EEG in Silent Small Vessel Disease: sLORETA Mapping Reveals Cortical Sources of Vascular Cognitive Impairment No Dementia in the Default Mode Network

A Review: Visuospatial Dysfunction in Patients with the Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) impairs visuospatial function, and this is one of the most obvious areas of cognitive impairment in CSVD. So, recognizing, monitoring, and treating visuospatial dysfunction are all important to the prognosis of CSVD. This review discussed the anatomical and pathological mechanisms, clinical recognition (scales, imaging, and biomarkers), and treatment of cognitive impairment especially visuospatial dysfunction in CSVD.

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.

The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment

Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.

Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer's Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.

A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique

Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose of base feature extraction, wake-sleep and rapid eye movement (REM) and non- rapid eye movement (NREM) models were developed by extracting defining features from mouse-derived electromyogram (EMG) and electroencephalogram (EEG) signals, respectively. The wake-sleep model and REM-NREM sleep model were integrated into three different algorithms including a rule-based integration approach, an ensemble stacking approach, and a multimodal with fine-tuning approach. The deep learning algorithm assessing sleep stages in animal experiments by the multimodal with fine-tuning approach showed high potential for increasing accuracy in sleep stage scoring in mice and promoting sleep research.

EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

Evaluation of SAMP8 Mice as a Model for Sleep-Wake and Rhythm Disturbances Associated with Alzheimer's Disease: Impact of Treatment with the Dual Orexin (Hypocretin) Receptor Antagonist Lemborexant

Background:

Many patients with Alzheimer’s disease (AD) display circadian rhythm and sleep-wake disturbances. However, few mouse AD models exhibit these disturbances. Lemborexant, a dual orexin receptor antagonist, is under development for treating circadian rhythm disorders in dementia.

Objective:

Evaluation of senescence-accelerated mouse prone-8 (SAMP8) mice as a model for sleep-wake and rhythm disturbances in AD and the effect of lemborexant by assessing sleep-wake/diurnal rhythm behavior.

Methods:

SAMP8 and control senescence-accelerated mouse resistant-1 (SAMR1) mice received vehicle or lemborexant at light onset; plasma lemborexant and diurnal cerebrospinal fluid (CSF) orexin concentrations were assessed. Sleep-wake behavior and running wheel activity were evaluated.

Results:

Plasma lemborexant concentrations were similar between strains. The peak/nadir timing of CSF orexin concentrations were approximately opposite between strains. During lights-on, SAMP8 mice showed less non-rapid eye movement (non-REM) and REM sleep than SAMR1 mice. Lemborexant treatment normalized wakefulness/non-REM sleep in SAMP8 mice. During lights-off, lemborexant-treated SAMR1 mice showed increased non-REM sleep; lemborexant-treated SAMP8 mice displayed increased wakefulness. SAMP8 mice showed differences in electroencephalogram architecture versus SAMR1 mice. SAMP8 mice exhibited more running wheel activity during lights-on. Lemborexant treatment reduced activity during lights-on and increased activity in the latter half of lights-off, demonstrating a corrective effect on overall diurnal rhythm. Lemborexant delayed the acrophase of activity in both strains by approximately 1 hour.

Conclusion:

SAMP8 mice display several aspects of sleep-wake and rhythm disturbances in AD, notably mistimed activity. These findings provide some preclinical rationale for evaluating lemborexant in patients with AD who experience sleep-wake and rhythm disturbances.

Efficient high resolution sLORETA in brain source localization

Objective. Estimation of the source location within the brain from electroencephalography (EEG) and magnetoencephalography measures is a challenging task. Among the existing techniques in the field, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work we illustrate that sLORETA is still noisy and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc theresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding. Approach. The proposed method, denoted by efficient high-resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates. Main results. The approach is evaluated using synthetic EEG and real EEG data. spatial dispersion (SD), and mean square error (MSE) are used as metrics to provide the quantitative performance of the method. In addition, qualitative analysis of the method is provided for real EEG data. This proposed model demonstrates advantages over the existing methods in sense of accuracy and robustness with SD and MSE comparison. Significance. EHR-sLORETA could have a significant impact on clinical studies with source estimation task, as it improves the accuracy of source estimation and eliminates the need for manual thresholding.

Modified cerebral small vessel disease score is associated with vascular cognitive impairment after lacunar stroke

We conducted a cross-sectional study to characterize the relationship between total and modified small vessel disease (SVD) score with vascular cognitive impairment (VCI). Patients (n = 157) between the ages of 50 and 85 years old who had suffered their first lacunar infarction were analyzed prospectively. Brain magnetic resonance imaging was performed to identify SVD manifestations, which were used to calculate total or modified SVD scores. Neuropsychological assessments measured cognitive function. Spearman correlation analysis demonstrated that the total and modified SVD scores were associated with overall cognition as well as with function in the executive and visuospatial domains. The associations remained significant in linear regression after adjusting for age, sex, education and vascular risk factors. Binary logistic regression and chi-squared trend tests revealed that VCI risk increased significantly with SVD burden based on the modified SVD score. Subsequent chi-squared testing demonstrated that the VCI rate was significantly higher in patients with a modified SVD score of 5-6 than in patients without any SVD burden. Our results suggest that both the total and modified SVD scores show a negative association with cognitive function, but the modified SVD score may be better at identifying patients at high VCI risk.

Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model

Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R2 = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.

Acute EEG Patterns Associated With Transient Ischemic Attack

BACKGROUND

Transient ischemic attack (TIA) is characterized by stroke-like neurologic signs and symptoms in the absence of demonstrable structural neuropathology. There is no test for TIA, with classification often reliant on subjective, retrospective report. Functional brain measures such as the electroencephalogram (EEG) may be helpful in objectively detecting and describing the pathophysiology of TIA, but this has not been adequately examined.

METHODS

EEG was obtained from a single electrode over the left frontal lobe during 3-minute resting-state and auditory oddball conditions administered to consecutive patients within 72 hours of admission to the acute stroke ward of a tertiary hospital. Separately, patients were classified by their treating team as having suffered either an ischemic stroke (n = 10) or a TIA (n = 10). Relative power of delta, theta, alpha, and beta EEG frequency bands were extracted for comparison between the 2 clinical groups and an existing normative sample of 10 healthy, age-, gender-, and education-matched older adults.

RESULTS

Analysis of variance with post hoc testing identified pronounced delta activity in stroke patients, while alpha and beta power were elevated in TIA patients. Both patient groups exhibited attenuated theta activity compared with healthy controls. Receiver operating characteristic curve analysis identified thresholds for each EEG frequency capable of distinguishing the 3 participant groups.

CONCLUSIONS

TIA, ischemic stroke, and healthy aging are each associated with distinct electrophysiological profiles. These preliminary findings suggest that acute EEG may be helpful in elucidating the pathophysiology and reversibility of TIA symptoms, and further exploration of the value of this unique functional brain data is encouraged.

Deep microbleeds and periventricular white matter disintegrity are independent predictors of attention/executive dysfunction in non-dementia patients with small vessel disease

BACKGROUND

Cerebral small vessel disease (SVD) is the common cause of cognitive decline in the old population. MRI can be used to clarify its mechanisms. However, the surrogate markers of MRI for early cognitive impairment in SVD remain uncertain to date. We investigated the cognitive impacts of cerebral microbleeds (CMBs), diffusion tensor imaging (DTI), and brain volumetric measurements in a cohort of post-stroke non-dementia SVD patients.

METHODS

Fifty five non-dementia SVD patients were consecutively recruited and categorized into two groups as no cognitive impairment (NCI) (n = 23) or vascular mild cognitive impairment (VaMCI) (n = 32). Detailed neuropsychological assessment and multimodal MRI were completed.

RESULTS

The two groups differed significantly on Z scores of all cognitive domains (all p < 0.01) except for the language. There were more patients with hypertension (p = 0.038) or depression (p = 0.019) in the VaMCI than those in the NCI group. Multiple regression analysis of cognition showed periventricular mean diffusivity (MD) (β = -0.457, p < 0.01) and deep CMBs numbers (β = -0.352, p < 0.01) as the predictors of attention/executive function, which explained 45.2% of the total variance. Periventricular MD was the independent predictor for either memory (β = -0.314, p < 0.05) or visuo-spatial function (β = -0.375, p < 0.01); however, only small proportion of variance could be accounted for (9.8% and 12.4%, respectively). Language was not found to be correlated with any of the MRI parameters. No correlation was found between brain atrophic indices and any of the cognitive measures.

CONCLUSION

Arteriosclerotic CMBs and periventricular white matter disintegrity seem to be independent MRI surrogated markers in the early stage of cognitive impairment in SVD.

Perturbation of Brain Oscillations after Ischemic Stroke: A Potential Biomarker for Post-Stroke Function and Therapy

Brain waves resonate from the generators of electrical current and propagate across brain regions with oscillation frequencies ranging from 0.05 to 500 Hz. The commonly observed oscillatory waves recorded by an electroencephalogram (EEG) in normal adult humans can be grouped into five main categories according to the frequency and amplitude, namely δ (1-4 Hz, 20-200 μV), θ (4-8 Hz, 10 μV), α (8-12 Hz, 20-200 μV), β (12-30 Hz, 5-10 μV), and γ (30-80 Hz, low amplitude). Emerging evidence from experimental and human studies suggests that groups of function and behavior seem to be specifically associated with the presence of each oscillation band, although the complex relationship between oscillation frequency and function, as well as the interaction between brain oscillations, are far from clear. Changes of brain oscillation patterns have long been implicated in the diseases of the central nervous system including ischemic stroke, in which the reduction of cerebral blood flow as well as the progression of tissue damage have direct spatiotemporal effects on the power of several oscillatory bands and their interactions. This review summarizes the current knowledge in behavior and function associated with each brain oscillation, and also in the specific changes in brain electrical activities that correspond to the molecular events and functional alterations observed after experimental and human stroke. We provide the basis of the generations of brain oscillations and potential cellular and molecular mechanisms underlying stroke-induced perturbation. We will also discuss the implications of using brain oscillation patterns as biomarkers for the prediction of stroke outcome and therapeutic efficacy.

Task-induced brain activity in aphasic stroke patients: what is driving recovery?

The estimated prevalence of aphasia in the UK and the USA is 250 000 and 1 000 000, respectively. The commonest aetiology is stroke. The impairment may improve with behavioural therapy, and trials using cortical stimulation or pharmacotherapy are undergoing proof-of-principle investigation, but with mixed results. Aphasia is a heterogeneous syndrome, and the simple classifications according to the Broca-Wernicke-Lichtheim model inadequately describe the diverse communication difficulties with which patients may present. Greater knowledge of how intact neural networks promote recovery after aphasic stroke, either spontaneously or in response to interventions, will result in clearer hypotheses about how to improve the treatment of aphasia. Twenty-five years ago, a pioneering study on healthy participants heralded the introduction of functional neuroimaging to the study of mechanisms of recovery from aphasia. Over the ensuing decades, such studies have been interpreted as supporting one of three hypotheses, which are not mutually exclusive. The first two predate the introduction of functional neuroimaging: that recovery is the consequence of the reconstitution of domain-specific language systems in tissue around the lesion (the 'perilesional' hypothesis), or by homotopic cortex in the contralateral hemisphere (the 'laterality-shift' hypothesis). The third is that loss of transcallosal inhibition to contralateral homotopic cortex hinders recovery (the 'disinhibition' hypothesis). These different hypotheses at times give conflicting views about rehabilitative intervention; for example, should one attempt to activate or inhibit a contralateral homotopic region with cortical stimulation techniques to promote recovery? This review proposes that although the functional imaging data are statistically valid in most cases, their interpretation has often favoured one explanation while ignoring plausible alternatives. In our view, this is particularly evident when recovery is attributed to activity in 'language networks' occupying sites not observed in healthy participants. In this review we will argue that much of the distribution of what has often been interpreted as language-specific activity, particularly in midline and contralateral cortical regions, is an upregulation of activity in intact domain-general systems for cognitive control and attention, responding in a task-dependent manner to the increased 'effort' when damaged downstream domain-specific language networks are impaired. We further propose that it is an inability fully to activate these systems that may result in sub optimal recovery in some patients. Interpretation of the data in terms of activity in domain-general networks affords insights into novel approaches to rehabilitation.

Electroencephalogram and Alzheimer's disease: clinical and research approaches

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.