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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 -
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 -
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 -
NeuroImage. Clinical 2021Magnetoencephalography (MEG), allows for a high degree temporal and spatial accuracy in recording cortical oscillatory activity and evoked fields. To date, no review has... (Review)
Review
INTRODUCTION
Magnetoencephalography (MEG), allows for a high degree temporal and spatial accuracy in recording cortical oscillatory activity and evoked fields. To date, no review has been undertaken to synthesise all MEG studies in Multiple Sclerosis (MS). We undertook a Systematic Review of the utility of MEG in MS.
METHODS
We identified MEG studies carried out in MS using EMBASE, Medline, Cochrane, TRIP and Psychinfo databases. We included original research articles with a cohort of minimum of five multiple sclerosis patients and quantifying of at least one MEG parameter. We used a modified version of the JBI (mJBI) for case-control studies to assess for risk of bias.
RESULTS
We identified 30 studies from 13 centres involving at least 433 MS patients and 347 controls. We found evidence that MEG shows perturbed activity (most commonly reduced power modulations), reduced connectivity and association with altered clinical function in Multiple Sclerosis. Specific replicated findings were decreased motor induced responses in the beta band, diminished increase of gamma power after visual stimulation, increased latency and reduced connectivity for somatosensory evoked fields. There was an association between upper alpha connectivity and cognitive measures in people with MS. Overall studies were of moderate quality (mean mJBI score 6.7).
DISCUSSION
We find evidence for the utility of MEG in Multiple Sclerosis. Event-related designs are of particular value and show replicability between centres. At this stage, it is not clear whether these changes are specific to Multiple Sclerosis or are also observable in other diseases. Further studies should look to explore cognitive control in more depth using in-task designs and undertake longitudinal studies to determine whether these changes have prognostic value.
Topics: Case-Control Studies; Cohort Studies; Humans; Magnetoencephalography; Multiple Sclerosis; Photic Stimulation
PubMed: 34537682
DOI: 10.1016/j.nicl.2021.102814 -
NeuroImage. Clinical 2021The global incidence of traumatic brain injuries is rising, with at least 80% being classified as mild. These mild injuries are not visible on routine clinical imaging.... (Review)
Review
BACKGROUND
The global incidence of traumatic brain injuries is rising, with at least 80% being classified as mild. These mild injuries are not visible on routine clinical imaging. The potential clinical role of a specific imaging biomarker be it diagnostic, prognostic or directing and monitoring progress of personalised treatment and rehabilitation has driven the exploration of several new neuroimaging modalities. This systematic review examined the evidence for magnetoencephalography (MEG) to provide an imaging biomarker in mild traumatic brain injury (mTBI).
METHODS
Our review was prospectively registered on PROSPERO: CRD42019151387. We searched EMBASE, MEDLINE, trial registers, PsycINFO, Cochrane Library and conference abstracts and identified 37 papers describing MEG changes in mTBI eligible for inclusion. Since meta-analysis was not possible, based on the heterogeneity of reported outcomes, we provide a narrative synthesis of results.
RESULTS
The two most promising MEG biomarkers are excess resting state low frequency power, and widespread connectivity changes in all frequency bands. These may represent biomarkers with potential for diagnostic application, which reflect time sensitive changes, or may be capable of offering clinically relevant prognostic information. In addition, the rich data that MEG produces are well-suited to new methods of machine learning analysis, which is now being actively explored.
INTERPRETATION
MEG reveals several promising biomarkers, in the absence of structural abnormalities demonstrable with either computerised tomography or magnetic resonance imaging. This review has not identified sufficient evidence to support routine clinical use of MEG in mTBI currently. However, verifying MEG's potential would help meet an urgent clinical need within civilian, sports and military medicine.
Topics: Adult; Brain; Brain Concussion; Brain Injuries, Traumatic; Humans; Magnetic Resonance Imaging; Magnetoencephalography
PubMed: 34010785
DOI: 10.1016/j.nicl.2021.102697 -
NeuroImage. Clinical 2023Fatigue is a highly prevalent and disabling symptom of many disorders and syndromes, resulting from different pathomechanisms. However, whether and how different...
Fatigue is a highly prevalent and disabling symptom of many disorders and syndromes, resulting from different pathomechanisms. However, whether and how different mechanisms converge and result in similar symptomatology is only partially understood, and transdiagnostic biomarkers that could further the diagnosis and treatment of fatigue are lacking. We, therefore, performed a transdiagnostic systematic review (PROSPERO: CRD42022330113) of quantitative resting-state electroencephalography (EEG) and magnetoencephalography (MEG) studies in adult patients suffering from pathological fatigue in different disorders. Studies investigating fatigue in healthy participants were excluded. The risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semi-quantitative data synthesis was conducted using modified albatross plots. After searching MEDLINE, Web of Science Core Collection, and EMBASE, 26 studies were included. Cross-sectional studies revealed increased brain activity at theta frequencies and decreased activity at alpha frequencies as potential diagnostic biomarkers. However, the risk of bias was high in many studies and domains. Together, this transdiagnostic systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of pathological fatigue. Beyond, this review might help to guide future M/EEG studies on the development of fatigue biomarkers.
Topics: Adult; Humans; Biomarkers; Cross-Sectional Studies; Electroencephalography; Fatigue; Magnetoencephalography
PubMed: 37632989
DOI: 10.1016/j.nicl.2023.103500 -
The Journal of Headache and Pain Aug 2022The diagnosis of migraine is mainly clinical and self-reported, which makes additional examinations unnecessary in most cases. Migraine can be subtyped into chronic (CM)... (Review)
Review
Headache-related circuits and high frequencies evaluated by EEG, MRI, PET as potential biomarkers to differentiate chronic and episodic migraine: Evidence from a systematic review.
BACKGROUND
The diagnosis of migraine is mainly clinical and self-reported, which makes additional examinations unnecessary in most cases. Migraine can be subtyped into chronic (CM) and episodic (EM). Despite the very high prevalence of migraine, there are no evidence-based guidelines for differentiating between these subtypes other than the number of days of migraine headache per month. Thus, we consider it timely to perform a systematic review to search for physiological evidence from functional activity (as opposed to anatomical structure) for the differentiation between CM and EM, as well as potential functional biomarkers. For this purpose, Web of Science (WoS), Scopus, and PubMed databases were screened.
FINDINGS
Among the 24 studies included in this review, most of them (22) reported statistically significant differences between the groups of CM and EM. This finding is consistent regardless of brain activity acquisition modality, ictal stage, and recording condition for a wide variety of analyses. That speaks for a supramodal and domain-general differences between CM and EM that goes beyond a differentiation based on the days of migraine per month. Together, the reviewed studies demonstrates that electro- and magneto-physiological brain activity (M/EEG), as well as neurovascular and metabolic recordings from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), show characteristic patterns that allow to differentiate between CM and EM groups.
CONCLUSIONS
Although a clear brain activity-based biomarker has not yet been identified to distinguish these subtypes of migraine, research is approaching headache specialists to a migraine diagnosis based not only on symptoms and signs reported by patients. Future studies based on M/EEG should pay special attention to the brain activity in medium and fast frequency bands, mainly the beta band. On the other hand, fMRI and PET studies should focus on neural circuits and regions related to pain and emotional processing.
Topics: Biomarkers; Chronic Disease; Electroencephalography; Headache; Humans; Magnetic Resonance Imaging; Migraine Disorders; Positron-Emission Tomography
PubMed: 35927625
DOI: 10.1186/s10194-022-01465-1 -
PloS One 2017Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.
OBJECTIVES
1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).
METHODS
Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.
RESULTS
Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.
CONCLUSIONS
The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.
Topics: Autistic Disorder; Connectome; Electroencephalography; Humans; Magnetoencephalography
PubMed: 28467487
DOI: 10.1371/journal.pone.0175870