Electroencephalography and event‐related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease

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.

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.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study

Background and Objectives

Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods

This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk.

Results

There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort.

Discussion

The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

Dopaminergic and norepinephrinergic modulation of endogenous event-related potentials: A systematic review and meta-analysis

Event-related potentials (ERPs) represent the cortical processing of sensory, motor or cognitive functions invoked by particular events or stimuli. A current theory posits that the catecholaminergic neurotransmitters dopamine (DA) and norepinephrine (NE) modulate a number of endogenous ERPs during various cognitive processes. This manuscript aims to evaluate a leading neurotransmitter hypothesis with a systematic overview and meta-analysis of pharmacologic DA and NE manipulation of specific ERPs in healthy subjects during executive function. Specifically, the frontally-distributed P3a, N2, and Ne/ERN (or error-related negativity) are supposedly modulated primarily by DA, whereas the parietally-distributed P3b is thought to be modulated by NE. Based on preceding research, we refer to this distinction between frontally-distributed DA-sensitive and parietally-distributed NE-sensitive ERP components as the Extended Neurobiological Polich (ENP) hypothesis. Our systematic review and meta-analysis indicate that this distinction is too simplistic and many factors interact with DA and NE to influence these specific ERPs. These may include genetic factors, the specific cognitive processes engaged, or elements of study design, i.e. session or sequence effects or data-analysis strategies.

Acupuncture on mild cognitive impairment: A systematic review of neuroimaging studies

Mild cognitive impairment (MCI) is a multifactorial and complex central neurodegenerative disease. Acupuncture appears to be an effective method for cognitive function improvement in MCI patients. Neural plasticity remaining in the MCI brain implies that acupuncture-associated benefits may not be limited to the cognitive function. Instead, neurological alternations in the brain play a vital role in corresponding to the cognitive improvement. However, previous studies have mainly focused on the effects of cognitive function, leaving neurological findings relatively unclear. This systematic review summarized existing studies that used various brain imaging techniques to explore the neurological effect regarding acupuncture use for MCI treatment. Potential neuroimaging trials were searched, collected, and identified independently by two researchers. Four Chinese databases, four English databases, and additional sources were searched to identify studies reporting the use of acupuncture for MCI from the inception of databases until 1 June 2022. Methodological quality was appraised using the Cochrane risk-of-bias tool. In addition, general, methodological, and brain neuroimaging information was extracted and summarized to investigate the potential neural mechanisms by which acupuncture affects patients with MCI. In total, 22 studies involving 647 participants were included. The methodological quality of the included studies was moderate to high. The methods used included functional magnetic resonance imaging, diffusion tensor imaging, functional near-infrared spectroscopy, and magnetic resonance spectroscopy. Acupuncture-induced brain alterations observed in those patients with MCI tended to be observable in the cingulate cortex, prefrontal cortex, and hippocampus. The effect of acupuncture on MCI may play a role in regulating the default mode network, central executive network, and salience network. Based on these studies, researchers could extend the recent research focus from the cognitive domain to the neurological level. Future researches should develop additional relevant, well-designed, high-quality, and multimodal neuroimaging researches to detect the effects of acupuncture on the brains of MCI patients.

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Purpose

Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR).

Participants and Methods

The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN).

Results

The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively.

Conclusion

The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

Sex, Age, and Regional Differences in CHRM1 and CHRM3 Genes Expression Levels in the Human Brain Biopsies: Potential Targets for Alzheimer's Disease-related Sleep Disturbances

BACKGROUND

Cholinergic hypofunction and sleep disturbance are hallmarks of Alzheimer's disease (AD), a progressive disorder leading to neuronal deterioration. Muscarinic acetylcholine receptors (M1-5 or mAChRs), expressed in hippocampus and cerebral cortex, play a pivotal role in the aberrant alterations of cognitive processing, memory, and learning, observed in AD. Recent evidence shows that two mAChRs, M1 and M3, encoded by CHRM1 and CHRM3 genes, respectively, are involved in sleep functions and, peculiarly, in rapid eye movement (REM) sleep.

METHODS

We used twenty microarray datasets extrapolated from post-mortem brain tissue of nondemented healthy controls (NDHC) and AD patients to examine the expression profile of CHRM1 and CHRM3 genes. Samples were from eight brain regions and stratified according to age and sex.

RESULTS

CHRM1 and CHRM3 expression levels were significantly reduced in AD compared with ageand sex-matched NDHC brains. A negative correlation with age emerged for both CHRM1 and CHRM3 in NDHC but not in AD brains. Notably, a marked positive correlation was also revealed between the neurogranin (NRGN) and both CHRM1 and CHRM3 genes. These associations were modulated by sex. Accordingly, in the temporal and occipital regions of NDHC subjects, males expressed higher levels of CHRM1 and CHRM3, respectively, than females. In AD patients, males expressed higher levels of CHRM1 and CHRM3 in the temporal and frontal regions, respectively, than females.

CONCLUSION

Thus, substantial differences, all strictly linked to the brain region analyzed, age, and sex, exist in CHRM1 and CHRM3 brain levels both in NDHC subjects and in AD patients.

Effects of the Duration of a Resting-State EEG Recording in Healthy Aging and Mild Cognitive Impairment

INTRODUCTION

The recording of resting-state EEG may provide a means to predict early cognitive decline associated with mild cognitive impairment (MCI). Previous studies have typically used very short recording times to avoid a confound with drowsiness that may occur in longer recordings. The effects of a longer recording have not however been systematically examined.

METHODS

Eyes-closed resting-state EEG activity was recorded in 40 older adult participants (20 healthy older adults and 20 people with MCI). The recording period was a relatively long 6 minutes, divided into two equal 3-minute halves to determine if drowsiness will be more apparent as the recording progresses. The participants also completed standardized neuropsychological tasks that assessed global cognition (Montreal Cognitive Assessment) and memory (California Verbal Learning Test, Second Edition). A spectral analysis was performed on both short (2 seconds) and long (8 seconds) segments in both 3-minute halves.

RESULTS

No differences in power density for any of the EEG frequency bands were found between the 2 halves of the study for either group. There was little evidence of increased drowsiness in the second half of the study even when frequency resolution was increased with the 8-second segmentation. Theta power density was overall larger for people with MCI compared to healthy older adults. A negative correlation was also observed between theta power and global cognition in healthy older adults.

CONCLUSIONS

The present results indicate that longer resting-state EEG recording can be reliably employed without increased risk of drowsiness.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Habituation of Somatosensory Evoked Potentials in Patients with Alzheimer’s Disease and Those with Vascular Dementia

Background and Objectives: The most prevalent dementia are Alzheimer’s disease and vascular dementia. There is evidence that cortical synaptic function may differ in these two conditions. Habituation of cortical responses to repeated stimuli is a well-preserved phenomenon in a normal brain cortex, related to an underlying mechanism of synaptic efficacy regulation. Lack of habituation represents a marker of synaptic dysfunction. The purpose of this study was to assess the habituation of somatosensory evoked potentials (SEPs) in 29 patients affected by mild-to-moderate Alzheimer’s disease (AD-type) or vascular (VD-type) dementia. Materials and Methods: All patients underwent a clinical history interview, neuropsychological evaluation, and neuroimaging examination. SEPs were elicited by electrical stimulation of the right median nerve at the wrist. Six-hundred stimuli were delivered, and cortical responses divided in three blocks of 200. Habituation was calculated by measuring changes of N20 amplitude from block 1 to block 3. SEP variables recorded in patients were compared with those recorded in 15 age- and gender-matched healthy volunteers. Results: SEP recordings showed similar N20 amplitudes in AD-type and VD-type patients in block 1, that were higher than those recorded in controls. N20 amplitude decreased from block 1 to block 3 (habituation) in normal subjects and in VD-type patients, whereas in AD-type patients it remained unchanged (lack of habituation). Conclusions: The findings suggest that neurophysiologic mechanisms of synaptic efficacy that underneath habituation are impaired in patients with AD-type dementia but not in patients with VD-type dementia. SEPs habituation may contribute to early distinction of Alzheimer’s disease vs. vascular dementia.

Brain-printing biometrics underlying mechanism as an early diagnostic technique for Alzheimer's disease neurodegenerative type

Early diagnosis of neurodegenerative diseases, especially Alzheimer's disease (AD), is essential for implementing the appropriate treatment protocols and controlling disease progression. Early AD diagnosis helps patients achieve the best therapeutic outcomes, lessening irreversible neurodegenerative damage and severe cognitive decline. The measurement of brain waves and structural modifications, including gray/white matter and brain volume, have recently been considered a promising approach for brain biometrics because of the inherent specificity, degree of confidentiality, and reproducibility. Brain printing biometrics (BPB) is thus becoming more commonly considered as tool for early AD detection. This review proposes using BPB as a tool for the detection of AD prior to the appearance of persistent hallmark depositions, including Aβ and tau protein aggregations in different brain regions. It also describes BPB authentication, a method of implementation, as well as potential outcomes.

Assessment of event-related evoked potentials and China-Wechsler intelligence scale for cognitive dysfunction in children with obstructive sleep apnea

OBJECTIVE

To explore the relationship between obstructive sleep apnea (OSA) and cognitive impairment by combining event-related evoked potentials (ERPs) and China-Wechsler Younger Children Scale (C-WISC) in children with sleep-disordered breathing (SDB) with vs. without OSA.

METHODS

This was a retrospective case-control study of all consecutive children (n = 148) with adenoid tonsil hypertrophy between July 2017 and March 2019 at the Hospital.

RESULTS

The children were divided into the OSA (n = 102) and non-OSA (n = 46) groups. The apnea-hypopnea index (AHI), obstructive apnea index (OAI), and obstructive apnea-hypopnea index (OAHI) in the OSA group were elevated compared with those of the non-OSA group (all P < 0.001). The mean oxygen saturation (SaO₂) and SaO₂ nadir were lower in the OSA group compared with the non-OSA group (both P < 0.001). The respiratory arousal index (RAI) values in the OSA group were larger than those of the non-OSA group (P < 0.001). The P300 and N100 latencies in the OSA group were longer than those of the non-OSA group (both P < 0.001). Pearson's correlation analysis revealed correlations of the P300 peak latency with full-scale intelligence quotient (FIQ) (P < 0.001 and r = -0.527), verbal intelligence quotient (VIQ) (P < 0.001 and r = -0.448), and performance intelligence quotient (PIQ) (P < 0.001 and r = -0.515). There was a correlation between the N100 peak latency and PIQ (P = 0.026 and r = -0.183).

CONCLUSION

ERPs, as an objective measurement, might help assess cognitive impairment in children with OSA.

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background:

Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals.

Objective:

Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis.

Methods:

Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed.

Results:

Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08).

Conclusion:

The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

When the time is right: Temporal dynamics of brain activity in healthy aging and dementia

Brain activity and communications are complex phenomena that dynamically unfold over time. However, in contrast with the large number of studies reporting neuroanatomical differences in activation relative to young adults, changes of temporal dynamics of neural activity during normal and pathological aging have been grossly understudied and are still poorly known. Here, we synthesize the current state of knowledge from MEG and EEG studies that aimed at specifying the effects of healthy and pathological aging on local and network dynamics, and discuss the clinical and theoretical implications of these findings. We argue that considering the temporal dynamics of brain activations and networks could provide a better understanding of changes associated with healthy aging, and the progression of neurodegenerative disease. Recent research has also begun to shed light on the association of these dynamics with other imaging modalities and with individual differences in cognitive performance. These insights hold great potential for driving new theoretical frameworks and development of biomarkers to aid in identifying and treating age-related cognitive changes.

Event-related brain potential indexes provide evidence for some decline in healthy people with subjective memory complaints during target evaluation and response inhibition processing

In the preclinical stage of the Alzheimer's disease (AD) continuum, subjects report subjective memory complaints (SMCs), although with the absence of any objective decline, and have a higher risk of progressing to dementia than the general population. Early identification of this stage therefore constitutes a major focus of current AD research, to enable early intervention. In this study, healthy adult participants with high and low SMCs (HSMCs and LSMCs) performed a Go/NoGo task during electroencephalogram (EEG) recording. Relative to LSMC participants, HSMC participants performed the task slower (longer reaction times) and showed changes in the event-related potential (ERP) components associated with response preparation (lower readiness potential -RP- amplitude in the Go condition), and also related to response inhibition processes (lower N2-P3 amplitude in the NoGo condition). In addition, HSMC participants showed lower Go-N2 and NoGo-N2 peak-to-baseline amplitudes, however these results seem to be influenced by a negative tendency overlapping stimulus-related waveforms. The declines observed in this study are mostly consistent with those observed in aMCI participants, supporting the notion of the AD continuum regarding SMC state.

Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.

Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.

Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.

Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.

Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

Psycho-Electrophysiological Benefits of Forest Therapies Focused on Qigong and Walking with Elderly Individuals

We developed two distinct forest therapy programs (FTPs) and compared their effects on dementia prevention and related health problems for older adults. One was focused on Qigong practice in the forest (QP) and the other involved active walking in the forest (WP). Both FTPs consisted of twelve 2-h sessions over six weeks and were conducted in an urban forest. We obtained data from 25, 18, and 26 participants aged 65 years or above for the QP, WP, and control groups, respectively. Neuropsychological scores via cognition (MoCA), geriatric depression (GDS) and quality of life (EQ-5D), and electrophysiological variables (electroencephalography, bioimpedance, and heart rate variability) were measured. We analyzed the intervention effects with a generalized linear model. Compared to the control group, the WP group showed benefits in terms of neurocognition (increases in the MoCA score, and alpha and beta band power values in the electroencephalogram), sympathetic nervous activity, and bioimpedance in the lower body. On the other hand, the QP group showed alleviated depression and an increased bioimpedance phase angle in the upper body. In conclusion, both active walking and Qigong in the forest were shown to have distinctive neuropsychological and electrophysiological benefits, and both had beneficial effects in terms of preventing dementia and relieving related health problems for elderly individuals.

Using Muse: Rapid Mobile Assessment of Brain Performance

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue - a neural state that is associated with an increased incidence of errorful performance - is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.

Face Recognition Deficits in a Patient With Alzheimer's Disease: Amnesia or Agnosia? The Importance of Electrophysiological Markers for Differential Diagnosis

Face recognition deficits are frequently reported in Alzheimer's disease (AD) and often attributed to memory impairment. However, it has been hypothesized that failure in identifying familiar people could also be due to deficits in higher-level perceptual processes, since there is evidence showing a reduced inversion effect for faces but not for cars in AD. To address the involvement of these higher processes, we investigated event-related potential (ERP) neural correlates of faces in a patient with AD showing a face recognition deficit. Eight healthy participants were tested as a control group. Participants performed different tasks following the stimulus presentation. In experiment 1, they should indicate whether the stimulus was either a face or a house or a scrambled image. In experiments 2 and 3, they should discriminate between upright and inverted faces (in experiment 2, stimuli were faces with neutral or fearful expressions, while in experiment 3, stimuli were famous or unfamiliar faces). Electrophysiological results reveal that the typical face-specific modulation of the N170 component, which is thought to reflect the structural encoding of faces, was not present in patient MCG, despite being affected by the emotional content of the face implicitly processed by MCG. Conversely, the N400 component, which is thought to reflect the recruitment of the memory trace of the face identity, was found to be implicitly modulated in MCG. These results may identify a possible role for gnosic processes in face recognition deficits in AD and suggest the importance of adopting an integrated approach to the AD diagnosis while considering electrophysiological markers.

Can electrophysiological tests be used as screening tools in detection of cognitive impairment in obstructive sleep apnea hypopnea syndrome?

Background

One of the most prevalent sleep disorders is obstructive sleep apnea (OSA). Patients with OSA have increased risk of car and work place accidents which may be attributed to cognitive impairment due to poor sleep quality. Thus, there is an increasing need to implement simple, reliable and rapid screening for cognitive impairment in OSA patients.

Objectives

To assess auditory event-related potentials (ERPs) and quantitative electroencephalography (QEEG) as screening tools for detection of cognitive impairment in OSA patients taking circadian effects into consideration.

Subjects and methods

Twenty OSA patients as well as 20 gender- and age-matched volunteers were subjected to trail making test (TMT), auditory ERPs and QEEG before and after at least 6 h polysomnographic recording. Correlating electrophysiological tools with TMT and clinical parameters was done as well

Results

Statistically significant changes in P300 latency and reaction time, in addition to occipital alpha power were noted in cases; however, TMT failed to detect such changes. Electrophysiological results were not constantly correlated to clinical parameters.

Conclusion

Auditory ERPs and QEEG before and after polysomnography can be of benefit as cognitive screening test for suspected cognitive impairment in OSA patients.

Diabetic patients are deficient in intentional visuospatial learning and show different learning-related patterns of theta and gamma EEG activity

Introduction: We hypothesized that diabetic patients without mild cognitive impairment would present deficiencies in visuospatial incidental/intentional memory processing and alterations in the underlying EEG alpha, theta and gamma patterns.Methods: Non-diabetic, diabetic-controlled, and diabetic-uncontrolled patients underwent a visuospatial incidental-intentional memory test under simultaneous recording of temporal, parietal, and frontal EEG. The test required patients to solve a maze, with eight objects irrelevant to the task, embedded in it, after an interference instruction, participants were asked to recall the positions of the objects (incidental test). Finally, the participants were explicitly told to study the object positions, and then were asked to recall the objects again (intentional test). Power from baseline, incidental learning, incidental memory, and intentional learning conditions was obtained in alpha, theta, and low-gamma bands. Comparisons were made across groups and conditions for each band, with age, sex, and years from the diagnosis as covariates (ANCOVA with blocking).Results: Diabetic patients showed spared incidental but deficient intentional visuospatial learning. Uncontrolled patients showed a more profound intentional learning deficit as they scored similar numbers of correct positions under incidental and intentional conditions; whereas, non-diabetic and diabetic-controlled patients increased their number after the intentional study. Non-diabetic participants showed increased power during intentional learning compared with the baseline condition in frontal theta, frontoparietal gamma (Fp2 and P4) and frontal alpha (F4) bands; whereas in diabetic patients the power increased in the theta band, in T5 (uncontrolled) and T5 and F7 (controlled).Conclusions: Diabetic patients without mild cognitive impairment show deficient intentional visuospatial learning which was worse in uncontrolled patients. Theta and gamma power increased in healthy participants during intentional learning principally in frontal areas. These EEG power changes were absent in diabetic patients. The reduced accuracy of diabetic patients in intentional visuospatial learning was associated with different EEG learning-related patterns.

A Multilevel View of the Development of Alzheimer's Disease

Every year the Alzheimer's Association publishes a report that provides facts and figures indicating the public health, social and economic impact of Alzheimer's disease (AD). In addition, there are a number of reviews on the disease for general readers. Also, at congresses, AD is analyzed at different but not always related levels, leading to an "elephant as seen by blind men situation" for many of the participants. The review presented herein seeks to provide readers with a holistic view of how AD develops from various perspectives: the whole human organism, brain, circuits, neurons, cellular hallmarks, and molecular level.

Abnormalities of resting-state EEG in patients with prodromal and overt dementia with Lewy bodies: Relation to clinical symptoms

OBJECTIVE

Here we tested if cortical sources of resting state electroencephalographic (rsEEG) rhythms may differ in sub-groups of patients with prodromal and overt dementia with Lewy bodies (DLB) as a function of relevant clinical symptoms.

METHODS

We extracted clinical, demographic and rsEEG datasets in matched DLB patients (N = 60) and control Alzheimer's disease (AD, N = 60) and healthy elderly (Nold, N = 60) seniors from our international database. The eLORETA freeware was used to estimate cortical rsEEG sources.

RESULTS

As compared to the Nold group, the DLB and AD groups generally exhibited greater spatially distributed delta source activities (DLB > AD) and lower alpha source activities posteriorly (AD > DLB). As compared to the DLB "controls", the DLB patients with (1) rapid eye movement (REM) sleep behavior disorders showed lower central alpha source activities (p < 0.005); (2) greater cognitive deficits exhibited higher parietal and central theta source activities as well as higher central, parietal, and occipital alpha source activities (p < 0.01); (3) visual hallucinations pointed to greater parietal delta source activities (p < 0.005).

CONCLUSIONS

Relevant clinical features were associated with abnormalities in spatial and frequency features of rsEEG source activities in DLB patients.

SIGNIFICANCE

Those features may be used as neurophysiological surrogate endpoints of clinical symptoms in DLB patients in future cross-validation prospective studies.

Electroencephalographic prediction of global and domain specific cognitive performance of clinically active Australian Nurses

OBJECTIVE

To investigate the relationship between EEG activity and the global and domain specific cognitive performance of healthy nurses, and determine the predictive capabilities of these relationships.

APPROACH

Sixty-four nurses were recruited for the present study, and data from 61 were utilised in the present analysis. Global and domain specific cognitive performance of each participant was assessed psychometrically using the Mini-mental state exam and the Cognistat, and a 32-lead monopolar EEG was recorded during a resting baseline phase and an active phase in which participants completed the Stroop test.

MAIN RESULTS

Global cognitive performance was successfully predicted (81%-85% of variance) by a combination of fast wave activity variables in the alpha, beta and theta frequency bands. Interestingly, predicting domain specific performance had varying degrees of success (42%-99% of the variance predicted) and relied on combinations of both slow and fast wave activity, with delta and gamma activity predicting attention performance; delta, theta, and gamma activity predicting memory performance; and delta and beta variables predicting judgement performance.

SIGNIFICANCE

Global and domain specific cognitive performance of Australian nurses may be predicted with varying degrees of success by a unique combination of EEG variables. These proposed models image transitory cognitive declines and as such may prove useful in the prediction of early cognitive impairment, and may enable better diagnosis, and management of cognitive impairment.

Blood-brain barrier dysfunction in aging induces hyperactivation of TGFβ signaling and chronic yet reversible neural dysfunction

Aging involves a decline in neural function that contributes to cognitive impairment and disease. However, the mechanisms underlying the transition from a young-and-healthy to aged-and-dysfunctional brain are not well understood. Here, we report breakdown of the vascular blood-brain barrier (BBB) in aging humans and rodents, which begins as early as middle age and progresses to the end of the life span. Gain-of-function and loss-of-function manipulations show that this BBB dysfunction triggers hyperactivation of transforming growth factor-β (TGFβ) signaling in astrocytes, which is necessary and sufficient to cause neural dysfunction and age-related pathology in rodents. Specifically, infusion of the serum protein albumin into the young rodent brain (mimicking BBB leakiness) induced astrocytic TGFβ signaling and an aged brain phenotype including aberrant electrocorticographic activity, vulnerability to seizures, and cognitive impairment. Furthermore, conditional genetic knockdown of astrocytic TGFβ receptors or pharmacological inhibition of TGFβ signaling reversed these symptomatic outcomes in aged mice. Last, we found that this same signaling pathway is activated in aging human subjects with BBB dysfunction. Our study identifies dysfunction in the neurovascular unit as one of the earliest triggers of neurological aging and demonstrates that the aging brain may retain considerable latent capacity, which can be revitalized by therapeutic inhibition of TGFβ signaling.

Assessing the Retest Reliability of Prefrontal EEG Markers of Brain Rhythm Slowing in the Eyes-Closed Resting State

Objective. We examined whether prefrontal lobe EEG markers of slower brain rhythms, which are correlated with functional brain aging, can reliably reflect those of other brain lobes, as measured by a multichannel device. Methods. EEG measurements were taken of 112 healthy individuals aged 20 to 69 years in the eyes-closed resting state. A 5-minute measurement was taken at 8 regions (Fp1, Fp2, F3, F4, T3, T4, O1, O2). Indices (median frequency [MDF], peak frequency [PF]) that quantitatively reflect the characteristics of EEG slowing, and traditional commonly used spectral indices (absolute powers as delta, theta, alpha, beta, and relative power as alpha-to-theta ratio [ATR]), were extracted from the EEG signals. For these indices, the differences between the prefrontal lobe and other areas were analyzed and the test-retest reproducibility was investigated. Results. The EEG slowing indicators showed high conformity over all brain lobes and stable reproducibility. On the other hand, the typical EEG spectral indicators delta, theta, alpha, beta, and ATR differed between brain regions. Conclusion. It was found that EEG slowing markers, which were used for assessing the aging or degeneration of brain functions, could be reliably extracted from a prefrontal EEG alone. Significance. These findings suggest that EEG prefrontal markers may reflect markers of other brain regions when a multi-channel device is used. Thus, this method may constitute a low-cost, wearable, wireless, easily accessible, and noninvasive tool for neurological assessment that could be used in the early detection of cognitive decline and in the prevention of dementia.

Negative effect of methyl bromide fumigation work on the central nervous system

Methyl bromide (MB) is a fumigant that has been widely used for killing pests on plants in trade, soils, and structures worldwide due to its excellent permeability and insecticidal effect; however, MB should be replaced because it is an ozone-depleting substance. It is well-known that MB is highly toxic and hazardous to workers, but the effects of exposure in asymptomatic workers have not been explored. The purpose of this study is to investigate the impact of MB fumigation on the health of fumigators at a sensitive level. The electroencephalogram (EEG) and urinary bromide ion levels of 44 fumigators (the study group) and 20 inspectors (the control) were measured before and after fumigation work from February to August 2019 in Busan, Korea. The mean post-work concentration of bromide ion (18.311 μg/mg CRE) in the fumigators was significantly increased from the pre-work level (7.390 μg/mg CRE) (P<0.001). The fumigator post-work median frequencies (MDF) and alpha-to-theta ratios (ATR) of EEG index were significantly decreased compared to the pre-work values (P<0.05 for all indices). In contrast, there were no significant differences in inspector EEG indices and urinary bromide ion. The urinary bromide ion levels in all the subjects were negatively correlated with MDF (P = 0.032). In conclusion, fumigators’ EEG indices and urinary bromide ion suggested that occupational exposure to MB negatively affected the health of workers, although the workers were asymptomatic.

Longitudinal changes in EEG power, sleep cycles and behaviour in a tau model of neurodegeneration

Background

Disturbed sleep is associated with cognitive decline in neurodegenerative diseases such as Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The progressive sequence of how neurodegeneration affects aspects of sleep architecture in conjunction with behavioural changes is not well understood.

Methods

We investigated changes in sleep architecture, spectral power and circadian rhythmicity in the tet-off rTg4510 mouse overexpressing human P301L tau within the same subjects over time. Doxycycline-induced transgene-suppressed rTg4510 mice, tTa carriers and wild-type mice were used as comparators. Spectral power and sleep stages were measured from within the home cage environment using EEG electrodes. In addition, locomotor activity and performance during a T-maze task were measured.

Results

Spectral power in the delta and theta bands showed a time-dependent decrease in rTg4510 mice compared to all other groups. After the initial changes in spectral power, wake during the dark period increased whereas NREM and number of REM sleep bouts decreased in rTg4510 compared to wild-type mice. Home cage locomotor activity in the dark phase significantly increased in rTg4510 compared to wild-type mice by 40 weeks of age. Peak-to-peak circadian rhythm amplitude and performance in the T-maze was impaired throughout the experiment independent of time. At 46 weeks, rTG4510 mice had significant degeneration in the hippocampus and cortex whereas doxycycline-treated rTG4510 mice were protected. Pathology significantly correlated with sleep and EEG outcomes, in addition to locomotor and cognitive measures.

Conclusions

We show that reduced EEG spectral power precedes reductions in sleep and home cage locomotor activity in a mouse model of tauopathy. The data shows increasing mutant tau changes sleep architecture, EEG properties, behaviour and cognition, which suggest tau-related effects on sleep architecture in patients with neurodegenerative diseases.

Weighted network measures reveal differences between dementia types: An EEG study

The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non‐invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age‐matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior–anterior patterns within the β‐band and greater network segregation within the θ‐band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β‐ and θ‐band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α‐band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.

Traditional Korean Medicine-Based Forest Therapy Programs Providing Electrophysiological Benefits for Elderly Individuals

We aimed to develop forest therapy programs (FTPs) to prevent dementia and related health problems in the elderly population, with the assumption that health benefits are FTP-type specific and depend on the participant's psychophysiological traits. For this purpose, we developed two distinct FTPs, namely, a guided-breathing meditation program (BP) and a walking program (WP); we adopted the approach of Sasang constitutional (SC) medicine, which categorizes individuals into one of three SC types (SC1, SC2, or SC3) for medical care. The FTPs ran 11 sessions over 11 weeks. We recruited 29/31/28 participants who were 65 years of age or older for the BP/WP/control groups, respectively; obtained electrophysiological measurements via electroencephalogram (EEG), heart rate variability (HRV), and bioimpedance; and analyzed the intervention effects with analysis of covariance. Compared with the control, the BP and WP resulted in benefits for neural activity and parasympathetic nervous activity (PNA), respectively, and both FTPs yielded distinct beneficial effects on bioimpedance. Constitution-specific effects were also present. The SC1- and SC2-type participants gained positive effects in neural activity from the BP and WP, respectively. The SC3-type participants showed improvements in PNA from the WP. In conclusion, for older individuals, both programs conferred health benefits that would help prevent dementia, and the benefits were program-specific and constitution-specific.

Cerebrospinal Fluid, Imaging, and Physiological Biomarkers in Dementia With Lewy Bodies

Dementia with Lewy bodies is a progressive neurodegenerative disorder, clinically characterized by gradual cognitive impairment and fluctuating cognition, behavioral changes and recurrent visual hallucinations, and autonomic function and movement symptoms in the type of parkinsonism. It is the second most common type of dementia in the Western world after Alzheimer disease. Over the last 20 years, many neurophysiological, neuroimaging, and cerebrospinal fluid (CSF) biomarkers have been described toward a better discrimination between dementia with Lewy bodies, Alzheimer disease, and other neurodegenerative conditions.In the present review, we aim to describe the neurophysiological, imaging, and CSF biomarkers in dementia with Lewy bodies and to question whether they could be reliable tools for the clinical practice.

Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals

We investigated whether cognitive decline could be explained by resting-state electroencephalography (EEG) biomarkers measured in prefrontal regions that reflect the slowing of intrinsic EEG oscillations. In an aged population dwelling in a rural community (total = 496, males = 165, females = 331), we estimated the global cognitive decline using the Mini-Mental State Examination (MMSE) and measured resting-state EEG parameters at the prefrontal regions of Fp1 and Fp2 in an eyes-closed state. Using a tertile split method, the subjects were classified as T3 (MMSE 28-30, N = 162), T2 (MMSE 25-27, N = 179), or T1 (MMSE ≤ 24, N = 155). The EEG slowing biomarkers of the median frequency, peak frequency and alpha-to-theta ratio decreased as the MMSE scores decreased from T2 to T1 for both sexes (-5.19 ≤ t-value ≤ -3.41 for males and -7.24 ≤ t-value ≤ -4.43 for females) after adjusting for age and education level. Using a double cross-validation procedure, we developed a prediction model for the MMSE scores using the EEG slowing biomarkers and demographic covariates of sex, age and education level. The maximum intraclass correlation coefficient between the MMSE scores and model-predicted values was 0.757 with RMSE = 2.685. The resting-state EEG biomarkers showed significant changes in people with early cognitive decline and correlated well with the MMSE scores. Resting-state EEG slowing measured in the prefrontal regions may be useful for the screening and follow-up of global cognitive decline in elderly individuals.

Insights into cognitive decline in spinocerebellar Ataxia type 2: a P300 event-related brain potential study

BACKGROUND

Cognitive decline is a common non-motor feature characterizing Spinocerebellar Ataxia type 2 (SCA2) during the prodromal stage, nevertheless a reduced number of surrogate biomarkers of these alterations have been described.

OBJECTIVE

To provide insights into cognitive dysfunction in SCA2 patients using P300 event-related potentials (ERP) and to evaluate these measures as biomarkers of the disease.

METHODS

A cross-sectional study was performed with 30 SCA2 patients, 20 preclinical carriers and 33 healthy controls, who underwent visual, auditory P300 ERPs, and neurological examinations and ataxia scoring.

RESULTS

SCA2 patients showed significant increase in P300 latencies and decrease of P300 amplitudes for visual and auditory stimuli, whereas preclinical carriers exhibit a less severe, but significant prolongation of P300 latencies. Multiple regression analyses disclosed a significant effect of SARA score on visual P300 abnormalities in patients as well as of the time to ataxia onset on visual P300 latencies in preclinical carriers.

CONCLUSIONS

This paper demonstrated the role of P300 ERP for the study of attentional, discriminative and working memory abnormalities in SCA2 patients and for the search of surrogate biomarkers from prodromal to the symptomatic stages. Moreover, our findings provide psychophysiological evidences supporting the cerebellar involvement in cognitive processes and allows us to identify promising outcome measures for future trials focusing on cognitive dysfunction.

The importance of age in the search for ERP biomarkers of aMCI

Alzheimer's Disease (AD) has become a major health issue in recent decades, and there is now growing interest in amnestic mild cognitive impairment (aMCI), an intermediate stage between healthy aging and dementia, usually AD. Event-related brain potential (ERP) studies have sometimes failed to detect differences between aMCI and control participants in the Go-P3 (or P3b, related to target classification processes in a variety of tasks) and NoGo-P3 (related to response inhibition processes, mainly in Go/NoGo tasks) ERP components. The aim of the present study was to evaluate whether the age factor, which is not usually taken into account in ERP studies, modulates group differences in these components. With this aim, we divided two groups of volunteer participants, 34 subjects with aMCI (51-87 years) and 31 controls (52-86 years), into two age subgroups: 69 years or less and 70 years or more. We recorded brain activity while the participants performed a distraction-attention auditory-visual (AV) task. Task performance was poorer in the older than in the younger group, and aMCI participants produced fewer correct responses than the matched controls; but no interactions of the age and group factors on performance were found. On the other hand, Go-P3 and NoGo-N2 latencies were longer in aMCI participants than in controls only in the younger subgroup. Thus, the younger aMCI participants categorized the Go stimuli in working memory and processed the NoGo stimuli (which required response inhibition) slower than the corresponding controls. Finally, the combination of the number of hits, Go-P3 latency and NoGo-N2 latency yielded acceptable sensitivity and specificity scores (0.70 and 0.92, respectively) as regards distinguishing aMCI participants aged 69 years or less from the age-matched controls. The findings indicate age should be taken into account in the search for aMCI biomarkers.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers

OBJECTIVE

In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers.

APPROACH

Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20).

MAIN RESULTS

Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores.

SIGNIFICANCE

Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.

Personality and its Relationship to Depression and Cognition in Older Adults: Implications for Practice

OBJECTIVES

Personality is an important contributor to an individual's mental health and is consistently linked to the two most prevalent mental health conditions among older adults: dementia and depression. This review summarizes the current findings on personality and its association with cognitive decline and depression, as well as treatment outcomes and possible intervention strategies.

METHODS

Literature searches were conducted in Web of Science, PubMed, and PsycINFO. Search terms included 'personality traits,' 'depression,' 'treatment,' and 'older adults.'

RESULTS

Cognitive decline and depressive disorders are both associated with a common personality profile: high neuroticism, and low conscientiousness, extraversion, openness, and agreeableness. Across studies, the most consistent predictor of late-life depression, its course, and treatment outcomes is higher neuroticism.

CONCLUSIONS

Personality traits are associated with cognitive decline, as well as the diagnosis and course of late-life depression in older adults. However, formal personality assessment is not typically incorporated in clients' treatment plans, even though personality traits may influence treatment efficacy and outcomes.

CLINICAL IMPLICATIONS

Formal assessment of personality traits may be beneficial in terms of treatment adherence and outcomes for older adults. Tailored interventions that specifically target the client's strengths are likely to be well received by both clients and clinicians.

Combining EEG signal processing with supervised methods for Alzheimer's patients classification

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.

METHODS

In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods.

RESULTS

By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively.

CONCLUSIONS

Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.

Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis-implications for brain-computer interfacing

OBJECTIVE

For patients with amyotrophic lateral sclerosis (ALS) who are suffering from severe communication or motor problems, brain-computer interfaces (BCIs) can improve the quality of life and patient autonomy. However, current BCI systems are not as widely used as their potential and patient demand would let assume. This underutilization is a result of technological as well as user-based limitations but also of the comparatively poor performance of currently existing BCIs in patients with late-stage ALS, particularly in the locked-in state.

APPROACH

Here we review a broad range of electrophysiological studies in ALS patients with the aim to identify electrophysiological correlates of ALS-related neurodegeneration in motor and non-motor brain regions in to better understand potential neurophysiological limitations of current BCI systems for ALS patients. To this end we analyze studies in ALS patients that investigated basic sensory evoked potentials, resting-state and task-based paradigms using electroencephalography or electrocorticography for basic research purposes as well as for brain-computer interfacing. Main results and significance. Our review underscores that, similarly to mounting evidence from neuroimaging and neuropathology, electrophysiological measures too indicate neurodegeneration in non-motor areas in ALS. Furthermore, we identify an unexpected gap of basic and advanced electrophysiological studies in late-stage ALS patients, particularly in the locked-in state. We propose a research strategy on how to fill this gap in order to improve the design and performance of future BCI systems for this patient group.

Neurocognitive and Behavioral Indexes for Identifying the Amnestic Subtypes of Mild Cognitive Impairment

Early identification of amnestic mild cognitive impairment (aMCI) subtypes is important for early diagnosis and prognosis of Alzheimer’s disease. Healthy, single-domain (sdaMCI) and multiple-domain aMCI (mdaMCI) participants performed an auditory-visual distraction-attention task. Event-related brain potentials (ERPs) were recorded while the participants performed the task to evaluate Go/NoGo N2 and P3 ERP components. The results showed the expected behavioral and cognitive decline in mdaMCI participants relative to controls (fewer hits, longer reaction times [RTs], slightly smaller Go-N2 and NoGo-N2 amplitudes), while sdaMCI participants showed some decline (slightly longer RTs, smaller Go- and NoGo-N2 amplitudes) along with some unexpected results (a late positive slow wave, PSW) and good levels of execution. In addition, some of these parameters proved to be useful markers. Thus, the number of hits was the best marker for diagnosing mdaMCI participants (distinguishing them from controls, from sdaMCI participants, and from both groups together), while the PSW amplitude was the best marker for diagnosing sdaMCI participants (distinguishing them from controls, and from control & mdaMCI participants).

Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis

•

EEG slowing was evident in dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) and less in Alzheimer’s disease (AD) patients compared to controls.

•

Dominant rhythm variability was larger in AD but only correlated with cognitive fluctuations in DLB.

•

QEEG variables classified DLB and AD patients with high sensitivity and specificity.

Objective

We investigated for quantitative EEG (QEEG) differences between Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) patients and healthy controls, and for QEEG signatures of cognitive fluctuations (CFs) in DLB.

Methods

We analysed eyes-closed, resting state EEGs from 18 AD, 17 DLB and 17 PDD patients with mild dementia, and 21 age-matched controls. Measures included spectral power, dominant frequency (DF), frequency prevalence (FP), and temporal DF variability (DFV), within defined EEG frequency bands and cortical regions.

Results

DLB and PDD patients showed a leftward shift in the power spectrum and DF. AD patients showed greater DFV compared to the other groups. In DLB patients only, greater DFV and EEG slowing were correlated with CFs, measured by the clinician assessment of fluctuations (CAF) scale. The diagnostic accuracy of the QEEG measures was 94% (90.4–97.9%), with 92.26% (80.4–100%) sensitivity and 83.3% (73.6–93%) specificity.

Conclusion

Although greater DFV was only shown in the AD group, within the DLB group a positive DFV – CF correlation was found. QEEG measures could classify DLB and AD patients with high sensitivity and specificity.

Significance

The findings add to an expanding literature suggesting that EEG is a viable diagnostic and symptom biomarker in dementia, particularly DLB.

Brain oscillations reveal impaired novelty detection from early stages of Parkinson's disease

The identification of reliable biomarkers for early diagnosis and progression tracking of neurodegenerative diseases has become an important objective in clinical neuroscience in the last years. The P3a event-related potential, considered as the neurophysiological hallmark of novelty detection, has been shown to be reduced in Parkinson's disease (PD) and proposed as a sensitive measure for illness duration and severity. Our aim for this study was to explore for the first time whether impaired novelty detection could be observed through phase- and time-locked brain oscillatory activity at early PD. Twenty-seven patients with idiopathic PD at early stages (disease duration <5 years and Hoehn and Yahr stage <3) were included. A healthy control group (n = 24) was included as well. All participants performed an auditory involuntary attention task including frequent and deviant tones while a digital EEG was obtained. A neuropsychological battery was administered as well. Time-frequency representations of power and phase-locked oscillations and P3a amplitudes were compared between groups. We found a significant reduction of power and phase locking of slow oscillations (3-7 Hz) for deviant tones in the PD group compared to controls in the P3a time range (300-550 ms). Also, reduced modulation of late induced (not phase locked) alpha-beta oscillations (400-650 ms, 8-25 Hz) was observed in the PD group after deviant tones onset. The P3a amplitude was predicted by years of evolution in the PD group. Finally, while phase-locked slow oscillations were associated with task behavioral distraction effects, induced alpha-beta activity was related to cognitive flexibility performance. Our results show that novelty detection impairment can be identified in neurophysiological terms from very early stages of PD, and such impairment increases linearly as the disease progresses. Also, induced alpha-beta oscillations underlying novelty detection are related to executive functioning.

Increased Amplitude of the P3a ERP Component as a Neurocognitive Marker for Differentiating Amnestic Subtypes of Mild Cognitive Impairment

The event-related potential (ERP) technique has been shown to be useful for evaluating changes in brain electrical activity associated with different cognitive processes, particularly in Alzheimer's disease (AD). Longitudinal studies have shown that a high proportion of people with amnestic mild cognitive impairment (aMCI) go on to develop AD. aMCI is divided into two subtypes according to the presence of memory impairment only (single-domain aMCI: sdaMCI) or impairment of memory and other cognitive domains (multi-domain aMCI: mdaMCI). The main aim of this study was to examine the effects of sdaMCI and mdaMCI on the P3a ERP component associated with the involuntary orientation of attention toward unattended infrequent novel auditory stimuli. Participants performed an auditory-visual distraction-attention task, in which they were asked to ignore the auditory stimuli (standard, deviant, and novel) and to attend to the visual stimuli (responding to some of them: Go stimuli). P3a was identified in the Novel minus Standard difference waveforms, and reaction times (RTs) and hits (in response to Go stimuli) were also analyzed. Participants were classified into three groups: Control, 20 adults (mean age (M): 65.8 years); sdaMCI, 19 adults (M: 67 years); and mdaMCI, 11 adults (M: 71 years). In all groups, the RTs were significantly longer when Go stimuli were preceded by novel (relative to standard) auditory stimuli, suggesting a distraction effect triggered by novel stimuli; mdaMCI participants made significantly fewer hits than control and sdaMCI participants. P3a comprised two consecutive phases in all groups: early-P3a (e-P3a), which may reflect the orienting response toward the irrelevant stimuli, and late-P3a (l-P3a), which may be a correlate of subsequent evaluation of these stimuli. The e-P3a amplitude was significantly larger in mdaMCI than in sdaMCI participants, and the l-P3a amplitude was significantly larger in mdaMCI than in sdaMCI and Control participants, indicating greater involuntary capture of attention to unattended novel auditory stimuli and allocation of more attentional resources for the subsequent evaluation of these stimuli in mdaMCI participants. The e-P3a and l-P3a components showed moderate to high sensitivity and specificity for distinguishing between groups, suggesting that both may represent optimal neurocognitive markers for differentiating aMCI subtypes.