Quantitative EEG and Electromagnetic Brain Imaging in Aging and in the Evolution of Dementia

Mitigation of Acute Seizures and Neuropathology after Traumatic Brain Injury by Structure-Based Discovery-Identified Drugs

Our objective was to test the hypothesis that structure-based identified or designed compounds exhibiting neuroprotective, antioxidant, and anti-inflammatory properties in vitro will mitigate early seizures and neuropathology after traumatic brain injury (TBI) in vivo. The neuroprotective and anti-inflammatory effects of 11 compounds identified by computer-assisted approximations were tested in vitro in neuronal microglial co-cultures. Among these, compound FBA exhibited the best neuroprotective (MAP-2, microtubule-associated-protein 2, a neuronal damage biomarker), antioxidative (nitrite production), and anti-inflammatory effects in vitro (all p < 0.01). Consequently, its neuroprotective and antiseizure effects were assessed in vivo in adult male Sprague-Dawley rats exposed to severe lateral fluid-percussion-induced TBI. Rats under continuous video-electroencephalogram monitoring received prophylactic treatment with an intraperitoneal (i.p.) injection of FBA (FBApro, 30 mg/kg) or vehicle (VEH, 48% PEG in 0.9% saline, 3 mL/kg) at 2 and 24 h post-TBI. Rats that developed status epilepticus received 1-2 additional on-demand FBA doses (FBApro+, 100 mg/kg, i.p.) to stop epileptiform activity. FBApro treatment reduced the cortical lesion area (18.9 ± 4.1 mm2, n = 7) compared with VEH treatment (24.8 ± 5.7 mm2, n = 10, p < 0.05). FBApro treatment also showed a favorable effect on the white matter by reducing plasma levels of pNF-H, a biomarker of axonal injury, compared with VEH treatment (Cohen's delta 0.657). Both FBApro (368 ± 407 s) and FBApro+ (256 ± 327 s) treatments reduced the average cumulative seizure duration compared with VEH (896 ± 703 s, both p < 0.05). The FBApro+ treatment regimen also reduced the mean relative theta and alpha power and increased the mean relative gamma power in the electroencephalogram (p < 0.05). Our data identified FBA as a novel structure-based discovered compound with promising favorable effects on structural and functional recovery after TBI.

Neuronal oscillations in cognition: Down syndrome as a model of mouse to human translation

Down syndrome (DS), a prevalent cognitive disorder resulting from trisomy of human chromosome 21 (Hsa21), poses a significant global health concern. Affecting approximately 1 in 800 live births worldwide, DS is the leading genetic cause of intellectual disability and a major predisposing factor for early-onset Alzheimer's dementia. The estimated global population of individuals with DS is 6 million, with increasing prevalence due to advances in DS health care. Global efforts are dedicated to unraveling the mechanisms behind the varied clinical outcomes in DS. Recent studies on DS mouse models reveal disrupted neuronal circuits, providing insights into DS pathologies. Yet, translating these findings to humans faces challenges due to limited systematic electrophysiological analyses directly comparing human and mouse. Additionally, disparities in experimental procedures between the two species pose hurdles to successful translation. This review provides a concise overview of neuronal oscillations in human and rodent cognition. Focusing on recent DS mouse model studies, we highlight disruptions in associated brain function. We discuss various electrophysiological paradigms and suggest avenues for exploring molecular dysfunctions contributing to DS-related cognitive impairments. Deciphering neuronal oscillation intricacies holds promise for targeted therapies to alleviate cognitive disabilities in DS individuals.

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture

BACKGROUND

Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking.

AIMS

This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall.

METHODS & PROCEDURES

This is a cross-sectional, online, self-administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence-Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi-QA-MPNet (Multi-Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence-Transformer.

OUTCOMES & RESULTS

The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task.

CONCLUSIONS & IMPLICATIONS

This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images.

WHAT THIS PAPER ADDS

What is already known on this subject It is already known that speech- and language-based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech- and language-based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.

Unmasking hidden risks: The surprising link between PDE5 inhibitors and seizure susceptibility

BACKGROUND

Phosphodiesterase 5 inhibitors (PDE5i) are the first line treatment for erectile dysfunction; however, several articles and case reports have shown central nervous system effects, that can cause seizures in susceptible patients. This study aims to describe the changes caused by the use of Sildenafil and Tadalafil through the analysis of abnormalities expressed in the electrocorticogram (ECoG) of rats and evaluate the seizure threshold response and treatment of seizures with anticonvulsants.

MATERIALS AND METHODS

The study used 108 rats (Wistar). Before surgery for electrode placement in dura mater, the animals were randomly separated into 3 experiments for electrocorticogram analysis. Experiment 1: ECoG response to using PD5i (Sildenafil 20mg/kg and Tadalafil 2.6mg/kg p.o.). Experiment 2: ECoG response to the use of PD5i in association with Pentylenetetrazole (PTZ-30 mg/kg i.p.), a convulsive model. Experiment 3: ECoG response to anticonvulsant treatment (Phenytoin, Phenobarbital and Diazepam) of seizures induced by association IPDE5 + PTZ. All recordings were made thirty minutes after administration of the medication and analyzed for ten minutes, only once. We considered statistical significance level of *p<0.05, **p<0.01 and ***p < 0.001.

RESULTS

After administration of Sildenafil and Tadalafil, there were increases in the power of recordings in the frequency bands in oscillations in alpha (p = 0.0920) and beta (p = 0.602) when compared to the control group (p<0.001). After the use of Sildenafil and Tadalafil associated with PTZ, greater potency was observed in the recordings during seizures (p<0.001), however, the Sildenafil group showed greater potency when compared to Tadalafil (p<0.05). Phenobarbital and Diazepam showed a better response in controlling discharges triggered by the association between proconvulsant drugs.

CONCLUSIONS

PDE5i altered the ECoG recordings in the rats' motor cortexes, demonstrating cerebral asynchrony and potentiating the action of PTZ. These findings demonstrate that PDE5i can lower the seizure threshold.

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

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

Cognitive Function Deterioration After Cardiopulmonary Bypass: Can Intraoperative Optimal Cerebral Regional Tissue Oxygen Saturation Predict Postoperative Cognitive Function?

OBJECTIVE

Cognitive impairment is a common neurologic complication after cardiac surgery with cardiopulmonary bypass (CPB). This study evaluated postoperative cognitive function to determine predictors of cognitive dysfunction, including intraoperative cerebral regional tissue oxygen saturation (rSO₂).

DESIGN

A prospective observational cohort study.

SETTING

At a single academic tertiary-care center.

PARTICIPANTS

A total of 60 adults undergoing cardiac surgery with CPB from January to August 2021.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

All patients underwent Mini-Mental State Examination (MMSE) and quantified electroencephalography (qEEG) 1 day before cardiac surgery, 7 days postoperatively (POD7), and POD60. Intraoperative cerebral rSO₂ was monitored continuously. For MMSE, no significant decrease in MMSE score was found on POD7 versus preoperatively (p = 0.09), but POD60 scores showed significant improvement compared with both preoperative (p = 0.02) and POD7 scores (p < 0.001). On qEEG, relative theta power on POD7 was increased versus preoperatively (p < 0.001), but it was decreased on POD60 (POD7 versus POD60, p < 0.001), and was close to preoperative data (p > 0.99). Baseline rSO₂ was an independent factor for postoperative MMSE. Both baseline and mean rSO₂ showed a significant influence in postoperative relative theta activity, whereas mean rSO₂ was the only predictor for the theta-gamma ratio (p = 0.04).

CONCLUSIONS

The MMSE in patients undergoing CPB declined at POD7 and recovered by POD60. Lower baseline rSO₂ indicated a higher potential for MMSE decline at POD60. Inferior intraoperative mean rSO₂ was related to higher postoperative relative theta activity and theta-gamma ratio, implying subclinical or further cognitive impairment.

Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline

Introduction

Age is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.

Methods

We explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.

Results

The risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.

Discussion

We propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.

One-Year Follow-Up of Healthy Older Adults with Electroencephalographic Risk for Neurocognitive Disorder After Neurofeedback Training

Background:

In healthy older adults, excess theta activity is an electroencephalographic (EEG) predictor of cognitive impairment. In a previous study, neurofeedback (NFB) treatment reinforcing reductions theta activity resulted in EEG reorganization and cognitive improvement.

Objective:

To explore the clinical applicability of this NFB treatment, the present study performed a 1-year follow-up to determine its lasting effects.

Methods:

Twenty seniors with excessive theta activity in their EEG were randomly assigned to the experimental or control group. The experimental group received an auditory reward when the theta absolute power (AP) was reduced. The control group received the reward randomly.

Results:

Both groups showed a significant decrease in theta activity at the training electrode. However, the EEG results showed that only the experimental group underwent global changes after treatment. These changes consisted of delta and theta decreases and beta increases. Although no changes were found in any group during the period between the posttreatment evaluation and follow-up, more pronounced theta decreases and beta increases were observed in the experimental group when the follow-up and pretreatment measures were compared. Executive functions showed a tendency to improve two months after treatment which became significant one year later.

Conclusion:

These results suggest that the EEG and behavioral benefits of this NFB treatment persist for at least one year, which adds up to the available evidence contributing to identifying factors that increase its efficacy level. The relevance of this study lies in its prophylactic features of addressing a clinically healthy population with EEG risk of cognitive decline.

Characteristics of Neural Network Changes in Normal Aging and Early Dementia

To understand the mechanisms underlying preserved and impaired cognitive function in healthy aging and dementia, respectively, the spatial relationships of brain networks and mechanisms of their resilience should be understood. The hub regions of the brain, such as the multisensory integration and default mode networks, are critical for within- and between-network communication, remain well-preserved during aging, and play an essential role in compensatory processes. On the other hand, these brain hubs are the preferred sites for lesions in neurodegenerative dementias, such as Alzheimer's disease. Disrupted primary information processing networks, such as the auditory, visual, and sensorimotor networks, may lead to overactivity of the multisensory integration networks and accumulation of pathological proteins that cause dementia. At the cellular level, the brain hub regions contain many synapses and require a large amount of energy. These regions are rich in ATP-related gene expression and had high glucose metabolism as demonstrated on positron emission tomography (PET). Importantly, the number and function of mitochondria, which are the center of ATP production, decline by about 8% every 10 years. Dementia patients often have dysfunction of the ubiquitin-proteasome and autophagy-lysosome systems, which require large amounts of ATP. If there is low energy supply but the demand is high, the risk of disease can be high. Imbalance between energy supply and demand may cause accumulation of pathological proteins and play an important role in the development of dementia. This energy imbalance may explain why brain hub regions are vulnerable to damage in different dementias. Here, we review (1) the characteristics of gray matter network, white matter network, and resting state functional network changes related to resilience in healthy aging, (2) the mode of resting state functional network disruption in neurodegenerative dementia, and (3) the cellular mechanisms associated with the disruption.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.

Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment

Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients. From a total of 1,912 screened studies, 10 were included in our final sample (N = 208 independent participants in experimental and N = 81 in the control groups completing the primary endpoint). We compared the clinical efficacy across studies, and evaluated their experimental designs and reporting quality. In most studies, patients showed improved scores in different cognitive tests. However, data from randomized controlled trials remains scarce, and clinical evidence based on standardized metrics is still inconclusive. In light of recent meta-research developments in the neurofeedback field and beyond, quality and reporting practices of individual studies are reviewed. We conclude with recommendations on best practices for future studies that investigate the effects of neurofeedback training in dementia and cognitive impairment.

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.

The Investigation of Simultaneous EEG and Eye Tracking Characteristics During Fixation Task in Mild Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder that occurs many years before the first clinical symptoms. Finding more exact, significant, and valuable criteria or indices for the diagnosis of the mild form of Alzheimer's disease is very important for clinical and research purposes. Electroencephalography (EEG) and eye tracking biomarkers would provide noninvasive tools for the early detection of AD. Due to the advantages of EEG and eye tracking, in this study, we employed them simultaneously to conduct research on the mild AD. For this purpose, 19 patients with mild AD were compared with 19 gender- and age-matched normal subjects who did not have any history of cognitive or neurological disorders. EEG and eye-tracking data were concurrently collected in both groups in a fixation task. Our results revealed that the total fixation duration was significantly shorter for the AD patients, but their fixation frequency was more than that of the controls. In addition, increased theta power and decreased alpha power were observed in the AD group. Interestingly, there was a statistically significant correlation between fixation frequency and alpha power in the parietal area in the control group. However, this connection was not statistically significant in the AD group. The findings also indicated an elevated coherence in the AD patients in the parieto-occipital area. It is assumed that the AD patients might use the neural compensational processes for the fixation state. This study provides evidence for the simultaneously EEG and eye-tracking changes in the areas, which are involved in the control of the fixational eye movements.

Resting EEG, Hair Cortisol and Cognitive Performance in Healthy Older People with Different Perceived Socioeconomic Status

Successful aging depends upon several internal and external factors that influence the overall aging process. Objective and subjective socioeconomic status emerge as potential psychosocial factors in the ethiopathophysiology of aging-related disorders. Presumably, low socioeconomic status can act as a psychosocial stressor that can affect humans’ physiology via psychoneuroendocrine mechanisms, that may, in turn, affect the brain physiology. In resting-state electroencephalography (EEG), excess theta and delta activity has been related to cognitive decline and dementia. The main aim of this study was to analyze the effect of objective and subjective socioeconomic status (SES) on cognition and brain electrical activity through EEG measures. The present research constitutes a cross-sectional study with thirty healthy older adults (61–82 years old) separated into two clusters: high socioeconomic (HS) and low socioeconomic (LS) status; they were evaluated and compared in cognitive terms using the Wechsler Adult Intelligence Scale (WAIS-IV). An EEG at rest was recorded to measure brain activity and, as an indicator of long-term stress exposure, hair cortisol concentrations (HCC) were measured. Our results show that lower SES is related to a worse performance in working memory tasks (p = 0.009), higher delta (p = 0.002) and theta power (p = 0.039), and lower alpha activity (p = 0.028). However, it seems that SES does not significantly affect HCC in this population of healthy older adults. The effects of SES on long-term cortisol exposure, brain electrical activity, and cognitive functions in healthy older people emphasize the role of psychosocial factors in aging from an integrative perspective that will allow us to implement better prevention programs to target cognitive decline in adults.

Bispectral Index Monitoring With Density Spectral Array for Delirium Detection

BACKGROUND

Delirium in hospitalized patients often goes undetected. Cerebral state monitors, which measure limited-channel electroencephalography, have shown potential for improving delirium detection.

OBJECTIVE

The aim of this study was to compare an FDA-approved cerebral state monitor, bispectral index monitoring with density spectral array (DSA), for delirium identification with clinical screening methods.

METHODS

Hospitalized patients receiving psychiatric consultation were assessed for delirium using the 3-minute Diagnostic Interview for Confusion Assessment Method (3D-CAM) and underwent bispectral index monitor + DSA monitoring. Visual inspection of frequency band power of the DSA was performed by 2 trained independent raters. Average hue values were calculated for each frequency band using image analysis software as the device did not allow for extraction of raw electroencephalography data. Delirious versus nondelirious group averages, sensitivity, specificity, and area under the curve were calculated for significant DSA variables and the 3D-CAM.

RESULTS

In an initial cohort of 43 patients, visual ratings of the DSA were not associated with delirium (P > 0.1). In a larger cohort of 123 subjects, multiple band hue ratios were associated with delirium, although none survived correction for multiple comparisons. In a subgroup of 74 non-neurological patients, low theta/low delta ratio was significantly associated with delirium (P = 0.001) (sensitivity/specificity/area under the curve: 83%/70%/0.757; 3D-CAM: 67%/77%/0.717; paired-sample area under the curve difference: -0.040, P = 0.68). In 21 patients with dementia, low theta power demonstrated significantly greater sensitivity/specificity/area under the curve of 83%/78%/0.824, whereas 3D-CAM achieved 50%/78%/0.639 (P = 0.04).

CONCLUSION

Bispectral index monitor + DSA was similar to 3D-CAM for detecting delirium in hospitalized patients with and without neurological disorders, and was significantly more accurate in patients with dementia. More studies are needed to validate the use of cerebral state monitors for quantitative delirium detection.

A Long-Living Bioengineered Neural Tissue Platform to Study Neurodegeneration

The prevalence of dementia and other neurodegenerative diseases continues to rise as age demographics in the population shift, inspiring the development of long-term tissue culture systems with which to study chronic brain disease. Here, it is investigated whether a 3D bioengineered neural tissue model derived from human induced pluripotent stem cells (hiPSCs) can remain stable and functional for multiple years in culture. Silk-based scaffolds are seeded with neurons and glial cells derived from hiPSCs supplied by human donors who are either healthy or have been diagnosed with Alzheimer's disease. Cell retention and markers of stress remain stable for over 2 years. Diseased samples display decreased spontaneous electrical activity and a subset displays sporadic-like indicators of increased pathological β-amyloid and tau markers characteristic of Alzheimer's disease with concomitant increases in oxidative stress. It can be concluded that the long-term stability of the platform is suited to study chronic brain disease including neurodegeneration.

Beta Neurofeedback Training Improves Attentional Control in the Elderly

One of the well-documented behavioral changes that occur with advancing age is a decline in executive functioning, for example, attentional control. Age-related executive deficits are said to be associated with a deterioration of the frontal lobes. Neurofeedback is a training method which aims at acquiring self-control over certain brain activity patterns. It is considered as an effective approach to help improve attentional and self-management capabilities. However, studies evaluating the efficacy of neurofeedback training to boost executive functioning in an elderly population are still relatively rare and controversial. The aim of our study was to contribute to the assessment of the efficacy of neurofeedback as a method for enhancing executive functioning in the elderly. We provided a group of seniors with beta up-training (12-22 Hz), consisting of 20 sessions (30 minutes each), on the Cz site and tested its possible beneficiary influence on attentional control assessed by means of the Stroop and Simon tasks. The analysis of the subjects' mean reaction times during consecutive tasks in the test and the retest, after implementation of neurofeedback training, showed a significant improvement. In contrast, the difference in reaction times between the test and the retest in the control group who had not been submitted to neurofeedback training was not significant.

Neurophysiological Markers of Alzheimer's Disease: Quantitative EEG Approach

Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.

Decreased Parietal Beta Power as a Sign of Disease Progression in Patients with Mild Cognitive Impairment

BACKGROUND

Electroencephalography (EEG) power has previously been used to compare mild cognitive impairment (MCI) patients who progress to Alzheimer's disease (pMCI) with patients with MCI who remain stable (sMCI) by using beta power. However, the beta band is very broad and smaller frequency bands may improve accuracy.

OBJECTIVE

In the present study, we wanted to investigate whether it was possible to find any differences between pMCI and sMCI using relative power and whether these differences were correlated to cognitive function or neuropathology markers.

METHODS

17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited from two memory clinics and followed for three years. EEGs were recorded at baseline for all participants and relative power was calculated. All participants underwent adjusted batteries of standardized cognitive tests and lumbar puncture.

RESULTS

We found that pMCI showed decreased baseline relative power in the parietal electrodes in the beta1 band (13-17.99 Hz). At 2-year follow-up, we found changes in all baseline beta bands but most pronounced in the beta1 band. In addition, we found that qEEG parietal power was correlated with amyloid-β42 and anterograde memory.

CONCLUSION

These findings suggests that relative power in the parietal electrodes in the beta1 band may be a better way to discriminate between pMCI and sMCI at the time of diagnosis than the broad beta band. Similar findings have also been found with resting state fMRI. In addition, we found that anterograde memory was correlated to qEEG parietal beta1 power.

Neuropsychological and neurophysiological characterization of mild cognitive impairment and Alzheimer's disease in Down syndrome

Down syndrome (DS) has been considered a unique model for the investigation of Alzheimer's disease (AD) but intermediate stages in the continuum are poorly defined. Considering this, we investigated the neurophysiological (i.e., magnetoencephalography [MEG]) and neuropsychological patterns of mild cognitive impairment (MCI) and AD in middle-aged adults with DS. The sample was composed of four groups: Control-DS (n = 14, mean age 44.64 ± 3.30 years), MCI-DS (n = 14, 51.64 ± 3.95 years), AD-DS (n = 13, 53.54 ± 6.58 years), and Control-no-DS (healthy controls, n = 14, 45.21 ± 4.39 years). DS individuals were studied with neuropsychological tests and MEG, whereas the Control-no-DS group completed only the MEG session. Our results showed that the AD-DS group exhibited a significantly poorer performance as compared with the Control-DS group in all tests. Furthermore, this effect was crucially evident in AD-DS individuals when compared with the MCI-DS group in verbal and working memory abilities. In the neurophysiological domain, the Control-DS group showed a widespread increase of theta activity when compared with the Control-no-DS group. With disease progression, this increased theta was substituted by an augmented delta, accompanied with a reduction of alpha activity. Such spectral pattern-specifically observed in occipital, posterior temporal, cuneus, and precuneus regions-correlated with the performance in cognitive tests. This is the first MEG study in the field incorporating both neuropsychological and neurophysiological information, and demonstrating that this combination of markers is sensitive enough to characterize different stages along the AD continuum in DS.

Sleep and EEG Power Spectral Analysis in Three Transgenic Mouse Models of Alzheimer's Disease: APP/PS1, 3xTgAD, and Tg2576

BACKGROUND

Sleep disturbances have long been associated with Alzheimer's disease (AD), and there is a growing interest in how these disturbances might impact AD pathophysiology. Despite this growing interest, surprisingly little is known about how sleep architecture and the broader neuronal network are affected in widely used transgenic mouse models of AD.

OBJECTIVE

We analyzed sleep and electroencephalography (EEG) power in three transgenic mouse models of AD, using identical and commercially available hardware and analytical software. The goal was to assess the suitability of these mouse lines to model sleep and the broader neuronal network dysfunction measured by EEG in AD.

METHODS

Tg2576, APP/PS1, and 3xTgAD transgenic AD mice were studied using in vivo EEG recordings for sleep/wake time and power spectral analysis.

RESULTS

Both the APP/PS1 model at 8- 10 months and the Tg2576 model at 12 months of age exhibited stage-dependent decreases in theta and delta power, and shifts in the power spectra toward higher frequencies. Stage-dependent power spectral analyses showed no changes in the 3xTgAD model at 18 months of age. The percentage of time spent awake, in non-rapid eye movement sleep (NREM), or in rapid-eye-movement sleep (REM) was not different between genotypes in any of the transgenic lines.

CONCLUSION

Our findings are consistent with data from several other transgenic AD models as well as certain studies in patients with mild cognitive impairment. Further studies will be needed to better understand the correlation between EEG spectra and AD pathophysiology, both in AD models and the human condition.

EEG Theta Power Is an Early Marker of Cognitive Decline in Dementia due to Alzheimer's Disease

BACKGROUND

Quantitative EEG (qEEG) power could potentially be used as a diagnostic tool for Alzheimer's disease (AD) and may further our understanding of the pathophysiology. However, the early qEEG power changes of AD are not well understood.

OBJECTIVE

To investigate the early changes in qEEG power and the possible correlation with memory function and cerebrospinal fluid biomarkers. In addition, whether qEEG power could discriminate between AD, mild cognitive impairment (MCI), and older healthy controls (HC) at the individual level.

METHODS

Standard EEGs from 138 HC, 117 MCI, and 117 AD patients were included from six Nordic memory clinics. All EEGs were recorded consecutively before the diagnosis and were not used for the consensus diagnosis. Absolute and relative power was calculated for both eyes closed and open condition.

RESULTS

At group level using relative power, we found significant increases globally in the theta band and decreases in high frequency power in the temporal regions for eyes closed for AD and, to a lesser extent, for MCI compared to HC. Relative theta power was significantly correlated with multiple neuropsychological measures and had the largest correlation coefficient with total tau. At the individual level, the classification rate for AD and HC was 72.9% for relative power with eyes closed.

CONCLUSION

Our findings suggest that the increase in relative theta power may be the first change in patients with dementia due to AD. At the individual level, we found a moderate classification rate for AD and HC when using EEGs alone.

Electromagnetic signatures of the preclinical and prodromal stages of Alzheimer's disease

Biomarkers useful for the predementia stages of Alzheimer’s disease are needed. Electroencephalography and magnetoencephalography (MEG) are expected to provide potential biomarker candidates for evaluating the predementia stages of Alzheimer’s disease. However, the physiological relevance of EEG/MEG signal changes and their role in pathophysiological processes such as amyloid-β deposition and neurodegeneration need to be elucidated. We evaluated 28 individuals with mild cognitive impairment and 38 cognitively normal individuals, all of whom were further classified into amyloid-β-positive mild cognitive impairment (n = 17, mean age 74.7 ± 5.4 years, nine males), amyloid-β-negative mild cognitive impairment (n = 11, mean age 73.8 ± 8.8 years, eight males), amyloid-β-positive cognitively normal (n = 13, mean age 71.8 ± 4.4 years, seven males), and amyloid-β-negative cognitively normal (n = 25, mean age 72.5 ± 3.4 years, 11 males) individuals using Pittsburgh compound B-PET. We measured resting state MEG for 5 min with the eyes closed, and investigated regional spectral patterns of MEG signals using atlas-based region of interest analysis. Then, the relevance of the regional spectral patterns and their associations with pathophysiological backgrounds were analysed by integrating information from Pittsburgh compound B-PET, fluorodeoxyglucose-PET, structural MRI, and cognitive tests. The results demonstrated that regional spectral patterns of resting state activity could be separated into several types of MEG signatures as follows: (i) the effects of amyloid-β deposition were expressed as the alpha band power augmentation in medial frontal areas; (ii) the delta band power increase in the same region was associated with disease progression within the Alzheimer’s disease continuum and was correlated with entorhinal atrophy and an Alzheimer’s disease-like regional decrease in glucose metabolism; and (iii) the global theta power augmentation, which was previously considered to be an Alzheimer’s disease-related EEG/MEG signature, was associated with general cognitive decline and hippocampal atrophy, but was not specific to Alzheimer’s disease because these changes could be observed in the absence of amyloid-β deposition. The results suggest that these MEG signatures may be useful as unique biomarkers for the predementia stages of Alzheimer’s disease.

Potential Clinical Applications and Future Prospect of Wireless and Mobile Electroencephalography on the Assessment of Cognitive Impairment

Electroencephalography (EEG) systems have been used for assessing cognitive function in dementia for several decades. Studies have demonstrated that EEG in Alzheimer's disease (AD) patients is generally characterized by significant and specific increases in delta and theta power, a decrease in alpha power, and a decrease in the coherence of the fast bands between different brain areas linked by long corticocortical fibers. Posterior EEG characteristics in dementia with Lewy bodies (DLB) allowed discrimination of DLB from AD and controls with high accuracy. Traditional EEG systems require a long application time and discomfort, which limited its use in dementia patients. Alternative tools for assessing cognition may be simple, low-cost, and mobile medical devices such as wireless and mobile EEG (wmEEG) sensor platforms with flexible electronics and stretchable electrode sheets that could be compatible with long-term EEG monitoring even in dementia patients. In this study, we review the utility of EEG in reflecting cognitive function and the prospects for clinical application of wmEEG monitoring for detecting early dementia and discriminating subtypes of dementia effectively and objectively assessing longitudinal cognitive changes. Repeated and longitudinal documentation of EEG using wmEEG will contribute to detection of specific sleep/wake EEG patterns for patients with sleep and wake-related problems related to dementia.

Quantitative Electroencephalography Analyzed by Statistical Pattern Recognition as a Diagnostic and Prognostic Tool in Mild Cognitive Impairment: Results from a Nordic Multicenter Cohort Study

Aim

To examine diagnostic and prognostic potential of quantitative electroencephalography (qEEG) analyzed by the statistical pattern recognition (SPR) method in patients with cognitive impairment. We compared the differential diagnostic ability of SPR to visual EEG analysis. Correlation between SPR findings and cerebrospinal fluid (CSF) Alzheimer disease (AD) biomarkers were evaluated.

Methods

It is a multicenter cohort study involving 129 patients, (mild cognitive impairment [MCI], AD, and healthy controls). Standardized EEG was performed at baseline. Patients were continuously clinically evaluated.

Results

Receiver Operating Characteristic curves showed a low discriminative ability of SPR and no ability to predict clinical progression in patients with MCI. Moderate correlation between SPR analysis and CSF AD biomarkers was found.

Conclusion

The diagnostic and prognostic abilities of qEEG were low. The SPR method was superior to the visual EEG analysis. The qEEG method correlates well to CSF AD biomarkers, suggesting association with pathology in AD.

A quantitative EEG and MRI analysis of intermittent temporal slowing in the elderly

Objective

Whereas the correlation between diffuse slowing of EEG activity and neurodegenerative diseases such as Alzheimer's disease is well established, intermittent slowing over the temporal regions, which is a frequent finding in the elderly, does not have a specific clinical correlate. In this study, we compared quantitative EEG parameters between patients with temporal slowing with no signs of neurological disease and controls to evaluate cortical function in the temporal lobes and other cerebral regions. We also compared the width of the temporal lobes on magnetic resonance imaging (MRI).

Methods

Mean dominant frequency and relative power in delta, theta, alpha, and beta frequency bands were examined in 20 patients older than 60 years with intermittent temporal slowing and 20 age-matched controls without significant lesions on MRI or medical conditions known to affect the EEG. Furthermore, the correlation between the frequency of temporal slowing and the mean dominant frequency and the width of the medial temporal lobes on MRI were examined.

Results

Mean dominant frequency and the relative power in the beta frequency band was lower in patients with temporal slowing than in controls in all of the cortical regions examined. No significant correlation was found between the frequency of slowing and the mean dominant frequency. There was no significant difference in the width of the medial temporal lobes.

Conclusions

Intermittent temporal slowing was correlated with diffusely reduced mean dominant frequency and a shift in relative power to lower frequency bands.

Significance

The results suggest that subclinical diffuse cerebral pathology may be present in subjects with intermittent temporal slowing, but prospective studies including tests of cognitive function, cerebral perfusion, metabolic status, and advanced neuroimaging should be conducted.

Quantitative electroencephalographic changes and hippocampal atrophy in diabetic patients with mild cognitive impairment in Ismailia region

BACKGROUND

Cognitive decline could start or get worse among elderly patients with diabetes mellitus more than elderly without diabetes mellitus. So, those diabetic elderly patients have more risk to develop Alzheimer's disease and vascular dementia.

PATIENTS AND METHODS

This study included 48 elderly, grouped into three equal groups. First group included patients with diabetes mellitus and cognitive impairment. Second group included patients with diabetes mellitus and no cognitive impairment. The last group included the controls. Evaluation through Mini Mental State Examination, MRI brain, and Quantitative Electroencephalography (QEEG) recording was done for every studied elderly.

RESULTS

MRI finding revealed that hippocampal atrophy was significantly more prevalent among diabetic patients with mild cognitive impairment (MCI) (37.5%). The QEEG showed increase in the distribution of alpha 1 (low alpha) waves among control and diabetic patients without MCI groups, while there was an increase in the distribution of alpha 2 (high alpha) among diabetic patients with MCI. The QEEG results revealed increased alpha 2/alpha 1 ratio among patients with hippocampal atrophy.

CONCLUSIONS

Type 2 DM was suggested to increase the risk of cognitive impairment. The cognitive impairment in patients with diabetes mellitus was associated with changes in hippocampal volume and QEEG changes.

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.

No association of cortical amyloid load and EEG connectivity in older people with subjective memory complaints

Changes in functional connectivity of cortical networks have been observed in resting-state EEG studies in healthy aging as well as preclinical and clinical stages of AD. Little information, however, exists on associations between EEG connectivity and cortical amyloid load in people with subjective memory complaints. Here, we determined the association of global cortical amyloid load, as measured by florbetapir-PET, with functional connectivity based on the phase-lag index of resting state EEG data for alpha and beta frequency bands in 318 cognitively normal individuals aged 70–85 years with subjective memory complaints from the INSIGHT-preAD cohort. Within the entire group we did not find any significant associations between global amyloid load and phase-lag index in any frequency band. Assessing exclusively the subgroup of amyloid-positive participants, we found enhancement of functional connectivity with higher global amyloid load in the alpha and a reduction in the beta frequency bands. In the amyloid-negative participants, higher amyloid load was associated with lower connectivity in the low alpha band. However, these correlations failed to reach significance after controlling for multiple comparisons. The absence of a strong amyloid effect on functional connectivity may represent a selection effect, where individuals remain in the cognitively normal group only if amyloid accumulation does not impair cortical functional connectivity.

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A confirmatory study in subjective memory complainers (SMC) is presented.

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Amyloid is not associated with functional connectivity in SMC.

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Effects of amyloid on cognition are independent of functional connectivity in SMC.

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Functional connectivity changes may follow amyloid load with a temporal delay.

Regional Beta Index of Electroencephalography May Differentiate Alzheimer's Disease from Depression

Differentiating early Alzheimer's disease (AD) from depression with cognitive impairment is challenging in the elderly. To develop a model for differentiating these two conditions using electroencephalography (EEG), we enrolled 11 patients with early probable AD and 11 age- and cognitive function-matched patients with major depressive disorder (MDD) and compared the EEG relative powers of 9 scalp regions. Compared to the MDD group, the AD group had a higher global theta relative power (p=0.021). In the MDD group, beta relative power was higher in the mid-central region than in the left or right central regions (p<0.01). The prediction model that included global theta relative power and regional beta index was able to discriminate AD from MDD (AUC=0.893, p=0.002). A combination of global theta relative power and intra-individual regional differences in beta may differentiate early AD from MDD with cognitive impairment.

Permutation Disalignment Index as an Indirect, EEG-Based, Measure of Brain Connectivity in MCI and AD Patients

Objective: In this work, we introduce Permutation Disalignment Index (PDI) as a novel nonlinear, amplitude independent, robust to noise metric of coupling strength between time series, with the aim of applying it to electroencephalographic (EEG) signals recorded longitudinally from Alzheimer’s Disease (AD) and Mild Cognitive Impaired (MCI) patients. The goal is to indirectly estimate the connectivity between the cortical areas, through the quantification of the coupling strength between the corresponding EEG signals, in order to find a possible matching with the disease’s progression. Method: PDI is first defined and tested on simulated interacting dynamic systems. PDI is then applied to real EEG recorded from 8 amnestic MCI subjects and 7 AD patients, who were longitudinally evaluated at time [Formula: see text]0 and 3 months later (time [Formula: see text]1). At time [Formula: see text]1, 5 out of 8 MCI patients were still diagnosed MCI (stable MCI) whereas the remaining 3 exhibited a conversion from MCI to AD (prodromal AD). PDI was compared to the Spectral Coherence and the Dissimilarity Index. Results: Limited to the size of the analyzed dataset, both Coherence and PDI resulted sensitive to the conversion from MCI to AD, even though only PDI resulted specific. In particular, the intrasubject variability study showed that the three patients who converted to AD exhibited a significantly ([Formula: see text]) increased PDI (reduced coupling strength) in delta and theta bands. As regards Coherence, even though it significantly decreased in the three converted patients, in delta and theta bands, such a behavior was also detectable in one stable MCI patient, in delta band, thus making Coherence not specific. From the Dissimilarity Index point of view, the converted MCI showed no peculiar behavior. Conclusions: PDI significantly increased, in delta and theta bands, specifically in the MCI subjects who converted to AD. The increase of PDI reflects a reduced coupling strength among the brain areas, which is consistent with the expected connectivity reduction associated to AD progression.

Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.

Neuroimaging Measures to Assess the Effectiveness of a Two-Day Marathon Group of Individuals with Early Developmental Trauma: A Pilot Study

Group therapy can be effective with individuals with developmental trauma who re-experience intense physiological traumatic distress and struggle with terror and despair. This modality can address the long-term ramifications of developmental trauma, including problems with perceptions, trust, emotional regulation, and loss of sense of self. Neuroimaging techniques can be combined with group therapy interventions as a way to empirically validate the effectiveness of group psychotherapy on brain structures and networks impacted by trauma. The neuroscience explaining overpowering traumatic responses and related emotions will be reviewed. Results of a pilot study combining group therapy with neuroimaging are presented.

Quantitative EEG parameters correlate with the progression of human prion diseases

BACKGROUND

Prion diseases are universally fatal and often rapidly progressive neurodegenerative diseases. EEG has long been used in the diagnosis of sporadic Creutzfeldt-Jakob disease; however, the characteristic waveforms do not occur in all types of prion diseases. Here, we re-evaluate the utility of EEG by focusing on the development of biomarkers. We test whether abnormal quantitative EEG parameters can be used to measure disease progression in prion diseases or predict disease onset in healthy individuals at risk of disease.

METHODS

In the National Prion Monitoring Cohort study, we did quantitative encephalography on 301 occasions in 29 healthy controls and 67 patients with prion disease. The patients had either inherited prion disease or sporadic Creutzfeldt-Jakob disease. We computed the main background frequency, the α and θ power and the α/θ power ratio, then averaged these within 5 electrode groups. These measurements were then compared among participant groups and correlated with functional and cognitive scores cross-sectionally and longitudinally.

RESULTS

We found lower main background frequency, α power and α/θ power ratio and higher θ power in patients compared to control participants. The main background frequency, the power in the α band and the α/θ power ratio also differed in a consistent way among the patient groups. Moreover, the main background frequency and the α/θ power ratio correlated significantly with functional and cognitive scores. Longitudinally, change in these parameters also showed significant correlation with the change in clinical and cognitive scores.

CONCLUSIONS

Our findings support the use of quantitative EEG to follow the progression of prion disease, with potential to help evaluate the treatment effects in future clinical-trials.

An Alpha and Theta Intensive and Short Neurofeedback Protocol for Healthy Aging Working-Memory Training

The present study tested the effects of an intensive and short alpha and theta neurofeedback (NF) protocol in working memory (WM) performance in a healthy elder population and explored the effects of a multimodal approach, by supplementing NF with cognitive tasks. Participants were allocated to four groups: NF (N = 9); neurofeedback supplemented with cognitive training (NFCT) (N = 8); cognitive training (CT) (N = 7) and sham neurofeedback (Sham-NF) (N = 6). The intervention consisted in 30-min sessions for 8 days. The NF group presented post intervention increases of alpha and theta relative power as well as performance in the matrix rotation task. In addition, a successful up training of frontal theta showed positive correlation with an improvement of post-training alpha and a better performance in the matrix rotation task. The results presented herein suggest that an intensive and short NF protocol enables elders to learn alpha and theta self-modulation and already presents moderate improvements in cognition and basal EEG. Also, CT group showed moderate performance gains on the cognitive tasks used during the training sessions but no clear improvements on neurophysiology and behavioral measurements were observed. This study represents a first attempt to study the effects of an intensive and short NF protocol in WM performance of elders. The evidence presented here suggests that an intensive and short NF intervention could be a valid alternative for introduction of older populations to NF methodologies.

Neuronal Network Oscillations in Neurodegenerative Diseases

Cognitive and behavioral acts go along with highly coordinated spatiotemporal activity patterns in neuronal networks. Most of these patterns are synchronized by coherent membrane potential oscillations within and between local networks. By entraining multiple neurons into a common time regime, such network oscillations form a critical interface between cellular activity and large-scale systemic functions. Synaptic integrity is altered in neurodegenerative diseases, and it is likely that this goes along with characteristic changes of coordinated network activity. This notion is supported by EEG recordings from human patients and from different animal models of such disorders. However, our knowledge about the pathophysiology of network oscillations in neurodegenerative diseases is surprisingly incomplete, and increased research efforts are urgently needed. One complicating factor is the pronounced diversity of network oscillations between different brain regions and functional states. Pathological changes must, therefore, be analyzed separately in each condition and affected area. However, cumulative evidence from different diseases may result, in the future, in more unifying "oscillopathy" concepts of neurodegenerative diseases. In this review, we report present evidence for pathological changes of network oscillations in Alzheimer's disease (AD), one of the most prominent and challenging neurodegenerative disorders. The heterogeneous findings from AD are contrasted to Parkinson's disease, where motor-related changes in specific frequency bands do already fulfill criteria of a valid biomarker.

Neuroplastic effects of combined computerized physical and cognitive training in elderly individuals at risk for dementia: an eLORETA controlled study on resting states

The present study investigates whether a combined cognitive and physical training may induce changes in the cortical activity as measured via electroencephalogram (EEG) and whether this change may index a deceleration of pathological processes of brain aging. Seventy seniors meeting the clinical criteria of mild cognitive impairment (MCI) were equally divided into 5 groups: 3 experimental groups engaged in eight-week cognitive and/or physical training and 2 control groups: active and passive. A 5-minute long resting state EEG was measured before and after the intervention. Cortical EEG sources were modelled by exact low resolution brain electromagnetic tomography (eLORETA). Cognitive function was assessed before and after intervention using a battery of neuropsychological tests including the minimental state examination (MMSE). A significant training effect was identified only after the combined training scheme: a decrease in the post- compared to pre-training activity of precuneus/posterior cingulate cortex in delta, theta, and beta bands. This effect was correlated to improvements in cognitive capacity as evaluated by MMSE scores. Our results indicate that combined physical and cognitive training shows indices of a positive neuroplastic effect in MCI patients and that EEG may serve as a potential index of gains versus cognitive declines and neurodegeneration. This trial is registered with ClinicalTrials.gov Identifier NCT02313935.

Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging

Rhythmic brain activity at low frequencies (<12Hz) during rest are thought to increase in neurodegenerative disease, but findings in healthy neurocognitive aging are mixed. Here we address two reasons conventional spectral analyses may have led to inconsistent results. First, spectral-power measures are compared to a baseline condition; when resting activity is the signal of interest, it is unclear what the baseline should be. Second, conventional methods do not clearly differentiate power due to rhythmic versus non-rhythmic activity. The Better OSCillation detection method (BOSC; Caplan et al., 2001; Whitten et al., 2011) avoids these problems by using the signal's own spectral characteristics as a reference to detect elevations in power lasting a few cycles. We recorded electroencephalographic (EEG) signal during rest, alternating eyes open and closed, in healthy younger (18-25 years) and older (60-74 years) participants. Topographic plots suggested the conventional and BOSC analyses measured different sources of activity, particularly at frequencies, like delta (1-4Hz), at which rhythms are sporadic; topographies were more similar in the 8-12Hz alpha band. There was little theta-band activity meeting the BOSC method's criteria, suggesting prior findings of theta power in healthy aging may reflect non-rhythmic signal. In contrast, delta oscillations were present at higher levels than theta in both age groups. In summary, applying strict and standardized criteria for rhythmicity, slow rhythms appear present in the resting brain at delta and alpha, but not theta frequencies, and appear unchanged in healthy aging.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene

To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults

The individual alpha frequency (IAF) of the human EEG reflects systemic properties of the brain, is highly heritable, and relates to cognitive functioning. Not much is known about the modifiability of IAF by cognitive interventions. We report analyses of resting EEG from a large-scale training study in which healthy younger (20-31 years, N = 30) and older (65-80 years, N = 28) adults practiced 12 cognitive tasks for ∼100 1-h sessions. EEG was recorded before and after the cognitive training intervention. In both age groups, IAF (and, in a control analysis, alpha amplitude) did not change, despite large gains in cognitive performance. As within-session reliability and test-retest stability were high for both age groups, imprecise measurements cannot account for the findings. In sum, IAF is highly stable in healthy adults up to 80 years, not easily modifiable by cognitive interventions alone, and thus qualifies as a stable neurophysiological trait marker.

Evolution of source EEG synchronization in early Alzheimer's disease

Alzheimer's disease (AD) disrupts functional connectivity in distributed cortical networks. We analyzed changes in the S-estimator, a measure of multivariate intraregional synchronization, in electroencephalogram (EEG) source space in 15 mild AD patients versus 15 age-matched controls to evaluate its potential as a marker of AD progression. All participants underwent 2 clinical evaluations and 2 EEG recording sessions on diagnosis and after a year. The main effect of AD was hyposynchronization in the medial temporal and frontal regions and relative hypersynchronization in posterior cingulate, precuneus, cuneus, and parietotemporal cortices. However, the S-estimator did not change over time in either group. This result motivated an analysis of rapidly progressing AD versus slow-progressing patients. Rapidly progressing AD patients showed a significant reduction in synchronization with time, manifest in left frontotemporal cortex. Thus, the evolution of source EEG synchronization over time is correlated with the rate of disease progression and should be considered as a cost-effective AD biomarker.