Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

The effect of diet on the development of EEG microstates in healthy infant throughout the first year of life

Electroencephalography (EEG) is used to directly measure neuronal activity and evaluate network dynamics with an excellent temporal resolution. These network dynamics in the form of EEG microstates - distinct yet transiently stable topographies captured at peaks of the global field power - are increasingly used as markers of disease, neurodegeneration, and neurodevelopment. However, few studies have evaluated EEG microstates throughout the first year of life, and currently none have examined the potential effects of infant diet. The current study seeks to investigate whether different diets impact EEG microstates throughout the first year of life. EEGs were collected from approximately 500 healthy infants who were fed a human milk, diary-, or soy-based formula at three, six, nine, and twelve months of age. Microstate classes and temporal characteristics were then calculated for each timepoint and diet. Microstates classes showed a clear developmental trajectory, with duration decreasing with age, and coverage, globally explained variance, and occurrence generally increasing with age. There were relatively few significant differences between infants fed different diets, indicating that diet potentially effects functional neurodevelopment more subtly than previously indicated in the literature. This study adds to the growing body of literature demonstrating that formula feeding does not have clear disadvantages in terms of infant functional neuronal development.

Self-related thought alterations associated with intrinsic brain dysfunction in mild cognitive impairment

The subjective experience of self-awareness is attributed to the human capacity for introspective thought during periods of mind-wandering. However, how this cognitive function is impacted in individuals with mild cognitive impairment (MCI) still needs to be better understood. To address this gap, we investigated alterations in self-referential thinking in a cohort of 30 MCI patients, comparing them to 60 healthy old-aged and 60 healthy younger controls. MCI patients exhibited a notable decline in overall cognitive function, as evidenced by significantly lower scores on the Montreal Cognitive Assessment (MoCA), with particular deficits in Memory subscore and Memory Index Score (MIS). Employing the Amsterdam Resting-State Questionnaire (ARSQ) to assess mind-wandering, we observed diminished self-related thoughts relating to personal past experiences and future thinking among MCI patients. Notably, using high-density electroencephalography (hdEEG) microstate analysis, we detected reduced neural activity for microstate C associated with self-related thoughts in MCI patients and healthy older relative to healthy younger controls, and an increase in neural activity for microstate A in MCI patients compared to healthy older and younger controls. This aberrant temporal activity was localized within brain regions implicated in episodic autobiographical memory and the default mode network. Our results highlight a link between impaired mind-wandering ability and dysfunction within the intrinsic neural networks of MCI patients, underscoring its implications for disruptions in the sense of self within this clinical population.

Electroencephalographic Biomarkers for Neuropsychiatric Diseases: The State of the Art

Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and mild cognitive impairment patients

Background. Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition.Methods. We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen participants (6 Stroke, 7 MCI, and 2 non-patients). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.Results. The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.Significance. The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.Conclusions. The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.

Eyes-open and eyes-closed EEG of older adults with subjective cognitive impairment versus healthy controls: A frequency principal components analysis study

Subjective Cognitive Impairment (SCI) is a self-perceived worsening of cognitive decline, carrying an increased risk of developing Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Due to the self-report nature of SCI, an understanding of the biological mechanisms that contribute to an increased dementia risk is needed. This study aims to assess the differences in resting state electroencephalography (EEG) (eyes-open, eyes-closed; EO, EC) between older adults with SCI and healthy controls (HCs) utilising frequency principal components analysis (fPCA), a novel data driven approach. Participants (n = 14 per group: SCI, HCs) were matched on age, sex, years of education, mood, cognition, and pre-morbid function. Continuous resting EEG was recorded during 2-minute conditions (EO, EC) and were submitted to 4 separate fPCAs (each condition, group). Corresponding components were assessed between groups and conditions, correlated with demographics, mood, and cognition variables; multivariate logistic regression was also carried out. Component amplitudes were larger in HCs for delta-theta and alpha-beta, while theta-alpha was larger for SCI. DASS anxiety scores contributed to higher amplitudes for HCs in EO delta-theta and alpha-beta, while male sex and depressive symptoms contributed to higher amplitudes for the SCI group in EO and EC theta-alpha. Findings demonstrate a distinct divergent signature of neurological activity in older people with SCI, despite normal objective cognitive function. This is the first fPCA study to investigate neuronal differences between HCs and older adults with SCI at rest. Novel confounders and effect modifiers were identified that should be controlled in future studies.

Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task

Subjective cognitive decline (SCD), mild cognitive impairment (MCI), and Alzheimer's disease stages lack well-defined electrophysiological correlates, creating a critical gap in the identification of robust biomarkers for early diagnosis and intervention. In this study, we analysed event-related potentials (ERPs) recorded during a sustained visual attention task in a cohort of 178 individuals (119 SCD, 40 MCI, and 19 healthy subjects, HS) to investigate sensory and cognitive processing alterations associated with these conditions. SCD patients exhibited significant attenuation in both sensory (P1, N1, P2) and cognitive (P300, P600, P900) components compared to HS, with cognitive components showing performance-related gains. In contrast, MCI patients did not show a further decrease in any ERP component compared to SCD. Instead, they exhibited compensatory enhancements, reversing the downward trend observed in SCD. This compensation resulted in a non-monotonic pattern of ERP alterations across clinical conditions, suggesting that MCI patients engage neural mechanisms to counterbalance sensory and cognitive deficits. These findings support the use of electrophysiological markers in support of medical decision-making, enhancing personalized prognosis and guiding targeted interventions in cognitive decline.

An EEG-based framework for automated discrimination of conversion to Alzheimer's disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study

Background

As a clinical precursor to Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) bears a considerably heightened risk of transitioning to AD compared to cognitively normal elders. Early prediction of whether aMCI will progress to AD is of paramount importance, as it can provide pivotal guidance for subsequent clinical interventions in an early and effective manner.

Methods

A total of 107 aMCI cases were enrolled and their electroencephalogram (EEG) data were collected at the time of the initial diagnosis. During 18-month follow-up period, 42 individuals progressed to AD (PMCI), while 65 remained in the aMCI stage (SMCI). Spectral, nonlinear, and functional connectivity features were extracted from the EEG data, subjected to feature selection and dimensionality reduction, and then fed into various machine learning classifiers for discrimination. The performance of each model was assessed using 10-fold cross-validation and evaluated in terms of accuracy (ACC), area under the curve (AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and F1-score.

Results

Compared to SMCI patients, PMCI patients exhibit a trend of "high to low" frequency shift, decreased complexity, and a disconnection phenomenon in EEG signals. An epoch-based classification procedure, utilizing the extracted EEG features and k-nearest neighbor (KNN) classifier, achieved the ACC of 99.96%, AUC of 99.97%, SEN of 99.98%, SPE of 99.95%, PPV of 99.93%, and F1-score of 99.96%. Meanwhile, the subject-based classification procedure also demonstrated commendable performance, achieving an ACC of 78.37%, an AUC of 83.89%, SEN of 77.68%, SPE of 76.24%, PPV of 82.55%, and F1-score of 78.47%.

Conclusion

Aiming to explore the EEG biomarkers with predictive value for AD in the early stages of aMCI, the proposed discriminant framework provided robust longitudinal evidence for the trajectory of the aMCI cases, aiding in the achievement of early diagnosis and proactive intervention.

Abnormalities of resting-state EEG microstates in older adults with cognitive frailty

This study aims to analyze the characteristics of EEG microstates across different cognitive frailty (CF) subtypes, providing insights for the prevention and early diagnosis of CF. This study included 60 eligible older adults. Their resting-state EEG microstates were analyzed using agglomerative adaptive hierarchical clustering. Microstate temporal parameters were extracted through global field power peak-based backfitting. Spearman's partial correlation analysis and linear mixed-effects models were employed to investigate the relationship between microstate temporal parameters and CF. Statistical differences were observed in transition probabilities (TPs) from microstate B to A between healthy controls (HCs) and reversible cognitive frailty (RCF) group (t = -2.076, P = 0.042). Potentially reversible cognitive frailty (PRCF) and RCF group also exhibited statistical differences in the TPs from microstate B to A (t = 3.122, P = 0.003). In the RCF group, the occurrence of microstates A and B differed significantly from microstate C (tAC = 3.455, PAC = 0.002; tBC = 3.108, PBC = 0.004). In the PRCF group, the occurrence of microstates A, B, and C differed significantly from microstate D (tAD = -3.688, PAD = 0.001; tBD = -3.334, PBD = 0.002; tCD = -4.188, PCD < 0.001). The neural networks and processing modes engaged by microstate D during executive memory tasks differ between RCF and PRCF. A decreased occurrence of microstate C and higher TPs of microstates A and B may serve as early warning signals for RCF. Conversely, an increased occurrence of microstate D and decreased TPs of microstates C and D indicate the onset of PRCF.

Abnormal nonlinear features of EEG microstate sequence in obsessive-compulsive disorder

BACKGROUND

At present, only a few studies have explored electroencephalography (EEG) microstates of patients with obsessive-compulsive disorder (OCD) and the results are inconsistent. Additionally, the nonlinear features of EEG microstate sequences contain rich information about the brain, yet how the nonlinear features of EEG microstate sequences abnormally change in patients with OCD is still unknown.

METHODS

Resting-state EEG data were collected from 48 OCD patients and macheted 48 healthy controls (HC). Subsequently, EEG microstate analysis was used to extract the microstate temporal parameters (duration, occurrence, coverage) and nonlinear features of EEG microstate sequences (sample entropy, Lempel-Ziv complexity, Hurst index). Finally, the temporal parameters and nonlinear features of EEG microstate sequences were sent to three kinds of machine learning models to classify OCD patients.

RESULTS

Both groups obtained four typical EEG microstate topographies. The duration of microstates A, B, and C in OCD patients decreased significantly, while the occurrence of microstate D increased significantly compared to HC. Sample entropy and Lempel-Ziv complexity of microstate sequences in OCD patients increased significantly, while Hurst index decreased significantly compared to HC. The classification accuracy using the nonlinear features of microstate sequences reached up to 85%, significantly higher than that based on microstate temporal parameter models.

CONCLUSION

This study provides supplementary findings on EEG microstates in OCD patients with a larger sample size. We found that the nonlinear features of EEG microstate sequences in OCD patients can serve as potential electrophysiological biomarkers for distinguishing OCD patients.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia

The early detection of cognitive decline in older adults is crucial for preventing dementia. This mini-review focuses on electroencephalography (EEG) markers of early dementia-related precursors, including subjective cognitive decline, subjective memory complaints, and cognitive frailty. We present recent findings from EEG analyses identifying high dementia risk in older adults, with an emphasis on conditions that precede mild cognitive impairment. We also cover event-related potentials, quantitative EEG markers, microstate analysis, and functional connectivity approaches. Moreover, we discuss the potential of these neurophysiological markers for the early detection of cognitive decline as well as their correlations with related biomarkers. The integration of EEG data with advanced artificial intelligence technologies also shows promise for predicting the trajectory of cognitive decline in neurodegenerative disorders. Although challenges remain in its standardization and clinical application, EEG-based approaches offer non-invasive, cost-effective methods for identifying individuals at risk of dementia, which may enable earlier interventions and personalized treatment strategies.

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer's disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

Difference between subjective and objective cognitive decline confirmed by power spectral density

INTRODUCTION

The study aims to use power spectrum changes in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), preclinical stages of Alzheimer's disease (AD), for future biomarker studies in early AD diagnosis.

METHODS

We recruited 23 SCD and 32 aMCI subjects and conducted comparative analysis using relative power spectral density (PSD). Automated preprocessing and statistical analysis were performed using iSync Brain® (iMediSync Inc., Republic of Korea) (https://isyncbrain.com/).

RESULTS

Theta band power in the temporal region was 14.826 ± 7.2394 for the SCD group and 20.003 ± 10.1768 for the aMCI group. In the parietal region, theta band power was 13.614 ± 7.5689 for SCD and 19.894 ± 11.1387 for aMCI. Beta1 band power in the frontal region was 6.639 ± 2.2904 for SCD and 5.465 ± 1.8907 for aMCI, and in the temporal region it was 7.359 ± 2.5619 for SCD and 5.921 ± 2.1605 for aMCI.

CONCLUSION

PSD analysis of resting-state EEG predicted SCD, a preclinical stage of AD. This cross-sectional study observed electrical-physiological characteristics of preclinical AD; however, follow-up studies are needed to evaluate predictive value for future cognitive decline.

EEG microstates in epilepsy with and without cognitive dysfunction: Alteration in intrinsic brain activity

OBJECTIVE

This study aims to investigate the difference between epilepsy comorbid with and without cognitive dysfunction.

METHOD

Participants were classified into patients with epilepsy comorbid cognitive dysfunction (PCCD) and patients with epilepsy without comorbid cognitive dysfunction (nPCCD). Microstate analysis was applied based on 20-channel electroencephalography (EEG) to detect the dynamic changes in the whole brain. The coverage, occurrence per second, duration, and transition probability were calculated.

RESULT

The occurrence per second and the coverage of microstate B in the PCCD group were higher than that of the nPCCD group. Coverage in microstate D was lower in the PCCD group than in the nPCCD group. In addition, the PCCD group has a higher probability of A to B and B to A transitions and a lower probability of A to D and D to A transitions.

CONCLUSION

Our research scrutinizes the disparities observed within EEG microstates among epilepsy patients both with and without comorbid cognitive dysfunction.

SIGNIFICANCE

EEG microstate analysis offers a novel metric for assessing neuropsychiatric disorders and supplies evidence for investigating the mechanisms and the dynamic change of epilepsy comorbid cognitive dysfunction.

The relationship between healthy lifestyles and cognitive function in Chinese older adults: the mediating effect of depressive symptoms

BACKGROUND

Previous studies have proven the positive relationship between healthy lifestyles and cognitive function in older adults. However, the specific impacts and mechanisms require further investigation. Therefore, this study aimed to investigate whether healthy lifestyles and cognitive function were associated with Chinese older adults and whether depressive symptoms mediated their association.

METHODS

8272 valid samples were included using the latest data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Pearson's test was applied to investigate the relationship between the key variables. Regression models were employed to examine the mediating effects of healthy lifestyles, using Sobel's test and the bootstrap method to confirm path effects.

RESULTS

There was a significant correlation between healthy lifestyles, depressive symptoms, and cognitive function (p < 0.01). Healthy lifestyles directly impact cognitive function (β = 0.162, p < 0.01). Healthy lifestyles had a significant effect on depressive symptoms (β=-0.301, p < 0.01), while depressive symptoms have a significant impact on cognitive function (β=-0.108, p < 0.01). Depressive symptoms partially mediated the effect of healthy lifestyles on cognitive function (β = 0.032, p < 0.01). The Sobel and bootstrap tests confirmed the robustness of the regression analysis results.

CONCLUSION

Depressive symptoms mediate the relationship between healthy lifestyles and cognitive function. Our findings suggest that prevention strategies for cognitive impairment in older adults should focus on healthy lifestyles and mental health.

Classification of cognitive impairment using electroencephalography for clinical inspection

Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

BACKGROUND

Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed.

AIM

We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol.

DESIGN

Prospective cohort study.

SETTING

Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy.

POPULATION

Sub-acute and chronic stroke survivors.

METHODS

Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery.

RESULTS

Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2.

CONCLUSIONS

The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns.

CLINICAL REHABILITATION IMPACT

This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

INTRODUCTION

Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern.

METHODS

We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings.

RESULTS

Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50).

DISCUSSION

Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings.

Highlights

Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

Recent Research Progress in Fluorescent Probes for Detection of Amyloid-β In Vivo

Alzheimer's disease (AD) is a neurodegenerative disease. Due to its complex pathological mechanism, its etiology is not yet clear. As one of the main pathological markers of AD, amyloid-β (Aβ) plays an important role in the development of AD. The deposition of Aβ is not only related to the degeneration of neurons, but also can activate a series of pathological events, including the activation of astrocytes and microglia, the breakdown of the blood-brain barrier, and the change in microcirculation, which is the main cause of brain lesions and death in AD patients. Therefore, the development of efficient and reliable Aβ-specific probes is crucial for the early diagnosis and treatment of AD. This paper focuses on reviewing the application of small-molecule fluorescent probes in Aβ imaging in vivo in recent years. These probes efficiently map the presence of Aβ in vivo, providing a pathway for the early diagnosis of AD and providing enlightenment for the design of Aβ-specific probes in the future.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol

BACKGROUND

As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.

METHODS

We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.

DISCUSSION

This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.

TRIAL REGISTRATION NUMBER (TRN)

NCT05569083.