What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease

INTRODUCTION

Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition.

METHODS

We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy.

RESULTS

AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified "fast cognitive decliners" among study participants who had AD.

DISCUSSION

Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development.

HIGHLIGHTS

Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

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.

Adenosine deficiency facilitates CA1 synaptic hyperexcitability in the presymptomatic phase of a knock in mouse model of Alzheimer's disease

The disease's trajectory of Alzheimer's disease (AD) is associated with and worsened by hippocampal hyperexcitability. Here we show that during the asymptomatic stage in a knock in mouse model of Alzheimer's disease (APPNL-G-F/NL-G-F; APPKI), hippocampal hyperactivity occurs at the synaptic compartment, propagates to the soma and is manifesting at low frequencies of stimulation. We show that this aberrant excitability is associated with a deficient adenosine tone, an inhibitory neuromodulator, driven by reduced levels of CD39/73 enzymes, responsible for the extracellular ATP-to-adenosine conversion. Both pharmacologic (adenosine kinase inhibitor) and non-pharmacologic (ketogenic diet) restorations of the adenosine tone successfully normalize hippocampal neuronal activity. Our results demonstrated that neuronal hyperexcitability during the asymptomatic stage of a KI model of Alzheimer's disease originated at the synaptic compartment and is associated with adenosine deficient tone. These results extend our comprehension of the hippocampal vulnerability associated with the asymptomatic stage of Alzheimer's disease.

A distributed theta network of error generation and processing in aging

Based on previous concepts that a distributed theta network with a central "hub" in the medial frontal cortex is critically involved in movement regulation, monitoring, and control, the present study explored the involvement of this network in error processing with advancing age in humans. For that aim, the oscillatory neurodynamics of motor theta oscillations was analyzed at multiple cortical regions during correct and error responses in a sample of older adults. Response-related potentials (RRPs) of correct and incorrect reactions were recorded in a four-choice reaction task. RRPs were decomposed in the time-frequency domain to extract oscillatory theta activity. Motor theta oscillations at extended motor regions were analyzed with respect to power, temporal synchronization, and functional connectivity. Major results demonstrated that errors had pronounced effects on motor theta oscillations at cortical regions beyond the medial frontal cortex by being associated with (1) theta power increase in the hemisphere contra-lateral to the movement, (2) suppressed spatial and temporal synchronization at pre-motor areas contra-lateral to the responding hand, (2) inhibited connections between the medial frontal cortex and sensorimotor areas, and (3) suppressed connectivity and temporal phase-synchronization of motor theta networks in the posterior left hemisphere, irrespective of the hand, left, or right, with which the error was made. The distributed effects of errors on motor theta oscillations demonstrate that theta networks support performance monitoring. The reorganization of these networks with aging implies that in older individuals, performance monitoring is associated with a disengagement of the medial frontal region and difficulties in controlling the focus of motor attention and response selection.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-10018-4.

Safety, tolerability, and efficacy estimate of evoked gamma oscillation in mild to moderate Alzheimer's disease

Background

Alzheimer's Disease (AD) is a multifactorial, progressive neurodegenerative disease that disrupts synaptic and neuronal activity and network oscillations. It is characterized by neuronal loss, brain atrophy and a decline in cognitive and functional abilities. Cognito's Evoked Gamma Therapy System provides an innovative approach for AD by inducing EEG-verified gamma oscillations through sensory stimulation. Prior research has shown promising disease-modifying effects in experimental AD models. The present study (NCT03556280: OVERTURE) evaluated the feasibly, safety and efficacy of evoked gamma oscillation treatment using Cognito's medical device (CogTx-001) in participants with mild to moderate AD.

Methods

The present study was a randomized, double blind, sham-controlled, 6-months clinical trial in participants with mild to moderate AD. The trial enrolled 76 participants, aged 50 or older, who met the clinical criteria for AD with baseline MMSE scores between 14 and 26. Participants were randomly assigned 2:1 to receive self-administered daily, one-hour, therapy, evoking EEG-verified gamma oscillations or sham treatment. The CogTx-001 device was use at home with the help of a care partner, over 6 months. The primary outcome measures were safety, evaluated by physical and neurological exams and monthly assessments of adverse events (AEs) and MRI, and tolerability, measured by device use. Although the trial was not statistically powered to evaluate potential efficacy outcomes, primary and secondary clinical outcome measures included several cognitive and functional endpoints.

Results

Total AEs were similar between groups, there were no unexpected serious treatment related AEs, and no serious treatment-emergent AEs that led to study discontinuation. MRI did not show Amyloid-Related Imaging Abnormalities (ARIA) in any study participant. High adherence rates (85-90%) were observed in sham and treatment participants. There was no statistical separation between active and sham arm participants in primary outcome measure of MADCOMS or secondary outcome measure of CDR-SB or ADAS-Cog14. However, some secondary outcome measures including ADCS-ADL, MMSE, and MRI whole brain volume demonstrated reduced progression in active compared to sham treated participants, that achieved nominal significance.

Conclusion

Our results demonstrate that 1-h daily treatment with Cognito's Evoked Gamma Therapy System (CogTx-001) was safe and well-tolerated and demonstrated potential clinical benefits in mild to moderate AD.Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03556280.

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.

Poor reactivity of posterior electroencephalographic alpha rhythms during the eyes open condition in patients with dementia due to Parkinson's disease

Here, we hypothesized that the reactivity of posterior resting-state electroencephalographic (rsEEG) alpha rhythms during the transition from eyes-closed to -open condition might be lower in patients with Parkinson's disease dementia (PDD) than in patients with Alzheimer's disease dementia (ADD). A Eurasian database provided clinical-demographic-rsEEG datasets in 73 PDD patients, 35 ADD patients, and 25 matched cognitively unimpaired (Healthy) persons. The eLORETA freeware was used to estimate cortical rsEEG sources. Results showed substantial (greater than -10%) reduction (reactivity) in the posterior alpha source activities from the eyes-closed to the eyes-open condition in 88% of the Healthy seniors, 57% of the ADD patients, and only 35% of the PDD patients. In these alpha-reactive participants, there was lower reactivity in the parietal alpha source activities in the PDD group than in the healthy control seniors and the ADD patients. These results suggest that PDD patients show poor reactivity of mechanisms desynchronizing posterior rsEEG alpha rhythms in response to visual inputs. That neurophysiological biomarker may provide an endpoint for (non) pharmacological interventions for improving vigilance regulation in those patients.

Firing Alterations of Neurons in Alzheimer's Disease: Are They Merely a Consequence of Pathogenesis or a Pivotal Component of Disease Progression?

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, yet its underlying causes remain elusive. The conventional perspective on disease pathogenesis attributes alterations in neuronal excitability to molecular changes resulting in synaptic dysfunction. Early hyperexcitability is succeeded by a progressive cessation of electrical activity in neurons, with amyloid beta (Aβ) oligomers and tau protein hyperphosphorylation identified as the initial events leading to hyperactivity. In addition to these key proteins, voltage-gated sodium and potassium channels play a decisive role in the altered electrical properties of neurons in AD. Impaired synaptic function and reduced neuronal plasticity contribute to a vicious cycle, resulting in a reduction in the number of synapses and synaptic proteins, impacting their transportation inside the neuron. An understanding of these neurophysiological alterations, combined with abnormalities in the morphology of brain cells, emerges as a crucial avenue for new treatment investigations. This review aims to delve into the detailed exploration of electrical neuronal alterations observed in different AD models affecting single neurons and neuronal networks.

The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review

OBJECTIVE

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD.

METHODS

PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study.

RESULTS

AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug.

CONCLUSIONS

We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.

Adjunct Methods for Alzheimer's Disease Detection: A Review of Auditory Evoked Potentials

The auditory afferent pathway as a clinical marker of Alzheimer's disease (AD) has sparked interest in investigating the relationship between age-related hearing loss (ARHL) and AD. Given the earlier onset of ARHL compared to cognitive impairment caused by AD, there is a growing emphasis on early diagnosis and intervention to postpone or prevent the progression from ARHL to AD. In this context, auditory evoked potentials (AEPs) have emerged as a widely used objective auditory electrophysiological technique for both the clinical diagnosis and animal experimentation in ARHL due to their non-invasive and repeatable nature. This review focuses on the application of AEPs in AD detection and the auditory nerve system corresponding to different latencies of AEPs. Our objective was to establish AEPs as a systematic and non-invasive adjunct method for enhancing the diagnostic accuracy of AD. The success of AEPs in the early detection and prediction of AD in research settings underscores the need for further clinical application and study.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model

Background

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance.

Objective

We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach.

Methods

We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology.

Results

The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics.

Conclusions

Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

A multidimensional model of memory complaints in older individuals and the associated hub regions

Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.

Coherence in event-related EEG oscillations in patients with Alzheimer's disease dementia and amnestic mild cognitive impairment

Objectives Working memory performances are based on brain functional connectivity, so that connectivity may be deranged in individuals with mild cognitive impairment (MCI) and patients with dementia due to Alzheimer's disease (ADD). Here we tested the hypothesis of abnormal functional connectivity as revealed by the imaginary part of coherency (ICoh) at electrode pairs from event-related electroencephalographic oscillations in ADD and MCI patients. Methods The study included 43 individuals with MCI, 43 with ADD, and 68 demographically matched healthy controls (HC). Delta, theta, alpha, beta, and gamma bands event-related ICoh was measured during an oddball paradigm. Inter-hemispheric, midline, and intra-hemispheric ICoh values were compared in ADD, MCI, and HC groups. Results The main results of the present study can be summarized as follows: (1) A significant increase of midline frontal and temporal theta coherence in the MCI group as compared to the HC group; (2) A significant decrease of theta, delta, and alpha intra-hemispheric coherence in the ADD group as compared to the HC and MCI groups; (3) A significant decrease of theta midline coherence in the ADD group as compared to the HC and MCI groups; (4) Normal inter-hemispheric coherence in the ADD and MCI groups. Conclusions Compared with the MCI and HC, the ADD group showed disrupted event-related intra-hemispheric and midline low-frequency band coherence as an estimate of brain functional dysconnectivity underlying disabilities in daily living. Brain functional connectivity during attention and short memory demands is relatively resilient in elderly subjects even with MCI (with preserved abilities in daily activities), and it shows reduced efficiency at multiple operating oscillatory frequencies only at an early stage of ADD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09920-0.

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.

Antidepressant effect of bright light therapy on patients with Alzheimer's disease and their caregivers

Background: As a non-pharmacologic treatment, bright light therapy (BLT) is often used to improve affective disorders and memory function. In this study, we aimed to determine the effect of BLT on depression and electrophysiological features of the brain in patients with Alzheimer's disease (AD) and their caregivers using a light-emitting diode device of 14000 lux. Methods: A 4-week case-control trial was conducted. Neuropsychiatric and electroencephalogram (EEG) examination were evaluated at baseline and after 4 weeks. EEG power in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) bands was calculated for our main analysis. Demographic and clinical variables were analyzed using Student's t test and the chi-square test. Pearson's correlation was used to determine the correlation between electrophysiological features, blood biochemical indicators, and cognitive assessment scale scores. Results: In this study, 22 in-patients with AD and 23 caregivers were recruited. After BLT, the Hamilton depression scale score decreased in the fourth week. Compared with the age-matched controls of their caregivers, a higher spectral power at the lower delta and theta frequencies was observed in the AD group. After BLT, the EEG power of the delta and theta frequencies in the AD group decreased. No change was observed in blood amyloid concentrations before and after BLT. Conclusion: In conclusion, a 4-week course of BLT significantly suppressed depression in patients with AD and their caregivers. Moreover, changes in EEG power were also significant in both groups.

Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia

Introduction

Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain.

Methods

In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds.

Results and discussion

Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.

Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease

Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.

Resting EEG power spectra across middle to late life: associations with age, cognition, APOE-ɛ4 carriage, and cardiometabolic burden

We investigated how resting electroencephalography (EEG) measures are associated with risk factors for late-life cognitive impairment and dementia, including age, apolipoprotein E ɛ4 (APOE-ɛ4) carriage, and cardiometabolic burden. Resting EEG was recorded from 86 adults (50-80 years of age). Participants additionally completed the Addenbrooke's Cognitive Examination (ACE) III and had blood drawn to assess APOE-ɛ4 carriage status and cardiometabolic burden. EEG power spectra were decomposed into sources of periodic and aperiodic activity to derive measures of aperiodic component slope and alpha (7-14 Hz) and beta (15-30 Hz) peak power and peak frequency. Alpha and beta peak power measures were corrected for aperiodic activity. The aperiodic component slope was correlated with ACE-III scores but not age. Alpha peak frequency decreased with age. Individuals with higher cardiometabolic burden had lower alpha peak frequencies and lower beta peak power. APOE-ɛ4 carriers had lower beta peak frequencies. Our findings suggest that the slope of the aperiodic component of resting EEG power spectra is more closely associated with measures of cognitive performance rather than chronological age in older adults.

Abnormal Temporal Slowing on EEG Findings in Preclinical Alzheimer's Disease Patients With the ApoE4 Allele: A Pilot Study

INTRODUCTION

Currently, there are limited accessible and cost-effective biomarkers for preclinical Alzheimer's disease (AD) patients. However, the apolipoprotein E (ApoE) polymorphic alleles can predict if someone is at high (e4), neutral (e3), or low (e2) genetic risk for developing AD. This study analyzed electroencephalogram (EEG) reports from individuals with various ApoE genotypes, aiming to identify EEG changes and patterns that could potentially serve as predictive markers for preclinical AD progression.

METHODS

Participants aged 64-78 were selected from the patient database at an outpatient neurology clinic. Genotype studies were performed to determine ApoE status, followed by EEG analysis to identify any apparent trends. A case-control design was used, categorizing participants into cases (e2e3, e2e4, e3e4, e4e4) and controls (e3e3). EEG recordings were compared between the groups to identify potential differences in EEG characteristics, including abnormal temporal slowing, frequency, and ApoE genotype association.

RESULTS

Among 43 participants, 49% demonstrated evidence of abnormal temporal slowing on EEG. Of these, 48% displayed focal left temporal slowing, and 52% displayed bilateral temporal slowing. The right-sided temporal slowing was not observed. Among participants with abnormal slowing, 95% exhibited theta frequency (4-8 Hz) slowing, while only 4.8% displayed delta frequency (0-4 Hz) slowing. Among participants with the ApoE4 allele, 61.5% demonstrated evidence of abnormal slowing, compared to 43.3% without it. Furthermore, the presence of an ApoE4 allele was associated with a significantly higher proportion of males (54%) compared to those without it (13%) (p=0.009).

CONCLUSIONS

Although we did not find a statistically significant difference in temporal EEG slowing among different ApoE genotypes, our findings suggest a potential association between temporal slowing on EEG and the presence of an ApoE4 allele in individuals with preclinical AD. These observations highlight the need for further exploration into the potential influence of the ApoE4 allele on EEG findings and the utility of EEG as a complementary diagnostic tool for AD. Longitudinal studies with large sample sizes are needed to establish the precise relationship between EEG patterns, ApoE genotypes, and AD progression.

Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia

BACKGROUND

Alzheimer's dementia (AD) is associated with electroencephalography (EEG) abnormalities including in the power ratio of beta to theta frequencies. EEG studies in mild cognitive impairment (MCI) have been less consistent in identifying such abnormalities. One potential reason is not excluding the EEG aperiodic components, which are less associated with cognition than the periodic components. Here, we investigate whether aperiodic and periodic EEG components are disrupted differently in AD or MCI vs. healthy control (HC) individuals and whether a periodic based beta/theta ratio differentiates better MCI from AD and HC groups than a ratio based on the full spectrum.

METHODS

Data were collected from 44 HC (mean age (SD) = 69.1 (5.3)), 114 MCI (mean age (SD) = 72.2 (7.5)), and 41 AD (mean age (SD) = 75.7 (6.5)) participants. Aperiodic and periodic components and full spectrum EEG were compared among the three groups. Receiver operating characteristic curves obtained via logistic regression classifications were used to distinguish the groups. Last, we explored the relationships between cognitive performance and the beta/theta ratios based on the full or periodic spectrum.

RESULTS

Aperiodic EEG components did not differ among the three groups. In contrast, AD participants showed an increase in full spectrum and periodic relative powers for delta, theta, and gamma and a decrease for beta when compared to HC or MCI participants. As predicted, MCI group differed from HC participants on the periodic based beta/theta ratio (Bonferroni corrected p-value = 0.036) measured over the occipital region. Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI participants, and outperformed classifiers based on beta/theta power ratio in full spectrum EEG. Beta/theta ratios were comparable in their association with cognition.

CONCLUSIONS

In contrast to a full spectrum EEG analysis, a periodic-based analysis shows that MCI individuals are different on beta/theta ratio when compared to healthy individuals. Focusing on periodic components in EEG studies with or without other biological markers of neurodegenerative diseases could result in more reliable findings to separate MCI from healthy aging, which would be valuable for designing preventative interventions.

Alterations of resting-state Gamma frequency characteristics in aging and Alzheimer's disease

Alzheimer's disease (AD) is an important brain disease associated with aging. It involves various functional and structural changes which alter the EEG characteristics. Although numerous studies have found changes in delta, theta, alpha, and beta power, fewer studies have looked at the changes in the resting state EEG gamma activity characteristics in AD. This study aimed to investigate the alterations in the frequency and power values of AD patients' resting-state EEG gamma oscillations compared with healthy elderly and young subjects. We performed Fast Fourier Transform (FFT) on the resting state EEG data from 179 participants, including 59 early stage AD patients, 60 healthy elderly, and 60 healthy young subjects. We averaged FFT performed epochs to investigate the power values in the gamma frequency range (28-48 Hz). We then sorted the peaks of power values in the gamma frequency range, and the average of the identified highest three values was named as the gamma dominant peak frequency. The gamma dominant peak frequency of AD patients (Meyes-opened = 33.4 Hz, Meyes-closed = 32.7 Hz) was lower than healthy elderly (Meyes-opened = 35.5 Hz, Meyes-closed = 35.0 Hz) and healthy young subjects (Meyes-opened = 37.2 Hz, Meyes-closed = 37.0 Hz). These results could be related to AD progression and therefore critical for the recent findings regarding the 40 Hz gamma entrainment because it seems they entrain the gamma frequency of AD towards that of healthy young.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09873-4.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

OBJECTIVE

This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.

METHODS

We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.

RESULTS

For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.

CONCLUSIONS

Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.

SIGNIFICANCE

Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients

As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.

Cortico-muscular coherence and brain networks in familial adult myoclonic epilepsy and progressive myoclonic epilepsy

OBJECTIVE

Familial Adult Myoclonic Epilepsy (FAME) presents with action-activated myoclonus, often associated with epilepsy, sharing various features with Progressive Myoclonic Epilepsy (PMEs), but with slower course and limited motor disability. We aimed our study to identify measures suitable to explain the different severity of FAME2 compared to EPM1, the most common PME, and to detect the signature of the distinctive brain networks.

METHODS

We analyzed the EEG-EMG coherence (CMC) during segmental motor activity and indexes of connectivity in the two patient groups, and in healthy subjects (HS). We also investigated the regional and global properties of the network.

RESULTS

In FAME2, differently from EPM1, we found a well-localized distribution of beta-CMC and increased betweenness-centrality (BC) on the sensorimotor region contralateral to the activated hand. In both patient groups, compared to HS, there was a decline in the network connectivity indexes in the beta and gamma band, which was more obvious in FAME2.

CONCLUSIONS

In FAME2, better localized CMC and increased BC in comparison with EPM1 patients could counteract the severity and the spreading of the myoclonus. Decreased indexes of cortical integration were more severe in FAME2.

SIGNIFICANCE

Our measures correlated with different motor disabilities and identified distinctive brain network impairments.

Pushing the boundaries of brain organoids to study Alzheimer's disease

Progression of Alzheimer's disease (AD) entails deterioration or aberrant function of multiple brain cell types, eventually leading to neurodegeneration and cognitive decline. Defining how complex cell-cell interactions become dysregulated in AD requires novel human cell-based in vitro platforms that could recapitulate the intricate cytoarchitecture and cell diversity of the human brain. Brain organoids (BOs) are 3D self-organizing tissues that partially resemble the human brain architecture and can recapitulate AD-relevant pathology. In this review, we highlight the versatile applications of different types of BOs to model AD pathogenesis, including amyloid-β and tau aggregation, neuroinflammation, myelin breakdown, vascular dysfunction, and other phenotypes, as well as to accelerate therapeutic development for AD.

The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early stages of Alzheimer's disease (AD). Neurophysiological markers such as electroencephalography (EEG) and event-related potential (ERP) are emerging as alternatives to traditional molecular and imaging markers. This paper aimed to review the literature on EEG and ERP markers in individuals with SCD. We analysed 30 studies that met our criteria, with 17 focusing on resting-state or cognitive task EEG, 11 on ERPs, and two on both EEG and ERP parameters. Typical spectral changes were indicative of EEG rhythm slowing and were associated with faster clinical progression, lower education levels, and abnormal cerebrospinal fluid biomarkers profiles. Some studies found no difference in ERP components between SCD subjects, controls, or MCI, while others reported lower amplitudes in the SCD group compared to controls. Further research is needed to explore the prognostic value of EEG and ERP in relation to molecular markers in individuals with SCD.

Prolonged fluoride exposure induces spatial-memory deficit and hippocampal dysfunction by inhibiting small heat shock protein 22 in mice

Millions of residents in areas with high-fluoride drinking water supply ingest excessive levels of fluoride for long periods. This study investigated the mechanisms and impacts of lifelong exposure to naturally occurring moderate-high-fluoride drinking water on spatial-memory function by studying mice in controlled experiments. Spatial-memory deficits and disorders of hippocampal neuronal electrical activity were observed in mice exposed to 25-ppm or 50-ppm-fluoride drinking water for 56 weeks, but not in adult or old mice exposed to 50 ppm fluoride for 12 weeks. Ultrastructural analysis showed severely damaged hippocampal mitochondria, evidenced by reduced mitochondrial membrane potential and ATP content. Mitochondrial biogenesis was impaired in fluoride-exposed mice, manifesting as a significantly reduced mtDNA content, mtDNA-encoded subunits mtND6 and mtCO1, and respiratory complex activities. Fluoride reduced expression of Hsp22, a beneficial mediator of mitochondrial homeostasis, and decreased levels of signaling for the PGC-1α/TFAM pathway-which regulates mitochondrial biogenesis-and the NF-κβ/STAT3 pathway-which regulates mitochondrial respiratory chain enzyme activity. Hippocampus-specific Hsp22-overexpression improved fluoride-induced spatial-memory deficits by activating the PGC-1α/TFAM and STAT3 signaling pathways, while Hsp22-silencing aggravated the deficits by inhibiting both pathways. Downregulation of Hsp22 plays a vital role in fluoride-induced spatial-memory deficits by impacting mtDNA-encoding subsets and mitochondrial respiratory chain enzyme activity.

Priorities for research on neuromodulatory subcortical systems in Alzheimer's disease: Position paper from the NSS PIA of ISTAART

The neuromodulatory subcortical system (NSS) nuclei are critical hubs for survival, hedonic tone, and homeostasis. Tau-associated NSS degeneration occurs early in Alzheimer's disease (AD) pathogenesis, long before the emergence of pathognomonic memory dysfunction and cortical lesions. Accumulating evidence supports the role of NSS dysfunction and degeneration in the behavioral and neuropsychiatric manifestations featured early in AD. Experimental studies even suggest that AD-associated NSS degeneration drives brain neuroinflammatory status and contributes to disease progression, including the exacerbation of cortical lesions. Given the important pathophysiologic and etiologic roles that involve the NSS in early AD stages, there is an urgent need to expand our understanding of the mechanisms underlying NSS vulnerability and more precisely detail the clinical progression of NSS changes in AD. Here, the NSS Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment highlights knowledge gaps about NSS within AD and provides recommendations for priorities specific to clinical research, biomarker development, modeling, and intervention. HIGHLIGHTS: Neuromodulatory nuclei degenerate in early Alzheimer's disease pathological stages. Alzheimer's pathophysiology is exacerbated by neuromodulatory nuclei degeneration. Neuromodulatory nuclei degeneration drives neuropsychiatric symptoms in dementia. Biomarkers of neuromodulatory integrity would be value-creating for dementia care. Neuromodulatory nuclei present strategic prospects for disease-modifying therapies.

Age-Related Modifications of Electroencephalogram Coherence in Mice Models of Alzheimer's Disease and Amyotrophic Lateral Sclerosis

Evident similarities in pathological features in aging and Alzheimer's disease (AD) raise the question of a role for natural age-related adaptive mechanisms in the prevention/elimination of disturbances in interrelations between different brain areas. In our previous electroencephalogram (EEG) studies on 5xFAD- and FUS-transgenic mice, as models of AD and amyotrophic lateral sclerosis (ALS), this suggestion was indirectly confirmed. In the current study, age-related changes in direct EEG synchrony/coherence between the brain structures were evaluated.

METHODS

In 5xFAD mice of 6-, 9-, 12-, and 18-month ages and their wild-type (WT_5xFAD) littermates, we analyzed baseline EEG coherence between the cortex, hippocampus/putamen, ventral tegmental area, and substantia nigra. Additionally, EEG coherence between the cortex and putamen was analyzed in 2- and 5-month-old FUS mice.

RESULTS

In the 5xFAD mice, suppressed levels of inter-structural coherence vs. those in WT_5xFAD littermates were observed at ages of 6, 9, and 12 months. In 18-month-old 5xFAD mice, only the hippocampus ventral tegmental area coherence was significantly reduced. In 2-month-old FUS vs. WT_FUS mice, the cortex-putamen coherence suppression, dominated in the right hemisphere, was observed. In 5-month-old mice, EEG coherence was maximal in both groups.

CONCLUSION

Neurodegenerative pathologies are accompanied by the significant attenuation of intracerebral EEG coherence. Our data are supportive for the involvement of age-related adaptive mechanisms in intracerebral disturbances produced by neurodegeneration.

Neuronal Hyperactivation in EEG Data during Cognitive Tasks Is Related to the Apolipoprotein J/Clusterin Genotype in Nondemented Adults

The clusterin (CLU) rs11136000 CC genotype is a probable risk factor for Alzheimer's disease (AD). CLU, also known as the apolipoprotein J gene, shares certain properties with the apolipoprotein E (APOE) gene with a well-established relationship with AD. This study aimed to determine whether the electrophysiological patterns of brain activation during the letter fluency task (LFT) depend on CLU genotypes in adults without dementia. Previous studies have shown that LFT performance involves activation of the frontal cortex. We examined EEG alpha1 and alpha2 band desynchronization in the frontal regions during the LFT in 94 nondemented individuals stratified by CLU (rs11136000) genotype. Starting at 30 years of age, CLU CC carriers exhibited more pronounced task-related alpha2 desynchronization than CLU CT&TT carriers in the absence of any differences in LFT performance. In CLU CC carriers, alpha2 desynchronization was significantly correlated with age. Increased task-related activation in individuals at genetic risk for AD may reflect greater "effort" to perform the task and/or neuronal hyperexcitability. The results show that the CLU genotype is associated with neuronal hyperactivation in the frontal cortex during cognitive tasks performances in nondemented individuals, suggesting systematic vulnerability of LFT related cognitive networks in people carrying unfavorable CLU alleles.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Toward the Identification of Neurophysiological Biomarkers for Alzheimer's Disease in Down Syndrome: A Potential Role for Cross-Frequency Phase-Amplitude Coupling Analysis

Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Molecular Biomarkers of Neuronal Injury in Epilepsy Shared with Neurodegenerative Diseases

In neurodegenerative diseases, changes in neuronal proteins in the cerebrospinal fluid and blood are viewed as potential biomarkers of the primary pathology in the central nervous system (CNS). Recent reports suggest, however, that level of neuronal proteins in fluids also alters in several types of epilepsy in various age groups, including children. With increasing evidence supporting clinical and sub-clinical seizures in Alzheimer's disease, Lewy body dementia, Parkinson's disease, and in other less common neurodegenerative conditions, these findings call into question the specificity of neuronal protein response to neurodegenerative process and urge analysis of the effects of concomitant epilepsy and other comorbidities. In this article, we revisit the evidence for alterations in neuronal proteins in the blood and cerebrospinal fluid associated with epilepsy with and without neurodegenerative diseases. We discuss shared and distinctive characteristics of changes in neuronal markers, review their neurobiological mechanisms, and consider the emerging opportunities and challenges for their future research and diagnostic use.

Single‐value scores of memory‐related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging

Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two single-value scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, we investigate the brain-behavior associations of these scores with age-related neurocognitive changes in 153 healthy middle-aged and older adults. All scores were associated with episodic recall performance. The memory network scores, but not the novelty network scores, additionally correlated with medial temporal gray matter and other neuropsychological measures including flexibility. Our results thus suggest that novelty-network-based fMRI scores show high brain-behavior associations with episodic memory and that encoding-network-based fMRI scores additionally capture individual differences in other aging-related functions. More generally, our results suggest that single-value scores of memory-related fMRI provide a comprehensive measure of individual differences in network dysfunction that may contribute to age-related cognitive decline.

Human neural network activity reacts to gravity changes in vitro

During spaceflight, humans experience a variety of physiological changes due to deviations from familiar earth conditions. Specifically, the lack of gravity is responsible for many effects observed in returning astronauts. These impairments can include structural as well as functional changes of the brain and a decline in cognitive performance. However, the underlying physiological mechanisms remain elusive. Alterations in neuronal activity play a central role in mental disorders and altered neuronal transmission may also lead to diminished human performance in space. Thus, understanding the influence of altered gravity at the cellular and network level is of high importance. Previous electrophysiological experiments using patch clamp techniques and calcium indicators have shown that neuronal activity is influenced by altered gravity. By using multi-electrode array (MEA) technology, we advanced the electrophysiological investigation covering single-cell to network level responses during exposure to decreased (micro-) or increased (hyper-) gravity conditions. We continuously recorded in real-time the spontaneous activity of human induced pluripotent stem cell (hiPSC)-derived neural networks in vitro. The MEA device was integrated into a custom-built environmental chamber to expose the system with neuronal cultures to up to 6 g of hypergravity on the Short-Arm Human Centrifuge at the DLR Cologne, Germany. The flexibility of the experimental hardware set-up facilitated additional MEA electrophysiology experiments under 4.7 s of high-quality microgravity (10–6 to 10–5 g) in the Bremen drop tower, Germany. Hypergravity led to significant changes in activity. During the microgravity phase, the mean action potential frequency across the neural networks was significantly enhanced, whereas different subgroups of neurons showed distinct behaviors, such as increased or decreased firing activity. Our data clearly demonstrate that gravity as an environmental stimulus triggers changes in neuronal activity. Neuronal networks especially reacted to acute changes in mechanical loading (hypergravity) or de-loading (microgravity). The current study clearly shows the gravity-dependent response of neuronal networks endorsing the importance of further investigations of neuronal activity and its adaptive responses to micro- and hypergravity. Our approach provided the basis for the identification of responsible mechanisms and the development of countermeasures with potential implications on manned space missions.

The Amyloid Cascade Hypothesis in Alzheimer’s Disease: Should We Change Our Thinking?

Old age increases the risk of Alzheimer’s disease (AD), the most common neurodegenerative disease, a devastating disorder of the human mind and the leading cause of dementia. Worldwide, 50 million people have the disease, and it is estimated that there will be 150 million by 2050. Today, healthcare for AD patients consumes 1% of the global economy. According to the amyloid cascade hypothesis, AD begins in the brain by accumulating and aggregating Aβ peptides and forming β-amyloid fibrils (Aβ42). However, in clinical trials, reducing Aβ peptide production and amyloid formation in the brain did not slow cognitive decline or improve daily life in AD patients. Prevention studies in cognitively unimpaired people at high risk or genetically destined to develop AD also have not slowed cognitive decline. These observations argue against the amyloid hypothesis of AD etiology, its development, and disease mechanisms. Here, we look at other avenues in the research of AD, such as the presenilin hypothesis, synaptic glutamate signaling, and the role of astrocytes and the glutamate transporter EAAT2 in the development of AD.

Highly synchronized cortical circuit dynamics mediate spontaneous pain in mice

Cortical neural dynamics mediate information processing for the cerebral cortex, which is implicated in fundamental biological processes such as vision and olfaction, in addition to neurological and psychiatric diseases. Spontaneous pain is a key feature of human neuropathic pain. Whether spontaneous pain pushes the cortical network into an aberrant state and, if so, whether it can be brought back to a "normal" operating range to ameliorate pain are unknown. Using a clinically relevant mouse model of neuropathic pain with spontaneous pain-like behavior, we report that orofacial spontaneous pain activated a specific area within the primary somatosensory cortex (S1), displaying synchronized neural dynamics revealed by intravital two-photon calcium imaging. This synchronization was underpinned by local GABAergic interneuron hypoactivity. Pain-induced cortical synchronization could be attenuated by manipulating local S1 networks or clinically effective pain therapies. Specifically, both chemogenetic inhibition of pain-related c-Fos-expressing neurons and selective activation of GABAergic interneurons significantly attenuated S1 synchronization. Clinically effective pain therapies including carbamazepine and nerve root decompression could also dampen S1 synchronization. More important, restoring a "normal" range of neural dynamics through attenuation of pain-induced S1 synchronization alleviated pain-like behavior. These results suggest that spontaneous pain pushed the S1 regional network into a synchronized state, whereas reversal of this synchronization alleviated pain.

Systematic Review of EEG Coherence in Alzheimer's Disease

BACKGROUND

Magnitude-squared coherence (MSCOH) is an electroencephalography (EEG) measure of functional connectivity. MSCOH has been widely applied to investigate pathological changes in patients with Alzheimer's disease (AD). However, significant heterogeneity exists between the studies using MSOCH.

OBJECTIVE

We systematically reviewed the literature on MSCOH changes in AD as compared to healthy controls to investigate the clinical utility of MSCOH as a marker of AD.

METHODS

We searched PubMed, Embase, and Scopus to identify studies reporting EEG MSCOH used in patients with AD. The identified studies were independently screened by two researchers and the data was extracted, which included cognitive scores, preprocessing steps, and changes in MSCOH across frequency bands.

RESULTS

A total of 35 studies investigating changes in MSCOH in patients with AD were included in the review. Alpha coherence was significantly decreased in patients with AD in 24 out of 34 studies. Differences in other frequency bands were less consistent. Some studies showed that MSCOH may serve as a diagnostic marker of AD.

CONCLUSION

Reduced alpha MSCOH is present in patients with AD and MSCOH may serve as a diagnostic marker. However, studies validating MSCOH as a diagnostic marker are needed.

Harnessing the potential of machine learning and artificial intelligence for dementia research

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

Determinants of approved acetylcholinesterase inhibitor response outcomes in Alzheimer's disease: relevance for precision medicine in neurodegenerative diseases

Acetylcholinesterase inhibitors (ChEI) are the global standard of care for the symptomatic treatment of Alzheimer's disease (AD) and show significant positive effects in neurodegenerative diseases with cognitive and behavioral symptoms. Although experimental and large-scale clinical evidence indicates the potential long-term efficacy of ChEI, primary outcomes are generally heterogeneous across outpatient clinics and regional healthcare systems. Sub-optimal dosing or slow tapering, heterogeneous guidelines about the timing for therapy initiation (prodromal versus dementia stages), healthcare providers' ambivalence to treatment, lack of disease awareness, delayed medical consultation, prescription of ChEI in non-AD cognitive disorders, contribute to the negative outcomes. We present an evidence-based overview of determinants, spanning genetic, molecular, and large-scale networks, involved in the response to ChEI in patients with AD and other neurodegenerative diseases. A comprehensive understanding of cerebral and retinal cholinergic system dysfunctions along with ChEI response predictors in AD is crucial since disease-modifying therapies will frequently be prescribed in combination with ChEI. Therapeutic algorithms tailored to genetic, biological, clinical (endo)phenotypes, and disease stages will help leverage inter-drug synergy and attain optimal combined response outcomes, in line with the precision medicine model.

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

Introduction

Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections).

Methods

Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used.

Results

Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants.

Discussion

In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.

Concurrent behavioral and electrophysiological longitudinal recordings for in vivo assessment of aging

Electrophysiological and behavioral alterations, including sleep and cognitive impairments, are critical components of age-related decline and neurodegenerative diseases. In preclinical investigation, many refined techniques are employed to probe these phenotypes, but they are often conducted separately. Herein, we provide a protocol for one-time surgical implantation of EMG wires in the nuchal muscle and a skull-surface EEG headcap in mice, capable of 9-to-12-month recording longevity. All data acquisitions are wireless, making them compatible with simultaneous EEG recording coupled to multiple behavioral tasks, as we demonstrate with locomotion/sleep staging during home-cage video assessments, cognitive testing in the Barnes maze, and sleep disruption. Time-course EEG and EMG data can be accurately mapped to the behavioral phenotype and synchronized with neuronal frequencies for movement and the location to target in the Barnes maze. We discuss critical steps for optimizing headcap surgery and alternative approaches, including increasing the number of EEG channels or utilizing depth electrodes with the system. Combining electrophysiological and behavioral measurements in preclinical models of aging and neurodegeneration has great potential for improving mechanistic and therapeutic assessments and determining early markers of brain disorders.

State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer's disease: Challenges and opportunities

Primary prevention trials have shifted their focus to the earliest stages of Alzheimer's disease (AD). Autopsy data indicates that the neuromodulatory subcortical systems' (NSS) nuclei are specifically vulnerable to initial tau pathology, indicating that these nuclei hold great promise for early detection of AD in the context of the aging brain. The increasing availability of new imaging methods, ultra-high field scanners, new radioligands, and routine deep brain stimulation implants has led to a growing number of NSS neuroimaging studies on aging and neurodegeneration. Here, we review findings of current state-of-the-art imaging studies assessing the structure, function, and molecular changes of these nuclei during aging and AD. Furthermore, we identify the challenges associated with these imaging methods, important pathophysiologic gaps to fill for the AD NSS neuroimaging field, and provide future directions to improve our assessment, understanding, and clinical use of in vivo imaging of the NSS.

Inter-Hospital Advanced and Mild Alzheimer's Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms

BACKGROUND

In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA).

OBJECTIVE

This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals.

METHODS

The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest.

RESULTS

The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis.

CONCLUSIONS

By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.

EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales.

OBJECTIVE

To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD.

METHODS

We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function.

RESULTS

SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power.

CONCLUSION

SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.