Resting-state electroencephalographic biomarkers of Alzheimer's disease

EEG-based graph network analysis in relation to regional tau in asymptomatic Alzheimer's disease

Tau aggregation in early affected regions in the asymptomatic stage of Alzheimer's disease marks a transitional phase between stable asymptomatic amyloid positivity and the clinically manifest stage. How this early region tau aggregation covertly affects brain function during this asymptomatic stage remains unclear. In this study, 83 participants underwent a 128 electrodes resting-state EEG, a dynamic 100 min tau PET scan (18F-MK6240), an amyloid PET scan, a structural T1 MRI scan and neuropsychological assessment. Tau PET data quality control led to a final sample of 66 subjects. Based on the clinical and cognitive status, amyloid and tau PET biomarkers, the group was composed of 37 cognitively unimpaired amyloid negative subjects, 14 cognitively unimpaired amyloid positive subjects and 15 patients with prodromal Alzheimer's disease. We calculated the average undirected weighted Phase Lag Index in the alpha frequency band with eyes closed and used this as weights for the graph and analysed the global clustering coefficient and characteristic path length in sensor space. As a primary objective, we assessed how these global graph measures correlated with tau PET values, in an a priori defined early metaVOI, comprised of the entorhinal and perirhinal cortex, hippocampus, parahippocampus and fusiform cortex. As secondary analyses, we investigated which specific brain regions were mainly implicated, what the contribution was of amyloid, the effect of electrode density and the relation to cognitive performance. In the overall group and within the cognitively unimpaired amyloid positive subgroup, tau aggregation was associated with a decrease in global clustering coefficient and an increase in characteristic path length. These changes reflect the initial disintegration of the small-world brain network during the transitional phase, even before clinical symptoms are apparent. The correlations are most prominent in the perirhinal cortex, indicating that global deterioration of the network is already present early in the Alzheimer's disease pathology. We obtained similar results with only taking 64 electrodes into account. To conclude, we found that in the asymptomatic stage of Alzheimer's disease, tau PET load in medial temporal cortex is associated with global electrophysiological measures of network disintegration. The study demonstrates the potential value of high-density EEG in the era of biologically defined Alzheimer's disease for characterizing brain function in the asymptomatic stage.

Exploration of working memory retrieval stage for mild cognitive impairment: time-varying causality analysis of electroencephalogram based on dynamic brain networks

BACKGROUND

Mild Cognitive Impairment (MCI) is an intermediate stage between the expected cognitive decline of normal aging and Alzheimer's disease (AD). Its management is crucial for it helps intervene and slow the progression of cognitive decline to AD. However, the understanding of the MCI mechanism is not completely clear. As working memory (WM) damage is a common symptom of MCI, this study focused on the core stage of WM, i.e., the memory retrieval stage, to investigate information processing and the causality relationships among brain regions based on electroencephalogram (EEG) signals.

METHOD

21 MCI and 20 normal cognitive control (NC) participants were recruited. The delayed matching sample paradigm with two different loads was employed to evaluate their WM functions. A time-varying network based on the Adaptive transfer function (ADTF) was constructed on the EEG of the memory retrieval trials.to perform the dynamic brain network analysis.

RESULTS

Our results showed that: (a) Behavioral data analysis: there were significant differences in accuracy and accuracy / reaction time between MCI and NC in tasks with memory load capacity of low load-four and high load-six, especially in tasks with memory load capacity of four. (b) Dynamic brain network analysis: there were significant differences in the dynamic changes of brain network patterns between the two groups during the memory retrieval stage of the WM task. Specifically, in low load WM tasks, the dynamic brain network changes of NC were more regular to accommodate for efficient information processing, with important core nodes showing a transition from bottom to up, while MCI did not display a regular dynamic brain network pattern. Further, the brain functional areas associated with low load WM disorders were mainly located in the left prefrontal lobe (FC1) and right occipital lobe (PO8). Compared with low load WM task, during the high load WM task, the dynamic brain network changes of NC during the memory retrieval stage were regular, and the core nodes exhibited a consistent transition phenomenon from up to bottom to up, which were not observed in MCI.

CONCLUSIONS

Behavioral data in the low load WM task paradigm and abnormal electrophysiological signals in the left prefrontal (FC1) and right occipital lobes (PO8) could be used for MCI diagnosis. This is the first time based on large-scale dynamic network methods to investigate the dynamic network patterns of MCI memory retrieval stages under different load WM tasks, providing a new perspective on the neural mechanisms of WM deficits in MCI patients and providing some reference for the clinical intervention treatment of MCI-WM memory disorders.

Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review

Background

Alzheimer's disease (AD) might be best conceptualized as a disconnection syndrome, such that symptoms may be largely attributable to disrupted communication between brain regions, rather than to deterioration within discrete systems. EEG is uniquely capable of directly and non-invasively measuring neural activity with precise temporal resolution; connectivity quantifies the relationships between such signals in different brain regions. EEG research on connectivity in AD and mild cognitive impairment (MCI), often considered a prodromal phase of AD, has produced mixed results and has yet to be synthesized for comprehensive review. Thus, we performed a systematic review of EEG connectivity in MCI and AD participants compared with cognitively healthy older adult controls.

Methods

We searched PsycINFO, PubMed, and Web of Science for peer-reviewed studies in English on EEG, connectivity, and MCI/AD relative to controls. Of 1,344 initial matches, 124 articles were ultimately included in the systematic review.

Results

The included studies primarily analyzed coherence, phase-locked, and graph theory metrics. The influence of factors such as demographics, design, and approach was integrated and discussed. An overarching pattern emerged of lower connectivity in both MCI and AD compared to healthy controls, which was most prominent in the alpha band, and most consistent in AD. In the minority of studies reporting greater connectivity, theta band was most commonly implicated in both AD and MCI, followed by alpha. The overall prevalence of alpha effects may indicate its potential to provide insight into nuanced changes associated with AD-related networks, with the caveat that most studies were during the resting state where alpha is the dominant frequency. When greater connectivity was reported in MCI, it was primarily during task engagement, suggesting compensatory resources may be employed. In AD, greater connectivity was most common during rest, suggesting compensatory resources during task engagement may already be exhausted.

Conclusion

The review highlighted EEG connectivity as a powerful tool to advance understanding of AD-related changes in brain communication. We address the need for including demographic and methodological details, using source space connectivity, and extending this work to cognitively healthy older adults with AD risk toward advancing early AD detection and intervention.

Relationship between regional relative theta power and amyloid deposition in mild cognitive impairment: an exploratory study

Introduction

Electroencephalographic (EEG) abnormalities, such as increased theta power, have been proposed as biomarkers for neurocognitive disorders. Advancements in amyloid positron emission tomography (PET) imaging have enhanced our understanding of the pathology of neurocognitive disorders, such as amyloid deposition. However, much remains unknown regarding the relationship between regional amyloid deposition and EEG abnormalities. This study aimed to explore the relationship between regional EEG abnormalities and amyloid deposition in patients with mild cognitive impairment (MCI).

Methods

We recruited 24 older adults with MCI from a community center for dementia prevention, and 21 participants were included in the final analysis. EEG was recorded using a 64-channel system, and amyloid deposition was measured using amyloid PET imaging. Magnetic resonance imaging (MRI) data were used to create individualized brain models for EEG source localization. Correlations between relative theta power and standardized uptake value ratios (SUVRs) in 12 brain regions were analyzed using Spearman's correlation coefficient.

Results

Significant positive correlations between relative theta power and SUVR values were found in several brain regions in the individualized brain model during the resting eyes-closed condition, including right temporal lobe (r = 0.581, p = 0.006), left hippocampus (r = 0.438, p = 0.047), left parietal lobe (r = 0.471, p = 0.031), right parietal lobe (r = 0.509, p = 0.018), left occipital lobe (r = 0.597, p = 0.004), and right occipital lobe (r = 0.590, p = 0.005). During the visual working memory condition, significant correlations were found in both cingulate lobes (left: r = 0.483, p = 0.027; right: r = 0.449, p = 0.041), left parietal lobe (r = 0.530, p = 0.010), right parietal lobe (r = 0.606, p = 0.004), left occipital lobe (r = 0.648, p = 0.001), and right occipital lobe (r = 0.657, p = 0.001).

Conclusion

The result suggests that regional increases in relative theta power are associated with regional amyloid deposition in patients with MCI. These findings highlight the potential of EEG in detecting amyloid deposition. Future large-scale studies are needed to validate these preliminary findings and explore their clinical applications.

Working memory deficit in patients with focal epilepsy is associated with higher interictal theta connectivity

OBJECTIVE

Interictal cognitive disturbances are frequent in patients with focal epilepsies and the links with alteration of resting state brain oscillations are not well known. Changes in theta oscillations, may contribute to cognitive impairment. This study aimed to investigate whether changes in theta activity are related to cognitive disturbances.

METHODS

Retrospective data of 23 patients with temporal/frontal lobe epilepsy were included. Theta connectivity, power and interictal spikes rate from five-minute interictal resting state stereoelectroencephalography datasets were computed. Cognitive performances were assessed by Wechsler Intelligence Scale (WAIS-IV) and Weschler Memory Scale (WMS-III). Linear regression was performed to evaluate effect of interictal activity and seizure related parameters on cognitive scores.

RESULTS

WAIS-IV working memory score in patients with epilepsy showed negative correlation with frontotemporal theta connectivity (F(1,17) = 5,239, p = 0,036, R2 = 0,200, β = -0,497). Moreover, theta connectivity was correlated with mesial temporal spike rate and theta power (F(2,17) = 10,967, p = 0,001, adj.R2 = 0,540).

CONCLUSIONS

Patients with focal epilepsy often encounter compromised cognitive functions, particularly notable in the domain of working memory. This impairment might be attributed to physiological mechanisms involving increased theta connectivity within the frontotemporal regions and interictal spiking.

SIGNIFICANCE

Our study highlights the relation between theta connectivity and working memory impairments in patients with focal epilepsy.

Resting-State EEG Alpha Rhythms Are Related to CSF Tau Biomarkers in Prodromal Alzheimer's Disease

Patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show abnormally high delta (<4 Hz) and low alpha (8-12 Hz) rhythms measured from resting-state eyes-closed electroencephalographic (rsEEG) activity. Here, we hypothesized that the abnormalities in rsEEG activity may be greater in ADMCI patients than in those with MCI not due to AD (noADMCI). Furthermore, they may be associated with the diagnostic cerebrospinal fluid (CSF) amyloid-tau biomarkers in ADMCI patients. An international database provided clinical-demographic-rsEEG datasets for cognitively unimpaired older (Healthy; N = 45), ADMCI (N = 70), and noADMCI (N = 45) participants. The rsEEG rhythms spanned individual delta, theta, and alpha frequency bands. The eLORETA freeware estimated cortical rsEEG sources. Posterior rsEEG alpha source activities were reduced in the ADMCI group compared not only to the Healthy group but also to the noADMCI group (p < 0.001). Negative associations between the CSF phospho-tau and total tau levels and posterior rsEEG alpha source activities were observed in the ADMCI group (p < 0.001), whereas those with CSF amyloid beta 42 levels were marginal. These results suggest that neurophysiological brain neural oscillatory synchronization mechanisms regulating cortical arousal and vigilance through rsEEG alpha rhythms are mainly affected by brain tauopathy in ADMCI patients.

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes

OBJECTIVES

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users.

DESIGN

Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes.

RESULTS

Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation.

CONCLUSIONS

This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Application status and prospects of multimodal EEG-fMRI in HIV-associated neurocognitive disorders

HIV-associated neurocognitive disorders (HAND) are one of the common complications in people living with HIV (PLWH), which can affect their attention, working memory, and other related cognitive functions. With the widespread use of combination antiretroviral therapy (cART), the incidence of HAND has declined. However, HAND is still an important complication of HIV, which not only affects the quality of life of patients but also affects their adherence to HIV treatment. Its diagnosis mainly relies on neurocognitive tests, which have a certain degree of subjectivity, making it difficult to diagnose and classify HAND accurately, and there is an urgent need to explore more sensitive biomarkers. Multimodal brain imaging has seen a surge in recent years with simultaneous EEG-fMRI being at the forefront of cognitive multimodal neuroimaging. It is a complementary fusion technique that effectively combines the high spatial resolution of fMRI with the high temporal resolution of EEG, compensating for the shortcomings of a single technique and providing a new method for studying cognitive function. It is expected to reveal the underlying mechanisms of HAND and provide high spatiotemporal warning biomarkers of HAND, which will provide a new perspective for the early diagnosis and treatment of HAND and contribute to the improvement of patient prognosis.

Exploring the Relationship Between CAIDE Dementia Risk and EEG Signal Activity in a Healthy Population

BACKGROUND

Accounting for dementia risk factors is essential in identifying people who would benefit most from intervention programs. The CAIDE dementia risk score is commonly applied, but its link to brain function remains unclear. This study aims to determine whether the variation in this score is associated with neurophysiological changes and cognitive measures in normative individuals.

METHODS

The sample comprised 38 participants aged between 54 and 79 (M = 67.05; SD = 6.02). Data were collected using paper-and-pencil tests and electroencephalogram (EEG) recordings in the resting state, channels FP1 and FP2. The EEG signals were analyzed using Power Spectral Density (PSD)-based features.

RESULTS

The CAIDE score is positively correlated with the relative power activation of the θ band and negatively correlated with the MMSE cognitive test score, and MMSE variations align with those found in distributions of EEG-extracted PSD-based features.

CONCLUSIONS

The findings suggest that CAIDE scores can identify individuals without noticeable cognitive changes who already exhibit brain activity similar to that seen in people with dementia. They also contribute to the convergent validity between CAIDE and the risk of cognitive decline. This underscores the importance of early monitoring of these factors to reduce the incidence of dementia.

Analysis of individual alpha frequency in a large cohort from a tertiary memory center

BACKGROUND AND PURPOSE

Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients.

METHODS

This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance.

RESULTS

IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38).

CONCLUSIONS

IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.

Altered EEG Theta and Alpha Band Functional Connectivity in Mild Cognitive Impairment During Working Memory Coding

Individuals with mild cognitive impairment (MCI), the preclinical stage of Alzheimer disease (AD), suffer decline in their visual working memory (WM) functions. Using large-scale network analysis of electroencephalography (EEG), the current study intended to investigate if there are differences in functional connectivity properties extracted during visual WM coding stages between MCI patients and normal controls (NC). A total of 21 MCI patients and 20 NC performed visual memory tasks of load four, while 32-channel EEG recordings were acquired. The functional connectivity properties were extracted from the acquired EEGs by the directed transform function (DTF) via spectral Granger causal analysis. Brain network analyses revealed distinctive brain network patterns between the two groups during the WM coding stage. Compared with the NC, MCI patients exhibited a reduced visual network connectivity of the frontal-temporal in θ (4-7Hz) band. A likely compensation mechanism was observed in MCI patients, with a strong brain functional connectivity of the frontal-occipital and parietal-occipital in both θ and α (8-13Hz) band. Further analyses of the network core node properties based on the differential brain network showed that, in θ band, there was a significant difference in the out-degree of the frontal lobe and parietal lobe between the two groups, while in α band, such difference was located only in the parietal lobe. The current study found that, in MCI patients, dysconnectivity is found from the prefrontal lobe to bilateral temporal lobes, leading to increased recruitment of functional connectivity in the frontal-occipital and parietal-occipital direction. The dysconnectivity pattern of MCI is more complex and primarily driven by core nodes Pz and Fz. These results significantly expanded previous knowledge of MCI patients' EEG dynamics during WM tasks and provide new insights into the underpinning neural mechanism MCI. It further provided a potential therapeutic target for clinical interventions of the condition.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Small World derived index to distinguish Alzheimer's type dementia and healthy subjects

BACKGROUND

This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns.

METHODS

Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones.

RESULTS

Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones.

CONCLUSIONS

These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.

Resting state electroencephalographic alpha rhythms are sensitive to Alzheimer's disease mild cognitive impairment progression at a 6-month follow-up

Are posterior resting-state electroencephalographic (rsEEG) alpha rhythms sensitive to the Alzheimer's disease mild cognitive impairment (ADMCI) progression at a 6-month follow-up? Clinical, cerebrospinal, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 Healthy old seniors (equivalent groups for demographic features) were available from an international archive (www.pdwaves.eu). The ADMCI patients were arbitrarily divided into two groups: REACTIVE and UNREACTIVE, based on the reduction (reactivity) in the posterior rsEEG alpha eLORETA source activities from the eyes-closed to eyes-open condition at ≥ -10% and -10%, respectively. 75% of the ADMCI patients were REACTIVE. Compared to the UNREACTIVE group, the REACTIVE group showed (1) less abnormal posterior rsEEG source activity during the eyes-closed condition and (2) a decrease in that activity at the 6-month follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD. Such a biomarker might reflect abnormalities in cortical arousal in quiet wakefulness to be used for clinical studies in ADMCI patients using 6-month follow-ups.

The association between posterior resting-state EEG alpha rhythms and functional MRI connectivity in older adults with subjective memory complaint

Resting-state eyes-closed electroencephalographic (rsEEG) alpha rhythms are dominant in posterior cortical areas in healthy adults and are abnormal in subjective memory complaint (SMC) persons with Alzheimer's disease amyloidosis. This exploratory study in 161 SMC participants tested the relationships between those rhythms and seed-based resting-state functional magnetic resonance imaging (rs-fMRI) connectivity between thalamus and visual cortical networks as a function of brain amyloid burden, revealed by positron emission tomography and cognitive reserve, measured by educational attainment. The SMC participants were divided into 4 groups according to 2 factors: Education (Edu+ and Edu-) and Amyloid burden (Amy+ and Amy-). There was a statistical interaction (p < 0.05) between the two factors, and the subgroup analysis using estimated marginal means showed a positive association between the mentioned rs-fMRI connectivity and the posterior rsEEG alpha rhythms in the SMC participants with low brain amyloidosis and high CR (Amy-/Edu+). These results suggest that in SMC persons, early Alzheimer's disease amyloidosis may contrast the beneficial effects of cognitive reserve on neurophysiological oscillatory mechanisms at alpha frequencies and connectivity between the thalamus and visual cortical networks.

Sex differences in the progression to Alzheimer's disease: a combination of functional and structural markers

Mild cognitive impairment (MCI) has been frequently interpreted as a transitional phase between healthy cognitive aging and dementia, particularly of the Alzheimer's disease (AD) type. Of note, few studies explored that transition from a multifactorial perspective, taking into consideration the effect of basic factors such as biological sex. In the present study 96 subjects with MCI (37 males and 59 females) were followed-up and divided into two subgroups according to their clinical outcome: "progressive" MCI (pMCI = 41), if they fulfilled the diagnostic criteria for AD at the end of follow-up; and "stable" MCI (sMCI = 55), if they remained with the initial diagnosis. Different markers were combined to characterize sex differences between groups, including magnetoencephalography recordings, cognitive performance, and brain volumes derived from magnetic resonance imaging. Results indicated that the pMCI group exhibited higher low-frequency activity, lower scores in neuropsychological tests and reduced brain volumes than the sMCI group, being these measures significantly correlated. When sex was considered, results revealed that this pattern was mainly due to the influence of the females' sample. Overall, females exhibited lower cognitive scores and reduced brain volumes. More interestingly, females in the pMCI group showed an increased theta activity that correlated with a more abrupt reduction of cognitive and volumetric scores as compared with females in the sMCI group and with males in the pMCI group. These findings suggest that females' brains might be more vulnerable to the effects of AD pathology, since regardless of age, they showed signs of more pronounced deterioration than males.

PEARL-Neuro Database: EEG, fMRI, health and lifestyle data of middle-aged people at risk of dementia

Interdisciplinary approaches are needed to understand the relationship between genetic factors and brain structure and function. Here we describe a database that includes genetic data on apolipoprotein E (APOE) and phosphatidylinositol binding clathrin assembly protein (PICALM) genes, both of which are known to increase the risk of late-onset Alzheimer's disease, paired with psychometric (memory, intelligence, mood, personality, stress coping strategies), basic demographic and health data on a cohort of 192 healthy middle-aged (50-63) individuals. Part of the database (~79 participants) also includes blood tests (blood counts, lipid profile, HSV virus) and functional neuroimaging data (EEG/fMRI) recorded with a resting-state protocol (eyes open and eyes closed) and two cognitive tasks (multi-source interference task, MSIT; and Sternberg's memory task). The data were validated and showed overall good quality. This open-science dataset is well suited not only for research relating to susceptibility to Alzheimer's disease but also for more general questions on brain aging or can be used as part of meta-analytical multi-disciplinary projects.

Cognitive and Neuropathophysiological Outcomes of Gamma-tACS in Dementia: A Systematic Review

Despite the numerous pharmacological interventions targeting dementia, no disease-modifying therapy is available, and the prognosis remains unfavorable. A promising perspective involves tackling high-frequency gamma-band (> 30 Hz) oscillations involved in hippocampal-mediated memory processes, which are impaired from the early stages of typical Alzheimer's Disease (AD). Particularly, the positive effects of gamma-band entrainment on mouse models of AD have prompted researchers to translate such findings into humans using transcranial alternating current stimulation (tACS), a methodology that allows the entrainment of endogenous cortical oscillations in a frequency-specific manner. This systematic review examines the state-of-the-art on the use of gamma-tACS in Mild Cognitive Impairment (MCI) and dementia patients to shed light on its feasibility, therapeutic impact, and clinical effectiveness. A systematic search from two databases yielded 499 records resulting in 10 included studies and a total of 273 patients. The results were arranged in single-session and multi-session protocols. Most of the studies demonstrated cognitive improvement following gamma-tACS, and some studies showed promising effects of gamma-tACS on neuropathological markers, suggesting the feasibility of gamma-tACS in these patients anyhow far from the strong evidence available for mouse models. Nonetheless, the small number of studies and their wide variability in terms of aims, parameters, and measures, make it difficult to draw firm conclusions. We discuss results and methodological limitations of the studies, proposing possible solutions and future avenues to improve research on the effects of gamma-tACS on dementia.

EEG Correlates in the 3 Variants of Primary Progressive Aphasia

BACKGROUND AND OBJECTIVES

The 3 clinical presentations of primary progressive aphasia (PPA) reflect heterogenous neuropathology, which is difficult to be recognized in vivo. Resting-state (RS) EEG is promising for the investigation of brain electrical substrates in neurodegenerative conditions. In this study, we aim to explore EEG cortical sources in the characterization of the 3 variants of PPA.

METHODS

This is a cross-sectional, single-center, memory center-based cohort study. Patients with PPA and healthy controls were consecutively recruited at the Neurology Unit, IRCCS San Raffaele Scientific Institute (Milan, Italy). Each participant underwent an RS 19-channel EEG. Using standardized low-resolution brain electromagnetic tomography, EEG current source densities were estimated at voxel level and compared among study groups. Using an RS functional MRI-driven model of source reconstruction, linear lagged connectivity (LLC) values within language and extra-language brain networks were obtained and analyzed among groups.

RESULTS

Eighteen patients with logopenic PPA variant (lvPPA; mean age = 72.7 ± 6.6; % female = 52.4), 21 patients with nonfluent/agrammatic PPA variant (nfvPPA; mean age = 71.7 ± 8.1; % female = 66.6), and 9 patients with semantic PPA variant (svPPA; mean age = 65.0 ± 6.9; % female = 44.4) were enrolled in the study, together with 21 matched healthy controls (mean age = 69.2 ± 6.5; % female = 57.1). Patients with lvPPA showed a higher delta density than healthy controls (p < 0.01) and patients with nfvPPA (p < 0.05) and svPPA (p < 0.05). Patients with lvPPA also displayed a greater theta density over the left posterior hemisphere (p < 0.01) and lower alpha2 values (p < 0.05) over the left frontotemporal regions than controls. Patients with nfvPPA showed a diffuse greater theta density than controls (p < 0.05). LLC was altered in all patients relative to controls (p < 0.05); the alteration was greater at slow frequency bands and within language networks than extra-language networks. Patients with lvPPA also showed greater LLC values at theta band than patients with nfvPPA (p < 0.05).

DISCUSSION

EEG findings in patients with PPA suggest that lvPPA-related pathology is associated with a characteristic disruption of the cortical electrical activity, which might help in the differential diagnosis from svPPA and nfvPPA. EEG connectivity was disrupted in all PPA variants, with distinct findings in disease-specific PPA groups.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that EEG analysis can distinguish PPA due to probable Alzheimer disease from PPA due to probable FTD from normal aging.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG

BACKGROUND

Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency.

METHODS

Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score.

RESULTS

The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%.

CONCLUSION

The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

A Pilot Electroencephalography Study of the Effect of CT1812 Treatment on Synaptic Activity in Patients with Mild to Moderate Alzheimer's Disease

BACKGROUND

CT1812 is a first-in-class, sigma-2 receptor ligand, that prevents and displaces binding of amyloid beta (Aβ) oligomers. Normalization of quantitative electroencephalography (qEEG) markers suggests that CT1812 protects synapses from Aβ oligomer toxicity.

OBJECTIVES

Evaluate CT1812 impact on synaptic function using qEEG measurements.

DESIGN

Phase 2, randomized, double-blind, placebo-controlled, 4-week crossover study.

SETTING

VU University Medical Center and Brain Research Center Amsterdam, The Netherlands.

PARTICIPANTS

Adults with mild or moderate Alzheimer's disease (AD).

INTERVENTION

A daily 300 mg dose of CT1812 or placebo for 4 weeks.

MEASUREMENTS

A resting-state, eyes closed qEEG assessment occurred on Day 1 and on Day 29 of Treatment Periods 1 and 2, and at follow-up. The primary endpoint was global relative theta power (4-8 Hz), along with secondary EEG measures including global alpha corrected Amplitude Envelope Correlation (AEC-c). Cognitive and functional assessments, fluid biomarkers, and safety and tolerability were assessed.

RESULTS

16 patients were randomized, and 15 completed. A non-significant (p=0.123) but consistent reduction occurred in global relative theta power and in relative theta power in frontal, temporal, parietal, occipital and central (p<0.006) brain regions with CT1812. A nominally significant (p=0.034) improvement was observed in global alpha AEC-c. Adverse events occurred in 11 patients with CT1812 and 6 with placebo - most commonly nausea, diarrhea, and procedural headache. No severe or serious AEs, deaths or discontinuations were reported.

CONCLUSION

CT1812 improved established EEG markers of spontaneous brain activity (spectral power, functional connectivity) in patients with mild-to-moderate AD, suggesting improved neuronal/synaptic function within a 4-week timespan.

Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies

OBJECTIVE

Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD.

METHODS

We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023.

RESULTS

Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25).

CONCLUSIONS

Alterations of alpha synchrony are present even at MCI stage.

SIGNIFICANCE

Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.

A novel micro-ECoG recording method for recording multisensory neural activity from the parietal to temporal cortices in mice

Characterization of inter-regional interactions in brain is essential for understanding the mechanism relevant to normal brain function and neurological disease. The recently developed flexible micro (μ)-electrocorticography (μECoG) device is one prominent method used to examine large-scale cortical activity across multiple regions. The sheet-shaped μECoG electrodes arrays can be placed on a relatively wide area of cortical surface beneath the skull by inserting the device into the space between skull and brain. Although rats and mice are useful tools for neuroscience, current μECoG recording methods in these animals are limited to the parietal region of cerebral cortex. Recording cortical activity from the temporal region of cortex in mice has proven difficult because of surgical barriers created by the skull and surrounding temporalis muscle anatomy. Here, we developed a sheet-shaped 64-channel μECoG device that allows access to the mouse temporal cortex, and we determined the factor determining the appropriate bending stiffness for the μECoG electrode array. We also established a surgical technique to implant the electrode arrays into the epidural space over a wide area of cerebral cortex covering from the barrel field to olfactory (piriform) cortex, which is the deepest region of the cerebral cortex. Using histology and computed tomography (CT) images, we confirmed that the tip of the μECoG device reached to the most ventral part of cerebral cortex without causing noticeable damage to the brain surface. Moreover, the device simultaneously recorded somatosensory and odor stimulus-evoked neural activity from dorsal and ventral parts of cerebral cortex in awake and anesthetized mice. These data indicate that our μECoG device and surgical techniques enable the recording of large-scale cortical activity from the parietal to temporal cortex in mice, including somatosensory and olfactory cortices. This system will provide more opportunities for the investigation of physiological functions from wider areas of the mouse cerebral cortex than those currently available with existing ECoG techniques.

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.

Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques

In recent years, applications of the network science to electrophysiological data have increased as electrophysiological techniques are not only relatively low cost, largely available on the territory and non-invasive, but also potential tools for large population screening. One of the emergent methods for the study of functional connectivity in electrophysiological recordings is graph theory: it allows to describe the brain through a mathematic model, the graph, and provides a simple representation of a complex system. As Alzheimer’s and Parkinson’s disease are associated with synaptic disruptions and changes in the strength of functional connectivity, they can be well described by functional connectivity analysis computed via graph theory. The aim of the present review is to provide an overview of the most recent applications of the graph theory to electrophysiological data in the two by far most frequent neurodegenerative disorders, Alzheimer’s and Parkinson’s diseases.

Correlations of frontal resting-state EEG markers with MMSE scores in patients with Alzheimer’s disease

Background

A previous study suggests that resting-state EEG biomarkers measured at prefrontal region (Fp1, and Fp2) are moderately correlated with Mini-Mental State Examination (MMSE) scores of elderly people with Alzheimer’s disease. In this study, our objective was to investigate whether resting-state EEG biomarkers recorded from frontal region are correlated with each MMSE sub-scores. 20 elderly patients diagnosed as Alzheimer’s disease entered to the study. After completion of MMSE, subjects underwent EEG for 5 min with closed eyes condition. We measured median frequency, theta/alpha power ratio, and relative powers. To examine the relationship between these features and MMSE sub-scores first, Pearson correlation coefficients were computed for each feature and MMSE sub-scores. Then, p values were computed for each correlation. Finally, a Bonferroni correction was done.

Results

Nine correlations have been found for markers recorded from F3, F7, and Fz. Alpha and beta relative powers were the markers which shows correlations. We found that MMSE overall, attention, and calculation scores are significantly correlated with beta relative powers recorded from F3, and Fz, and alpha relative power from F7. Orientation to time scores were correlated with F3, and Fz beta relative powers. The only correlation found for orientation to place was beta relative power of F3.

Conclusions

Our results indicate that there are correlations between frontal EEG markers and MMSE sub-scores of patients with Alzheimer’s disease. The results show that alpha and beta relative powers are markers correlated with MMSE scores. It seems that if we want to develop predicting models for Alzheimer’s disease, using data recorded from other frontal electrodes, especially what we have introduced should be considered.

Cognitive, EEG, and MRI features of COVID-19 survivors: a 10-month study

Background and objectives

To explore cognitive, EEG, and MRI features in COVID-19 survivors up to 10 months after hospital discharge.

Methods

Adult patients with a recent diagnosis of COVID-19 and reporting subsequent cognitive complaints underwent neuropsychological assessment and 19-channel-EEG within 2 months (baseline, N = 49) and 10 months (follow-up, N = 33) after hospital discharge. A brain MRI was obtained for 36 patients at baseline. Matched healthy controls were included. Using eLORETA, EEG regional current densities and linear lagged connectivity values were estimated. Total brain and white matter hyperintensities (WMH) volumes were measured. Clinical and instrumental data were evaluated between patients and controls at baseline, and within patient whole group and with/without dysgeusia/hyposmia subgroups over time. Correlations among findings at each timepoint were computed.

Results

At baseline, 53% and 28% of patients showed cognitive and psychopathological disturbances, respectively, with executive dysfunctions correlating with acute-phase respiratory distress. Compared to healthy controls, patients also showed higher regional current density and connectivity at delta band, correlating with executive performances, and greater WMH load, correlating with verbal memory deficits. A reduction of cognitive impairment and delta band EEG connectivity were observed over time, while psychopathological symptoms persisted. Patients with acute dysgeusia/hyposmia showed lower improvement at memory tests than those without. Lower EEG delta band at baseline predicted worse cognitive functioning at follow-up.

Discussion

COVID-19 patients showed interrelated cognitive, EEG, and MRI abnormalities 2 months after hospital discharge. Cognitive and EEG findings improved at 10 months. Dysgeusia and hyposmia during acute COVID-19 were related with increased vulnerability in memory functions over time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11047-5.

EEG as a translational biomarker and outcome measure in fragile X syndrome

Targeted treatments for fragile X syndrome (FXS) have frequently failed to show efficacy in clinical testing, despite success at the preclinical stages. This has highlighted the need for more effective translational outcome measures. EEG differences observed in FXS, including exaggerated N1 ERP amplitudes, increased resting gamma power and reduced gamma phase-locking in the sensory cortices, have been suggested as potential biomarkers of the syndrome. These abnormalities are thought to reflect cortical hyper excitability resulting from an excitatory (glutamate) and inhibitory (GABAergic) imbalance in FXS, which has been the target of several pharmaceutical remediation studies. EEG differences observed in humans also show similarities to those seen in laboratory models of FXS, which may allow for greater translational equivalence and better predict clinical success of putative therapeutics. There is some evidence from clinical trials showing that treatment related changes in EEG may be associated with clinical improvements, but these require replication and extension to other medications. Although the use of EEG characteristics as biomarkers is still in the early phases, and further research is needed to establish its utility in clinical trials, the current research is promising and signals the emergence of an effective translational biomarker.