal electroencephalographic (EEG) recordings of mild cognitive impairment (MCI) patients after a 6-month prospective memory training, suggesting EEG criticality as a possible monitoring biomarker in MCI and Alzheimer's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10155-4.

Recognition of mild cognitive impairment in older adults using a polynomial regression model based on prefrontal cortex hemoglobin oxygenation

AIM

This study employed a three-minute game-based intelligence test (GBIT) to create a hemoglobin polynomial regression model for early identification of mild cognitive impairment (MCI) in older adults.

METHODS

210 older adult participants were recruited from community centers in the central region of Taichung City. Working memory (WM) performance in older adults was assessed during GBIT, while hemoglobin responses were measured by near-infrared spectroscopy (NIRS). Variables included oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb). Data sequences underwent a fitting procedure using a transformed cubic polynomial function. The transformed coefficients were used as predictors of a logistic regression model to recognize MCI in older adults.

RESULTS

This study confirmed the relationship between age and cognitive performance. The findings demonstrate that the NIRS cubic polynomial function trends during the GBIT test showed significant changes in older adults, increasing with age. Logistic regression analysis identified age and the orientation (coefficient a) of HHb as the main factors for recognizing MCI. The model achieved an overall precision of 83.33 % (sensitivity = 75.00 %; specificity = 84.68 %) with the formula: ln (Odds [MCI]) = -1.64 + 0.57 × HHb_a + 1.40 × age.

CONCLUSIONS

NIRS hemoglobin response characteristics during GBIT may serve as an efficient indicator of MCI in older adults. These findings may advance the field of cognitive health evaluation, resulting in earlier detection of cognitive deterioration in older adults.

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging

OBJECTIVE

To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

METHODS

Twenty-two cognitively normal preclinical AD participants with elevated amyloid and 21 cognitively normal controls without elevated amyloid completed n-back working memory tasks (n = 0, 1, 2). The absolute and relative power of ERO was extracted using the discrete wavelet transform in the delta, theta, alpha, and beta bands. Four machine learning methods were employed, and classification performance was assessed using three metrics.

RESULTS

The low-frequency bands produced higher discriminative performances compared to high-frequency bands. The 2-back task yielded the best classification capability among the three tasks. The highest area under the curve value (0.86) was achieved in the 2-back delta band nontarget condition data. The highest accuracy (80.47%) was obtained in the 2-back delta and theta bands nontarget data. The highest F1 score (0.82) was in the 2-back theta band nontarget data. The support vector machine achieved the highest performance among tested classifiers.

CONCLUSION

This study demonstrates the promise of using machine learning on EEG ERO from working memory tasks to detect preclinical AD.

SIGNIFICANCE

EEG ERO may reveal pathophysiological differences in the earliest stage of AD when no cognitive impairments are apparent.

Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Digital assessment of cognitive-affective biases related to mental health

With an increasing societal need for digital therapy solutions for poor mental health, we face a corresponding rise in demand for scientifically validated digital contents. In this study we aimed to lay a sound scientific foundation for the development of brain-based digital therapeutics to assess and monitor cognitive effects of social and emotional bias across diverse populations and age-ranges. First, we developed three computerized cognitive tasks using animated graphics: 1) an emotional flanker task designed to test attentional bias, 2) an emotional go-no-go task to measure bias in memory and executive function, and 3) an emotional social evaluation task to measure sensitivity to social judgments. Then, we confirmed the generalizability of our results in a wide range of samples (children (N = 50), young adults (N = 172), older adults (N = 39), online young adults (N=93), and depression patients (N = 41)) using touchscreen and online computer-based tasks, and devised a spontaneous thought generation task that was strongly associated with, and therefore could potentially serve as an alternative to, self-report scales. Using PCA, we extracted five components that represented different aspects of cognitive-affective function (emotional bias, emotional sensitivity, general accuracy, and general/social attention). Next, a gamified version of the above tasks was developed to test the feasibility of digital cognitive training over a 2-week period. A pilot training study utilizing this application showed decreases in emotional bias in the training group (that were not observed in the control group), which was correlated with a reduction in anxiety symptoms. Using a 2-channel wearable EEG system, we found that frontal alpha and gamma power were associated with both emotional bias and its reduction across the 2-week training period.

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Background

Electroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.

Methods

Related publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.

Results

Between 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.

Conclusion

The application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives

Introduction

Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.

Methods

To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.

Results

Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.

Discussion

All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.