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Archives of Clinical Neuropsychology :... Oct 2021Cognitive reserve (CR) is the capacity to adapt to (future) brain damage without any or only minimal clinical symptoms. The underlying neuroplastic mechanisms remain...
OBJECTIVE
Cognitive reserve (CR) is the capacity to adapt to (future) brain damage without any or only minimal clinical symptoms. The underlying neuroplastic mechanisms remain unclear. Electrocorticography (ECOG), electroencephalography (EEG), and magnetoencephalography (MEG) may help elucidate the brain mechanisms underlying CR, as CR is thought to be related to efficient utilization of remaining brain resources. The purpose of this systematic review is to collect, evaluate, and synthesize the findings on neural correlates of CR estimates using ECOG, EEG, and MEG.
METHOD
We examined articles that were published from the first standardized definition of CR. Eleven EEG and five MEG cross-sectional studies met the inclusion criteria: They concerned original research, analyzed (M)EEG in humans, used a validated CR estimate, and related (M)EEG to CR. Quality assessment was conducted using an adapted form of the Newcastle-Ottawa scale. No ECOG study met the inclusion criteria.
RESULTS
A total of 1383 participants from heterogeneous patient, young and older healthy groups were divided into three categories by (M)EEG methodology: Eight (M)EEG studies employed event-related fields or potentials, six studies analyzed brain oscillations at rest (of which one also analyzed a cognitive task), and three studies analyzed brain connectivity. Various CR estimates were employed and all studies compared different (M)EEG measures and CR estimates. Several associations between (M)EEG measures and CR estimates were observed.
CONCLUSION
Our findings support that (M)EEG measures are related to CR estimates, particularly in healthy individuals. However, the character of this relationship is dependent on the population and task studied, warranting further studies.
Topics: Brain; Brain Mapping; Cognitive Reserve; Cross-Sectional Studies; Electroencephalography; Humans; Magnetoencephalography; Neuropsychological Tests
PubMed: 33522563
DOI: 10.1093/arclin/acaa132 -
Neuroscience and Biobehavioral Reviews Jul 2022Cross-frequency coupling (CFC), an electrophysiologically derived measure of oscillatory coupling in the brain, is believed to play a critical role in neuronal... (Review)
Review
Cross-frequency coupling (CFC), an electrophysiologically derived measure of oscillatory coupling in the brain, is believed to play a critical role in neuronal computation, learning and communication. It has received much recent attention in the study of both health and disease. We searched for literature that studied CFC during resting state and task-related activities during electroencephalography and magnetoencephalography in psychiatric disorders. Thirty-eight studies were identified, which included attention-deficit hyperactivity disorder, Alzheimer's dementia, autism spectrum disorder, bipolar disorder, depression, obsessive compulsive disorder, social anxiety disorder and schizophrenia. The systematic review was registered with PROSPERO (ID#CRD42021224188). The current review indicates measurable differences exist between CFC in disease states vs. healthy controls. There was variance in CFC at different regions of the brain within the same psychiatric disorders, perhaps this could be explained by the mechanisms and functionality of CFC. There was heterogeneity in methodologies used, which may lead to spurious CFC analyses. Going forward, standardized methodologies need to be established and utilized in further research to understand the neuropathophysiology associated with psychiatric disorders.
Topics: Autism Spectrum Disorder; Brain; Electroencephalography; Humans; Neurons; Obsessive-Compulsive Disorder
PubMed: 35569580
DOI: 10.1016/j.neubiorev.2022.104690 -
Progress in Neuro-psychopharmacology &... Apr 2023There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and... (Review)
Review
There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.
Topics: Infant; Humans; Magnetoencephalography; Autism Spectrum Disorder; Reproducibility of Results; Electroencephalography; Machine Learning
PubMed: 36574922
DOI: 10.1016/j.pnpbp.2022.110705 -
NeuroImage Apr 2019Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that provides whole-head measures of neural activity with millisecond temporal resolution. Over the...
Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that provides whole-head measures of neural activity with millisecond temporal resolution. Over the last three decades, MEG has been used for assessing brain activity, most commonly in adults. MEG has been used less often to examine neural function during early development, in large part due to the fact that infant whole-head MEG systems have only recently been developed. In this review, an overview of infant MEG studies is provided, focusing on the period from birth to three years. The advantages of MEG for measuring neural activity in infants are highlighted (See Box 1), including the ability to assess activity in brain (source) space rather than sensor space, thus allowing direct assessment of neural generator activity. Recent advances in MEG hardware and source analysis are also discussed. As the review indicates, efforts in this area demonstrate that MEG is a promising technology for studying the infant brain. As a noninvasive technology, with emerging hardware providing the necessary sensitivity, an expected deliverable is the capability for longitudinal infant MEG studies evaluating the developmental trajectory (maturation) of neural activity. It is expected that departures from neuro-typical trajectories will offer early detection and prognosis insights in infants and toddlers at-risk for neurodevelopmental disorders, thus paving the way for early targeted interventions.
Topics: Brain; Evoked Potentials; Functional Neuroimaging; Humans; Infant; Magnetoencephalography
PubMed: 30685329
DOI: 10.1016/j.neuroimage.2019.01.059 -
Pain Jun 2023Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy...
Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.
Topics: Adult; Humans; Magnetoencephalography; Chronic Pain; Cross-Sectional Studies; Electroencephalography; Biomarkers
PubMed: 36409624
DOI: 10.1097/j.pain.0000000000002825 -
Clinical Neurophysiology : Official... Sep 2022Bipolar disorder is characterized by aberrant neurophysiological responses as measured with electroencephalography (EEG) and magnetoencephalography (MEG), including the... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Bipolar disorder is characterized by aberrant neurophysiological responses as measured with electroencephalography (EEG) and magnetoencephalography (MEG), including the 40-Hz auditory steady-state response (ASSR). 40-Hz ASSR deficits are also found in patients with schizophrenia and may represent a transdiagnostic biomarker of neuronal circuit dysfunction. In this systematic review and meta-analysis, we summarize and evaluate the evidence for 40-Hz ASSR deficits in patients with bipolar disorder.
METHODS
We identified studies from PubMed, EMBASE, and SCOPUS. We assessed the risk of bias, calculated Hedges' g meta-level effect sizes, and investigated small-study effects using funnel plots and Egger regression.
RESULTS
Seven studies, comprising 396 patients with bipolar disorder and 404 healthy controls, were included in the meta-analysis. Studies displayed methodological heterogeneity and an overall high risk of bias. Patients with bipolar disorder showed consistent reductions in 40-Hz ASSR evoked power (Hedges' g = -0.49; 95% confidence intervals [-0.67, -0.31]) and inter-trial phase coherence (ITPC) (Hedges' g = -0.43; 95 %CI [-0.58, -0.29]) compared with healthy controls.
CONCLUSIONS
Our meta-analysis provides evidence that 40-Hz ASSRs are reduced in patients with bipolar disorder compared with healthy controls.
SIGNIFICANCE
Future large-scale studies are warranted to link 40-Hz ASSR deficits to clinical features and developmental trajectories.
Topics: Acoustic Stimulation; Bipolar Disorder; Electroencephalography; Evoked Potentials, Auditory; Humans; Magnetoencephalography; Schizophrenia
PubMed: 35853310
DOI: 10.1016/j.clinph.2022.06.014 -
Explore (New York, N.Y.) 2018Across cultures and throughout history, transcendent states achieved through meditative practices have been reported. The practices to attain transcendent states vary... (Review)
Review
BACKGROUND
Across cultures and throughout history, transcendent states achieved through meditative practices have been reported. The practices to attain transcendent states vary from transcendental meditation to yoga to contemplative prayer, to other various forms of sitting meditation. While these transcendent states are ascribed many different terms, those who experience them describe a similar unitive, ineffable state of consciousness. Despite the common description, few studies have systematically examined transcendent states during meditation.
OBJECTIVES
The objectives of this systematic review were to: 1) characterize studies evaluating transcendent states associated with meditation in any tradition; 2) qualitatively describe physiological and phenomenological outcomes collected during transcendent states and; 3) evaluate the quality of these studies using the Quality Assessment Tool.
METHODS
Medline, PsycINFO, CINAHL, AltHealthWatch, AMED, and the Institute of Noetic Science Meditation Library were searched for relevant papers in any language. Included studies required adult participants and the collection of outcomes before, during, or after a reported transcendent state associated with meditation.
RESULTS
Twenty-five studies with a total of 672 combined participants were included in the final review. Participants were mostly male (61%; average age 39 ± 11 years) with 12.7 ± 6.6 (median 12.6; range 2-40) average years of meditation practice. A variety of meditation traditions were represented: (Buddhist; Christian; Mixed (practitioners from multiple traditions); Vedic: Transcendental Meditation and Yoga). The mean quality score was 67 ± 13 (100 highest score possible). Subjective phenomenology and the objective outcomes of electroencephalography (EEG), electrocardiography, electromyography, electrooculogram, event-related potentials, functional magnetic resonance imaging, magnetoencephalography, respiration, and skin conductance and response were measured. Transcendent states were most consistently associated with slowed breathing, respiratory suspension, reduced muscle activity and EEG alpha blocking with external stimuli, and increased EEG alpha power, EEG coherence, and functional neural connectivity. The transcendent state is described as being in a state of relaxed wakefulness in a phenomenologically different space-time. Heterogeneity between studies precluded any formal meta-analysis and thus, conclusions about outcomes are qualitative and preliminary.
CONCLUSIONS
Future research is warranted into transcendent states during meditation using more refined phenomenological tools and consistent methods and outcome evaluation.
Topics: Adult; Consciousness; Female; Humans; Male; Meditation; Middle Aged; Religion and Psychology; Yoga
PubMed: 29269049
DOI: 10.1016/j.explore.2017.07.007 -
Clinical Neurophysiology : Official... Jun 2015To systematically evaluate evidence for configural and affective face processing abnormalities as measured by the N170 and Vertex Positive Potential (VPP) event-related... (Review)
Review
OBJECTIVE
To systematically evaluate evidence for configural and affective face processing abnormalities as measured by the N170 and Vertex Positive Potential (VPP) event-related potential components, and analogous M170 magnetoencephalography (MEG) component, in neurological and psychiatric disorders.
METHODS
1251 unique articles were identified using PsychINFO and PubMed databases. Sixty-seven studies were selected for review, which employed various tasks to measure the N170, M170 or VPP; the 13 neurological/psychiatric conditions were Attention-Deficit Hyperactivity Disorder (ADHD), Alcohol Dependence, Alzheimer's Disease, Autism Spectrum Disorders (ASDs), Bipolar Disorder, Bulimia Nervosa, Fibromyalgia, Huntington's Disease, Major Depressive Disorder, Parkinson's Disease, Prosopagnosia, Schizophrenia and Social Phobia.
RESULTS
Smaller N170 and VPP amplitudes to faces compared to healthy controls were consistently reported in Schizophrenia but not in ASDs. In Schizophrenia N170 and VPP measures were not correlated with clinical symptoms. Findings from other disorders were highly inconsistent; however, reported group differences were almost always smaller amplitudes or slower latencies to emotional faces in disordered groups regardless of diagnosis.
CONCLUSIONS
Results suggest that N170/VPP abnormalities index non-specific facial affect processing dysfunction in these neurological and psychiatric conditions, reflecting social impairments being broadly characteristic of these groups.
SIGNIFICANCE
The N170 and analogous components hold promise as diagnostic and treatment monitoring biomarkers for social dysfunction.
Topics: Alzheimer Disease; Attention Deficit Disorder with Hyperactivity; Bipolar Disorder; Electroencephalography; Evoked Potentials; Facial Expression; Humans; Magnetoencephalography; Mental Disorders; Nervous System Diseases; Schizophrenia; Visual Perception
PubMed: 25306210
DOI: 10.1016/j.clinph.2014.09.015 -
Brain Topography Mar 2023Background Magnetoencephalography (MEG) and electroencephalography (EEG) record two main types of data: continuous measurements at rest or during sleep, and... (Review)
Review
Background Magnetoencephalography (MEG) and electroencephalography (EEG) record two main types of data: continuous measurements at rest or during sleep, and event-related potentials/evoked magnetic fields (ERPs/EMFs) that involve specific and repetitive tasks. In this systematic review, we summarized longitudinal studies on recovery from post-stroke aphasia that used continuous or event-related temporal imaging (EEG or MEG). Methods We searched PubMed and Scopus for English articles published from 1950 to May 31, 2022. Results 34 studies were included in this review: 11 were non-interventional studies and 23 were clinical trials that used specific rehabilitation methods, neuromodulation, or drugs. The results of the non-interventional studies suggested that poor language recovery was associated with slow-wave activity persisting over time. The results of some clinical trials indicated that behavioral improvements were correlated with significant modulation of the N400 component. Discussion Compared with continuous EEG, ERP/EMF may more reliably identify biomarkers of therapy-induced effects. Electrophysiology should be used more often to explore language processes that are impaired after a stroke, as it may highlight treatment challenges for patients with post-stroke aphasia.
Topics: Humans; Male; Female; Electroencephalography; Evoked Potentials; Aphasia; Stroke; Magnetoencephalography
PubMed: 36749552
DOI: 10.1007/s10548-023-00941-4 -
Biological Psychiatry May 2023Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of... (Review)
Review
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
Topics: Child; Child, Preschool; Humans; Infant; Machine Learning; Neuroimaging
PubMed: 36759257
DOI: 10.1016/j.biopsych.2022.10.014