Brain health in diverse settings: How age, demographics and cognition shape brain function.

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

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.

Parallel electrophysiological abnormalities due to COVID-19 infection and to Alzheimer's disease and related dementia.

Many coronavirus disease 2019 (COVID-19) positive individuals exhibit abnormal electroencephalographic (EEG) activity reflecting "brain fog" and mild cognitive impairments even months after the acute phase of infection. Resting-state EEG abnormalities include EEG slowing (reduced alpha rhythm; increased slow waves) and epileptiform activity. An expert panel conducted a systematic review to present compelling evidence that cognitive deficits due to COVID-19 and to Alzheimer's disease and related dementia (ADRD) are driven by overlapping pathologies and neurophysiological abnormalities. EEG abnormalities seen in COVID-19 patients resemble those observed in early stages of neurodegenerative diseases, particularly ADRD. It is proposed that similar EEG abnormalities in Long COVID and ADRD are due to parallel neuroinflammation, astrocyte reactivity, hypoxia, and neurovascular injury. These neurophysiological abnormalities underpinning cognitive decline in COVID-19 can be detected by routine EEG exams. Future research will explore the value of EEG monitoring of COVID-19 patients for predicting long-term outcomes and monitoring efficacy of therapeutic interventions. HIGHLIGHTS: Abnormal intrinsic electrophysiological brain activity, such as slowing of EEG, reduced alpha wave, and epileptiform are characteristic findings in COVID-19 patients. EEG abnormalities have the potential as neural biomarkers to identify neurological complications at the early stage of the disease, to assist clinical assessment, and to assess cognitive decline risk in Long COVID patients. Similar slowing of intrinsic brain activity to that of COVID-19 patients is typically seen in patients with mild cognitive impairments, ADRD. Evidence presented supports the idea that cognitive deficits in Long COVID and ADRD are driven by overlapping neurophysiological abnormalities resulting, at least in part, from neuroinflammatory mechanisms and astrocyte reactivity. Identifying common biological mechanisms in Long COVID-19 and ADRD can highlight critical pathologies underlying brain disorders and cognitive decline. It elucidates research questions regarding cognitive EEG and mild cognitive impairment in Long COVID that have not yet been adequately investigated.

Resting State Alpha Electroencephalographic Rhythms Are Affected by Sex in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment: A Retrospective and Exploratory Study.

In the present retrospective and exploratory study, we tested the hypothesis that sex may affect cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms recorded in normal elderly (Nold) seniors and patients with Alzheimer's disease and mild cognitive impairment (ADMCI). Datasets in 69 ADMCI and 57 Nold individuals were taken from an international archive. The rsEEG rhythms were investigated at individual delta, theta, and alpha frequency bands and fixed beta (14-30 Hz) and gamma (30-40 Hz) bands. Each group was stratified into matched females and males. The sex factor affected the magnitude of rsEEG source activities in the Nold seniors. Compared with the males, the females were characterized by greater alpha source activities in all cortical regions. Similarly, the parietal, temporal, and occipital alpha source activities were greater in the ADMCI-females than the males. Notably, the present sex effects did not depend on core genetic (APOE4), neuropathological (Aß42/phospho-tau ratio in the cerebrospinal fluid), structural neurodegenerative and cerebrovascular (MRI) variables characterizing sporadic AD-related processes in ADMCI seniors. These results suggest the sex factor may significantly affect neurophysiological brain neural oscillatory synchronization mechanisms underpinning the generation of dominant rsEEG alpha rhythms to regulate cortical arousal during quiet vigilance.

Event-related delta and theta responses may reflect the valence discrimination in the emotional oddball task.

How emotion and cognition interact is still a matter of debate. Investigation of this interaction in terms of the brain oscillatory dynamics appears to be an essential approach. To investigate this topic, we designed two separate three-stimulus oddball tasks, including emotional stimuli with different valences. Twenty healthy young subjects were included in the study. They completed two tasks, namely: the positive emotional oddball task and the negative emotional oddball task. Each task included the target, non-target, and distractor stimuli. Positive and negative pictures were the target stimuli in the positive and negative emotional oddball task. We asked participants to determine the number of target stimuli in each task. During sessions, EEGs were recorded with 32 electrodes. We found that (negative) target stimuli elicit higher delta (1-3.5 Hz) and theta (4-7 Hz) power responses but not the phase-locking responses compared to (positive) distractor stimuli during the negative oddball task. On the other hand, the same effect was not seen during the positive emotional oddball task. Here, we showed that the valence dimension interacted with the target status. Finally, we summarized our results that the presence of negative distractors attenuated the target effect of the positive stimuli due to the negative bias.

Oscillatory delta and theta frequencies differentially support multiple items encoding to optimize memory performance during the digit span task.

The human brain has limited storage capacity often challenging the encoding and recall of a long series of multiple items. Different encoding strategies are therefore employed to optimize performance in memory processes such as chunking where particular items are 'grouped' to reduce the number of items to store artificially. Additionally, related to the position of an item within a series, there is a tendency to remember the first and last items on the list better than the middle ones, which calls the "serial position effect". Although relatively well-established in behavioral research, the neuronal mechanisms underlying such encoding strategies and memory effects remain poorly understood. Here, we used event-related EEG oscillation analyses to unravel the neuronal substrates of serial encoding strategies and effects during the behaviorally controlled execution of the digit span task. We recorded EEG in forty-four healthy young-adult participants during a backward digit span (ds) task with two difficulty levels (i.e., 3-ds and 5-ds). Participants were asked to recall the digits in reverse order after the presentation of each set. We analyzed the pattern of event-related delta and theta oscillatory power in the time-frequency domain over fronto-central and parieto-occipital areas during the item (digit) list encoding, focusing on how these oscillatory responses changed with each subsequent digit being encoded in the series. Results showed that the development of event-related delta power evoked by digits in each series matched the 'serial position curve', with higher delta power being present during the first, and especially last, digits as compared to digits presented in the middle of a set, for both difficulty levels. Event-related theta power, in contrast, rather resembled a neural correlate of a chunking pattern where, during the 5-ds encoding, a clear change in event-related theta occurred around the third/fourth positions, with decreasing power values for later digits. This suggests that different oscillatory mechanisms linked to different frequency bands may code for the different encoding strategies and effects in serial item presentation. Furthermore, recall-EEG correlations suggested that participants with higher fronto-central delta responses during digit encoding showed also higher recall scores. The here presented findings contribute to our understanding of the neural oscillatory mechanisms underlying multiple item encoding, directly informing recent efforts towards memory enhancement through targeted oscillation-based neuromodulation.

Abnormalities of Cortical Sources of Resting State Alpha Electroencephalographic Rhythms are Related to Education Attainment in Cognitively Unimpaired Seniors and Patients with Alzheimer's Disease and Amnesic Mild Cognitive Impairment.

In normal old (Nold) and Alzheimer's disease (AD) persons, a high cognitive reserve (CR) makes them more resistant and resilient to brain neuropathology and neurodegeneration. Here, we tested whether these effects may affect neurophysiological oscillatory mechanisms generating dominant resting state electroencephalographic (rsEEG) alpha rhythms in Nold and patients with mild cognitive impairment (MCI) due to AD (ADMCI). Data in 60 Nold and 70 ADMCI participants, stratified in higher (Edu+) and lower (Edu-) educational attainment subgroups, were available in an Italian-Turkish archive. The subgroups were matched for age, gender, and education. RsEEG cortical sources were estimated by eLORETA freeware. As compared to the Nold-Edu- subgroup, the Nold-Edu+ subgroup showed greater alpha source activations topographically widespread. On the contrary, in relation to the ADMCI-Edu- subgroup, the ADMCI-Edu+ subgroup displayed lower alpha source activations topographically widespread. Furthermore, the 2 ADMCI subgroups had matched cerebrospinal AD diagnostic biomarkers, brain gray-white matter measures, and neuropsychological scores. The current findings suggest that a high CR may be related to changes in rsEEG alpha rhythms in Nold and ADMCI persons. These changes may underlie neuroprotective effects in Nold seniors and subtend functional compensatory mechanisms unrelated to brain structure alterations in ADMCI patients.

Event-related EEG oscillatory responses elicited by dynamic facial expression.

BACKGROUND: Recognition of facial expressions (FEs) plays a crucial role in social interactions. Most studies on FE recognition use static (image) stimuli, even though real-life FEs are dynamic. FE processing is complex and multifaceted, and its neural correlates remain unclear. Transitioning from static to dynamic FE stimuli might help disentangle the neural oscillatory mechanisms underlying face processing and recognition of emotion expression. To our knowledge, we here present the first time-frequency exploration of oscillatory brain mechanisms underlying the processing of dynamic FEs. RESULTS: Videos of joyful, fearful, and neutral dynamic facial expressions were presented to 18 included healthy young adults. We analyzed event-related activity in electroencephalography (EEG) data, focusing on the delta, theta, and alpha-band oscillations. Since the videos involved a transition from neutral to emotional expressions (onset around 500 ms), we identified time windows that might correspond to face perception initially (time window 1; first TW), and emotion expression recognition subsequently (around 1000 ms; second TW). First TW showed increased power and phase-locking values for all frequency bands. In the first TW, power and phase-locking values were higher in the delta and theta bands for emotional FEs as compared to neutral FEs, thus potentially serving as a marker for emotion recognition in dynamic face processing. CONCLUSIONS: Our time-frequency exploration revealed consistent oscillatory responses to complex, dynamic, ecologically meaningful FE stimuli. We conclude that while dynamic FE processing involves complex network dynamics, dynamic FEs were successfully used to reveal temporally separate oscillation responses related to face processing and subsequently emotion expression recognition.

Electrophysiological and Cognitive Changes in Hard Coal Miners Associated with Working Underground.

Miners working underground face some risk factors that affect the nervous system-such as high noise, dark environment, chronic stress, and exposure to toxic gases. However, it is not known whether these risk factors affect the cognition of miners. In this study, the cognitive changes of miners were examined through event-related oscillations via electroencephalogram (EEG). Twenty underground miners and control groups, equal to each other in age, education level, and working duration, participated in this study. Neuropsychological tests were applied to all participants to examine their cognitive characteristics. Then, 20-channel EEG was recorded for electrophysiological changes during visual oddball paradigm. Event-related power spectrum and phase locking were analyzed in delta (0.5-3.5), theta (4-7), and alpha (8-13 Hz) frequency bands. It was determined that the delta responses that emerged during the target stimulus differed between the two groups in terms of phase locking (p < 0.05). Considering event-related alpha responses, a statistical difference was found regarding power spectrum and phase locking (p < 0.05). Moreover, the alpha power spectrum in the miners was found to be negatively statistically correlated with working duration (p < 0.05). This study determined that the event-related electrophysiological responses of the miners were negatively affected depending on the working conditions. In addition, neuropsychological assessment determined miners had deficiencies in learning and memory skills and many other cognitive functions such as attention, behavioral inhibition, and visual perception.

From Infancy to Childhood: A Comprehensive Review of Event- and Task-Related Brain Oscillations.

Brain development from infancy through childhood involves complex structural and functional changes influenced by both internal and external factors. This review provides a comprehensive analysis of event and task-related brain oscillations, focusing on developmental changes across different frequency bands, including delta, theta, alpha, beta, and gamma. Electroencephalography (EEG) studies highlight that these oscillations serve as functional building blocks for sensory and cognitive processes, with significant variations observed across different developmental stages. Delta oscillations, primarily associated with deep sleep and early cognitive demands, gradually diminish as children age. Theta rhythms, crucial for attention and memory, display a distinct pattern in early childhood, evolving with cognitive maturation. Alpha oscillations, reflecting thalamocortical interactions and cognitive performance, increase in complexity with age. Beta rhythms, linked to active thinking and problem-solving, show developmental differences in motor and cognitive tasks. Gamma oscillations, associated with higher cognitive functions, exhibit notable changes in response to sensory stimuli and cognitive tasks. This review underscores the importance of understanding oscillatory dynamics to elucidate brain development and its implications for sensory and cognitive processing in childhood. The findings provide a foundation for future research on developmental neuroscience and potential clinical applications.

Reconstructing brain functional networks through identifiability and deep learning.

We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer's and Parkinson's disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting-state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson's disease patients.

Lower oddball event-related EEG delta and theta responses in patients with dementia due to Parkinson's and Lewy body than Alzheimer's disease.

Oddball task-related EEG delta and theta responses are associated with frontal executive functions, which are significantly impaired in patients with dementia due to Parkinson's disease (PDD) and Lewy bodies (DLB). The present study investigated the oddball task-related EEG delta and theta responses in patients with PDD, DLB, and Alzheimer's disease dementia (ADD). During visual and auditory oddball paradigms, EEG activity was recorded in 20 ADD, 17 DLB, 20 PDD, and 20 healthy (HC) older adults. Event-related EEG power spectrum and phase-locking analysis were performed at the delta (1-4 Hz) and theta (4-7 Hz) frequency bands for target and nontarget stimuli. Compared to the HC persons, dementia groups showed lower frontal and central delta and theta power and phase-locking associated with task performance and neuropsychological test scores. Notably, this effect was more significant in the PDD and DLB than in the ADD. In conclusion, oddball task-related frontal and central EEG delta and theta responses may reflect frontal supramodal executive dysfunctions in PDD and DLB patients.

rTMS reduces delta and increases theta oscillations in Alzheimer's disease: A visual-evoked and event-related potentials study.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative therapy for Alzheimer's disease (AD) due to its ability to modulate neural networks and enhance cognitive function. This treatment offers the unique advantage of enabling real-time monitoring of immediate cognitive effects and dynamic brain changes through electroencephalography (EEG). OBJECTIVE: This study focused on exploring the effects of left parietal rTMS stimulation on visual-evoked potentials (VEP) and visual event-related potentials (VERP) in AD patients. METHODS: Sixteen AD patients were recruited for this longitudinal study. EEG data were collected within a Faraday cage both pre- and post-rTMS to evaluate its impact on potentials. RESULTS: Significant alterations were found in both VEP and VERP oscillations. Specifically, delta power in VEP decreased, while theta power in VERP increased post-rTMS, indicating a modulation of brain activities. DISCUSSION: These findings confirm the positive modulatory impact of rTMS on brain activities in AD, evidenced by improved cognitive scores. They align with previous studies highlighting the potential of rTMS in managing hyperexcitability and oscillatory disturbances in the AD cortex. CONCLUSION: Cognitive improvements post-rTMS endorse its potential as a promising neuromodulatory treatment for cognitive enhancement in AD, thereby providing critical insights into the neurophysiological anomalies in AD and possible therapeutic avenues.

Neuroecological links of the exposome and One Health.

This NeuroView assesses the interplay among exposome, One Health, and brain capital in health and disease. Physical and social exposomes affect brain health, and green brain skills are required for environmental health strategies. Ibanez et al. address current gaps and strategies needed in research, policy, and technology, offering a road map for stakeholders.

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

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

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

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

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Brain clocks capture diversity and disparity in aging and dementia.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Review of delta, theta, alpha, beta, and gamma response oscillations in neuropsychiatric disorders.

Method and concepts of brain oscillations pervade the neuroscience literature, especially in cognitive processes. Electrophysiological changes in patients with cognitive impairment will provide fundamental knowledge, not only for clinical studies but also, in turn, for understanding cognitive processes in healthy subjects. This review includes description of brain oscillations in schizophrenia, bipolar disorder, mild cognitive impairment, Alzheimer's disease, and attention deficit hyperactivity disorder. The reviewed publications include several methodological approaches: analysis of spontaneous electroencephalogram (EEG) spectra, evoked oscillations, event-related oscillations, and coherences both in spontaneous EEG and event-related oscillations. The review clearly shows that, in cognitive impairment, fundamental changes are observed in all diseases under study. Accordingly, oscillations can most probably be used as biomarkers in clinical studies. The conclusions of this review include several remarks indicating the nature of brain oscillations, their application to cognitive processes, and the usefulness of recording brain oscillations in memory loss, attention deficit, and learning.

Brain's alpha, beta, gamma, delta, and theta oscillations in neuropsychiatric diseases: proposal for biomarker strategies.

Brain oscillations have gained tremendous importance in neuroscience during recent decades as functional building blocks of sensory-cognitive processes. Research also shows that event-related oscillations (EROs) in "alpha," "beta," "gamma," "delta," and "theta" frequency windows are highly modified in pathological brains, especially in patients with cognitive impairment. The strategies and methods applied in the present report reflect the innate organization of the brain: "the whole brain work." The present paper is an account of methods such as evoked/event-related spectra, evoked/ERDs, coherence analysis, and phase-locking. The report does not aim to cover all strategies related to the systems theory applied in brain research literature. However, the essential methods and concepts are applied in several examples from Alzheimer's disease (AD), schizophrenia, and bipolar disorder (BD), and such examples lead to fundamental statements in the search for neurophysiological biomarkers in cognitive impairment. An overview of the results clearly demonstrates that it is obligatory to apply the method of oscillations in multiple electroencephalogram frequency windows in search of functional biomarkers and to detect the effects of drug applications. Again, according to the summary of results in AD patients and BD patients, multiple oscillations and selectively distributed recordings must be analyzed and should include multiple locations. Selective connectivity between selectively distributed neural networks has to be computed by means of spatial coherence. Therefore, by designing a strategy for diagnostics, the differential diagnostics, and application of (preventive) drugs, neurophysiological information should be analyzed within a framework including multiple methods and multiple frequency bands. The application of drugs/neurotransmitters gains a new impact with the analysis of oscillations and coherences. A more clear and differentiated analysis of drug effects can be attained in comparison to the application of the conventional wide-band evoked potential and event-related potential applications.