Test-retest reliability of resting-state magnetoencephalography power in sensor and source space

Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam

SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.

Aberrant age-related alterations in spontaneous cortical activity in participants with cerebral palsy

Introduction

Cerebral Palsy (CP) is the most common neurodevelopmental motor disability, resulting in life-long sensory, perception and motor impairments. Moreover, these impairments appear to drastically worsen as the population with CP transitions from adolescents to adulthood, although the underlying neurophysiological mechanisms remain poorly understood.

Methods

We began to address this knowledge gap by utilizing magnetoencephalographic (MEG) brain imaging to study how the amplitude of spontaneous cortical activity (i.e., resting state) is altered during this transition period in a cohort of 38 individuals with spastic diplegic CP (Age range = 9.80-47.50 years, 20 females) and 67 neurotypical controls (NT) (Age range = 9.08-49.40 years, Females = 27). MEG data from a five-minute eyes closed resting-state paradigm were source imaged, and the power within the delta (2-4 Hz), theta (5-7 Hz), alpha (8-12 Hz), beta (15-29 Hz), and gamma (30-59 Hz) frequency bands were computed.

Results

For both groups, the delta and theta spontaneous power decreased in the bilateral temporoparietal and superior parietal regions with age, while alpha, beta, and gamma band spontaneous power increased in temporoparietal, frontoparietal and premotor regions with age. We also found a significant group x age interaction, such that participants with CP demonstrated significantly less age-related increases in the spontaneous beta activity in the bilateral sensorimotor cortices compared to NT controls.

Discussion

Overall, these results demonstrate that the spontaneous neural activity in individuals with CP has an altered trajectory when transitioning from adolescents to adulthood. We suggest that these differences in spontaneous cortical activity may play a critical role in the aberrant motor actions seen in this patient group, and may provide a neurophysiological marker for assessing the effectiveness of current treatment strategies that are directed at improving the mobility and sensorimotor impairments seen in individuals with CP.

Abnormal resting-state functional connectivity underlies cognitive and clinical symptoms in patients with schizophrenia

Introduction

The cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity.

Methods

We capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC).

Results

We found that SZ showed significant global disrupted functional connectivity in delta-theta (2-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition.

Discussion

The multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.

A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset

Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.

Brainprints: identifying individuals from magnetoencephalograms

Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing and the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized.

Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study

Neural oscillations may be sensitive to aspects of brain maturation such as myelination and synaptic density changes. Better characterization of developmental trajectories and reliability is necessary for understanding typical and atypical neurodevelopment. Here, we examined reliability in 110 typically developing children and adolescents (aged 9-17 years) across 2.25 years. From 10 min of magnetoencephalography resting-state data, normalized source spectral power and intraclass correlation coefficients were calculated. We found sex-specific differences in global normalized power, with males showing age-related decreases in delta and theta, along with age-related increases in beta and gamma. Females had fewer significant age-related changes. Structural magnetic resonance imaging revealed that males had more total gray, subcortical gray, and cortical white matter volume. There were significant age-related changes in total gray matter volume with sex-specific and frequency-specific correlations to normalized power. In males, increased total gray matter volume correlated with increased theta and alpha, along with decreased gamma. Split-half reliability was excellent in all frequency bands and source regions. Test-retest reliability ranged from good (alpha) to fair (theta) to poor (remaining bands). While resting-state neural oscillations can have fingerprint-like quality in adults, we show here that neural oscillations continue to evolve in children and adolescents due to brain maturation and neurodevelopmental change.

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Background

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal.

Methods

For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level.

Findings

After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).

Alfa band power increases in posterior brain regions in attention deficit hyperactivity disorder after digital cognitive stimulation treatment

The changes triggered by pharmacological treatments in resting-state alpha-band (8–14 Hz) oscillations have been widely studied in attention deficit hyperactivity disorder. However, to date, there has been no evidence regarding the possible changes in cognitive stimulation treatments on these oscillations. This paper sets out to verify whether cognitive stimulation treatments based on progressive increases in cognitive load can be effective in triggering changes in alpha-band power in attention deficit hyperactivity disorder. With this objective, we compared a cognitive stimulation treatment (n = 13) to placebo treatment (n = 13) for 12 weeks (36 sessions of 15 min) in child patients (8–11 years old) with attention deficit hyperactivity disorder. Two magnetoencephalographic recordings were acquired for all the participants. In order to extract the areas with changes in alpha power between both magnetoencephalographic recordings, the differences in the power ratio (pre/post-condition) were calculated using an Analysis of Covariance test adjusted for the age variable. The results show an increase in the post-treatment power ratio in the experimental group versus the placebo group (P < 0.01) in posterior regions and the default mode network. In addition, these alpha changes were related to measures of attention, working memory and cognitive flexibility. The results seem to indicate that cognitive stimulation treatment based on progressive increases in cognitive load triggers alpha-band power changes in child attention deficit hyperactivity disorder patients in the direction of their peers without this disorder.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BACKGROUND

Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric.

NEW METHODS

We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity.

RESULTS

Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals.

CONCLUSIONS

The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Reproducibility of Rolandic beta rhythm modulation in MEG and EEG

The Rolandic beta rhythm, at ∼20 Hz, is generated in the somatosensory and motor cortices and is modulated by motor activity and sensory stimuli, causing a short lasting suppression that is followed by a rebound of the beta rhythm. The rebound reflects inhibitory changes in the primary sensorimotor (SMI) cortex, and thus it has been used as a biomarker to follow the recovery of patients with acute stroke. The longitudinal stability of beta rhythm modulation is a prerequisite for its use in long-term follow-ups. We quantified the reproducibility of beta rhythm modulation in healthy subjects in a 1-year-longitudinal study both for MEG and EEG at T₀, 1 month (T_1-month, n = 8) and 1 year (T_1-year, n = 19). The beta rhythm (13–25 Hz) was modulated by fixed tactile and proprioceptive stimulations of the index fingers. The relative peak strengths of beta suppression and rebound did not differ significantly between the sessions, and intersession reproducibility was good or excellent according to intraclass correlation-coefficient values (0.70–0.96) both in MEG and EEG. Our results indicate that the beta rhythm modulation to tactile and proprioceptive stimulation is well reproducible within 1 year. These results support the use of beta modulation as a biomarker in long-term follow-up studies, e.g., to quantify the functional state of the SMI cortex during rehabilitation and drug interventions in various neurological impairments.

NEW & NOTEWORTHY The present study demonstrates that beta rhythm modulation is highly reproducible in a group of healthy subjects within a year. Hence, it can be reliably used as a biomarker in longitudinal follow-up studies in different neurological patient groups to reflect changes in the functional state of the sensorimotor cortex.

Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces

Prior studies have used graph analysis of resting‐state magnetoencephalography (MEG) to characterize abnormal brain networks in neurological disorders. However, a present challenge for researchers is the lack of guidance on which network construction strategies to employ. The reproducibility of graph measures is important for their use as clinical biomarkers. Furthermore, global graph measures should ideally not depend on whether the analysis was performed in the sensor or source space. Therefore, MEG data of the 89 healthy subjects of the Human Connectome Project were used to investigate test–retest reliability and sensor versus source association of global graph measures. Atlas‐based beamforming was used for source reconstruction, and functional connectivity (FC) was estimated for both sensor and source signals in six frequency bands using the debiased weighted phase lag index (dwPLI), amplitude envelope correlation (AEC), and leakage‐corrected AEC. Reliability was examined over multiple network density levels achieved with proportional weight and orthogonal minimum spanning tree thresholding. At a 100% density, graph measures for most FC metrics and frequency bands had fair to excellent reliability and significant sensor versus source association. The greatest reliability and sensor versus source association was obtained when using amplitude metrics. Reliability was similar between sensor and source spaces when using amplitude metrics but greater for the source than the sensor space in higher frequency bands when using the dwPLI. These results suggest that graph measures are useful biomarkers, particularly for investigating functional networks based on amplitude synchrony.

Test-retest reproducibility comparison in resting and the mental task states: A sensor and source-level EEG spectral analysis

Previous test-retest analysis of EEG mostly focused on eyes open and eyes closed resting-state. However, less attention was paid to the EEG during the subject-driven mental imaginary task state. In the current study, we compared the test-retest reproducibility of EEG spectrum in three mental imaginary task states (i.e. performed mental arithmetic, recalled the events of their day, and silently sang lyrics) and two resting states (i.e. eyes open and closed) during three EEG sessions. The resting state with eyes closed has the highest reproducibility, while the resting state with eyes opened has the lowest reproducibility for the spectral features of EEG signals at the sensor level. However, the reproducibility during eyes-open ranked higher among the five states at the source level. Moreover, the mental arithmetic state has the highest reproducibility among all the three task states. And its reproducibility in certain rhythms (theta, gamma, etc) was higher than the resting states. The reproducibility of the EEG spectrum was also investigated from the perspective of large-scale brain networks. The dorsal attention network showed the highest reproducibility in a wide frequency range of the alpha and beta rhythms. Our study suggests the importance of task selection based on the target brain region and the target frequency band. This may provide some suggestions for future researchers to choose appropriate experimental paradigms and provide a guideline on EEG study for the basic and clinical applications.

Stimulus-induced narrowband gamma oscillations are test–retest reliable in human EEG

Visual stimulus-induced gamma oscillations in electroencephalogram (EEG) recordings have been recently shown to be compromised in subjects with preclinical Alzheimer’s Disease (AD), suggesting that gamma could be an inexpensive biomarker for AD diagnosis provided its characteristics remain consistent across multiple recordings. Previous magnetoencephalography studies in young subjects have reported consistent gamma power over recordings separated by a few weeks to months. Here, we assessed the consistency of stimulus-induced slow (20–35 Hz) and fast gamma (36–66 Hz) oscillations in subjects (n = 40) (age: 50–88 years) in EEG recordings separated by a year, and tested the consistency in the magnitude of gamma power, its temporal evolution and spectral profile. Gamma had distinct spectral/temporal characteristics across subjects, which remained consistent across recordings (average intraclass correlation of ~0.7). Alpha (8–12 Hz) and steady-state-visually evoked-potentials were also reliable. We further tested how EEG features can be used to identify 2 recordings as belonging to the same versus different subjects and found high classifier performance (AUC of ~0.89), with temporal evolution of slow gamma and spectral profile being most informative. These results suggest that EEG gamma oscillations are reliable across sessions separated over long durations and can also be a potential tool for subject identification.

MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

Modulation of magnetoencephalography alpha band activity by radiofrequency electromagnetic field depicted in sensor and source space

Several studies reported changes in spontaneous electroencephalogram alpha band activity related to radiofrequency electromagnetic fields, but findings showed both an increase and a decrease of its spectral power or no effect. Here, we studied the alpha band modulation after 900 MHz mobile phone radiofrequency exposure and localized cortical regions involved in these changes, via a magnetoencephalography (MEG) protocol with healthy volunteers in a double-blind, randomized, counterbalanced crossover design. MEG was recorded during eyes open and eyes closed resting-state before and after radiofrequency exposure. Potential confounding factors, known to affect alpha band activity, were assessed as control parameters to limit bias. Entire alpha band, lower and upper alpha sub-bands MEG power spectral densities were estimated in sensor and source space. Biochemistry assays for salivary biomarkers of stress (cortisol, chromogranin-A, alpha amylase), heart rate variability analysis and high-performance liquid chromatography for salivary caffeine concentration were realized. Results in sensor and source space showed a significant modulation of MEG alpha band activity after the radiofrequency exposure, with different involved cortical regions in relation to the eyes condition, probably because of different attention level with open or closed eyes. None of the control parameters reported a statistically significant difference between experimental sessions.

Three-year reliability of MEG resting-state oscillatory power

Introduction:

Resting-state oscillatory activity has been extensively studied across a wide array of disorders. Establishing which spectrally- and spatially-specific oscillatory components exhibit test-retest reliability is essential to move the field forward. While studies have shown short-term reliability of MEG resting-state activity, no studies have examined test-retest reliability across an extended period of time to establish the stability of these signals which is critical for reproducibility.

Methods:

We examined 18 healthy adults age 23 – 61 who completed three visits across three years. For each visit participants completed both a resting state MEG and structural MRI scan. MEG data were source imaged, and the cortical power in canonical frequency bands (delta, theta, alpha, beta, low gamma, high gamma) was computed Intra-class correlation coefficients (ICC) were then calculated across the cortex for each frequency band.

Results:

Over three years, power in the alpha and beta bands displayed the highest reliability estimates, while gamma showed the lowest estimates of three-year reliability. Spatially, delta, alpha, and beta all showed the highest degrees of reliability in the parietal cortex. Interestingly, the peak signal for each of these frequency bands was located outside of the parietal cortex, suggesting that reliability estimates were not solely dependent on the signal-to-noise ratio.

Conclusion:

Oscillatory resting-state power in parietal delta, posterior beta, and alpha across most of the cortex are reliable across three years and future MEEG studies may focus on these measures for the development of specific markers.

Three-Year Reliability of MEG Visual and Somatosensory Responses

A major goal of many translational neuroimaging studies is the identification of biomarkers of disease. However, a prerequisite for any such biomarker is robust reliability, which for magnetoencephalography (MEG) and many other imaging modalities has not been established. In this study, we examined the reliability of visual (Experiment 1) and somatosensory gating (Experiment 2) responses in 19 healthy adults who repeated these experiments for three visits spaced 18 months apart. Visual oscillatory and somatosensory oscillatory and evoked responses were imaged, and intraclass correlation coefficients (ICC) were computed to examine the long-term reliability of these responses. In Experiment 1, ICCs showed good reliability for visual theta and alpha responses in occipital cortices, but poor reliability for gamma responses. In Experiment 2, the time series of somatosensory gamma and evoked responses in the contralateral somatosensory cortex showed good reliability. Finally, analyses of spontaneous baseline activity indicated excellent reliability for occipital alpha, moderate reliability for occipital theta, and poor reliability for visual/somatosensory gamma activity. Overall, MEG responses to visual and somatosensory stimuli show a high degree of reliability across 3 years and therefore may be stable indicators of sensory processing long term and thereby of potential interest as biomarkers of disease.

Symptom-Dependent Changes in MEG-Derived Neuroelectric Brain Activity in Traumatic Brain Injury Patients with Chronic Symptoms

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort, Cambridge Centre for Ageing and Neuroscience (CamCAN), baseline: n = 619; mean 16-month follow-up: n = 253) and a chronic symptomatic TBI cohort, Targeted Evaluation, Action and Monitoring of Traumatic Brain Injury (TEAM-TBI), baseline: n = 64; mean 6-month follow-up: n = 39). For the CamCAN cohort, MEG-derived neuroelectric measures showed good long-term test-retest reliability for most of the 103 automatically identified stereotypic regions. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index. Linear classifiers constructed from the 103 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, with p < 0.01 for each-i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful-i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Picture naming yields highly consistent cortical activation patterns: Test-retest reliability of magnetoencephalography recordings

Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a delayed picture naming paradigm, source-localized evoked activity and modulations of cortical oscillations that show high test-retest reliability across measurement days in healthy individuals, demonstrating their applicability in clinical settings. For patients with a language disorder picture naming can be a challenging task. Therefore, we also determined whether a semantic judgment task ('Is this item living?') with a spoken response ("yes"/"no") would suffice to induce comparably consistent activity within brain regions related to language production. The MEG data was collected from 19 healthy participants on two separate days. In picture naming, evoked activity was consistent across measurement days (intraclass correlation coefficient (ICC)>0.4) in the left frontal (400-800 ms after image onset), sensorimotor (200-800 ms), parietal (200-600 ms), temporal (200-800 ms), occipital (400-800 ms) and cingulate (600-800 ms) regions, as well as the right temporal (600-800 ms) region. In the semantic judgment task, consistent evoked activity was spatially more limited, occurring in the left temporal (200-800 ms), sensorimotor (400-800 ms), occipital (400-600 ms) and subparietal (600-800 ms) regions, and the right supramarginal cortex (600-800 ms). The delayed naming task showed typical beta oscillatory suppression in premotor and sensorimotor regions (800-1200 ms) but other consistent modulations of oscillatory activity were mostly observed in posterior cortical regions that have not typically been associated with language processing. The high test-retest consistency of MEG evoked activity in the picture naming task testifies to its applicability in clinical evaluations of language function, as well as in longitudinal MEG studies of language production in clinical and healthy populations.

Resting EEG Asymmetry Markers of Multiple Facets of the Behavioral Approach System: A LORETA Analysis

Previously published models of frontal activity linked high relative left frontal activity to the behavioral approach system (BAS) and impulsivity. Additionally, these models did not account for BAS facets encompassing the anticipation of reward, i.e., goal-driven persistence (BAS–GDP) and reward interest (BAS–RI), from those that deal with the actual hedonic experience of reward, i.e., reward reactivity (BAS–RR) and impulsivity (BAS–I). Using resting electroencephalographic (EEG) recordings, the source localization (LORETA) method allowed us to calculate the hemispheric asymmetry of the current density within the alpha band (7.5–13 Hz) in ten regions of interest. Compared to low BAS subtrait scorers, high BAS subtrait scorers (except for BAS–I) were correlated with greater relative left-sided activity in the superior frontal gyrus (BA10). Further, an isolated effective coherence (iCOH) analysis of the beta activity (21 Hz) disclosed that high impulsive scorers as compared to low impulsive ones had higher connectivity between the superior frontal gyrus and middle temporal gyrus, which was not compensated for by enhanced inhibitory alpha (11 Hz) connectivity between these regions. For the beta frequency, we also found in highly impulsive individuals that (i) both left and right middle temporal lobes directly influenced the activity of the left and right superior frontal lobes, and (ii) a clear decoupling between left and right superior frontal lobes. These findings could indicate reduced control by the supervisory system in more impulsive individuals.

Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG

BACKGROUND

Characteristics from resting-state electroencephalography (rsEEG) provides relevant information about individual differences in cognitive tasks and personality traits. Due to its increasing application, it is crucial to know the reproducibility of several analysis measures of rsEEG.

NEW METHOD

A new brain network construction method was proposed based on simplified forward model (SFM). In addition, we aimed to carry out an extensive examination of the reproducibility of the power spectrum and functional connectivity at both the sensor-level and the source-level. We systematically proposed multiple new pipelines by integration source imaging, time-course extraction and network reconstruction.

RESULTS/COMPARISON WITH EXISTING METHOD(S)

Our results revealed that the reproducibility of eyes-closed was slightly higher than that of eyes-open, and the relative power was more repeatable than the absolute power, especially in high-frequency bands. The reproducibility of the sensor-level was higher than that of the source-level, both for power and connectivity. Remarkably, connectivity measures could be separated into two classes according to their reproducibility. Notably, the reproducibility of power envelope correlation (PEC) was generally the highest among those connectivity measures which are insensitive to volume conduction effect. For the whole-brain network construction, single dipole modeling was better than the dimensionality reduction methods, such as mean or principal component analysis (PCA) of multiple dipoles of a region.

CONCLUSIONS

In conclusion, our results described the reproducibility of rsEEG power spectrum, connectivity measures, and network constructions, which could be considered in assessing inter-individual differences in brain-behavior relationships, as well as automatic biometric applications.

Role of kinaesthetic motor imagery in mirror-induced visual illusion as intervention in post-stroke rehabilitation

Mirror-induced visual illusion obtained through mirror therapy is widely used to facilitate motor recovery after stroke. Activation of primary motor cortex (M1) ipsilateral to the moving limb has been reported during mirror-induced visual illusion. However, the mechanism through which the mirror illusion elicits motor execution processes without movements observed in the mirrored limb remains unclear. This study aims to review evidence based on brain imaging studies for testing the hypothesis that neural processes associated with kinaesthetic motor imagery are attributed to ipsilateral M1 activation. Four electronic databases were searched. Studies on functional brain imaging, investigating the instant effects of mirror-induced visual illusion among stroke survivors and healthy participants were included. Thirty-five studies engaging 78 stroke survivors and 396 healthy participants were reviewed. Results of functional brain scans (n = 20) indicated that half of the studies (n = 10, 50%) reported significant changes in the activation of ipsilateral M1, which mediates motor preparation and execution. Other common neural substrates included primary somatosensory cortex (45%, kinaesthesia), precuneus (40%, image generation and self-processing operations) and cerebellum (20%, motor control). Similar patterns of ipsilateral M1 activations were observed in the two groups. These neural substrates mediated the generation, maintenance, and manipulation of motor-related images, which were the key processes in kinaesthetic motor imagery. Relationships in terms of shared neural substrates and mental processes between mirror-induced visual illusion and kinaesthetic motor imagery generate new evidence on the role of the latter in mirror therapy. Future studies should investigate the imagery processes in illusion training for post-stroke patients.

Anterior temporal lobe is necessary for efficient lateralised processing of spoken word identity

In the healthy human brain, the processing of language is strongly lateralised, usually to the left hemisphere, while the processing of complex non-linguistic sounds recruits brain regions bilaterally. Here we asked whether the anterior temporal lobes, strongly implicated in semantic processing, are critical to this special treatment of spoken words. Nine patients with semantic dementia (SD) and fourteen age-matched controls underwent magnetoencephalography and structural MRI. Voxel based morphometry demonstrated the stereotypical pattern of SD: severe grey matter loss restricted to the anterior temporal lobes, with the left side more affected. During magnetoencephalography, participants listened to word sets in which identity and meaning were ambiguous until word completion, for example PLAYED versus PLATE. Whereas left-hemispheric responses were similar across groups, patients demonstrated increased right hemisphere activity 174-294 msec after stimulus disambiguation. Source reconstructions confirmed recruitment of right-sided analogues of language regions in SD: atrophy of anterior temporal lobes was associated with increased activity in right temporal pole, middle temporal gyrus, inferior frontal gyrus and supramarginal gyrus. Overall, the results indicate that anterior temporal lobes are necessary for normal and efficient lateralised processing of word identity by the language network.

Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia

The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1-58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.

Reduced parietal alpha power and psychotic symptoms: Test-retest reliability of resting-state magnetoencephalography in schizophrenia and healthy controls

BACKGROUND

Despite increased reporting of resting-state magnetoencephalography (MEG), reliability of those measures remains scarce and predominately reported in healthy controls (HC). As such, there is limited knowledge on MEG resting-state reliability in schizophrenia (SZ).

METHODS

To address test-retest reliability in psychosis, a reproducibility study of 26 participants (13-SZ, 13-HC) was performed. We collected eyes open and eyes closed resting-state data during 4 separate instances (2 Visits, 2 runs per visit) to estimate spectral power reliability (power, normalized power, alpha reactivity) across one hour and one week. Intraclass correlation coefficients (ICCs) were calculated. For source modeling, we applied an anatomically constrained linear estimation inverse model known as dynamic statistical parametric mapping (MNE dSPM) and source-based connectivity using the weighted phase lag index.

RESULTS

Across one week there was excellent test-retest reliability in global spectral measures in theta-gamma bands (HC ICC_Avg = 0.87, SZ ICC_Avg = 0.87), regional spectral measures in all bands (HC ICC_Avg = 0.86, SZ ICC_Avg = 0.80), and parietal alpha measures (HC ICC_Avg = 0.90, SZ ICC_Avg = 0.84). Conversely, functional connectivity had poor reliability, as did source spectral power across one hour for SZ. Relative to HC, SZ also had reduced parietal alpha normalized power during eyes closed only, reduced alpha reactivity, and an association between higher PANSS positive scores and lower parietal alpha power.

CONCLUSIONS

There was excellent to good test-retest reliability in most MEG spectral measures with a few exceptions in the schizophrenia patient group. Overall, these findings encourage the use of resting-state MEG while emphasizing the importance of determining reliability in clinical populations.

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.

Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics

Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.

Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics

The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from magnetic resonance imaging (MRI)-derived T1- to T2-weighted (T1w/T2w) mapping and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to functional MRI (fMRI)-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized higher-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.

How to Enhance the Power to Detect Brain-Behavior Correlations With Limited Resources

Neuroscience has been diagnosed with a pervasive lack of statistical power and, in turn, reliability. One remedy proposed is a massive increase of typical sample sizes. Parts of the neuroimaging community have embraced this recommendation and actively push for a reallocation of resources toward fewer but larger studies. This is especially true for neuroimaging studies focusing on individual differences to test brain-behavior correlations. Here, I argue for a more efficient solution. Ad hoc simulations show that statistical power crucially depends on the choice of behavioral and neural measures, as well as on sampling strategy. Specifically, behavioral prescreening and the selection of extreme groups can ascertain a high degree of robust in-sample variance. Due to the low cost of behavioral testing compared to neuroimaging, this is a more efficient way of increasing power. For example, prescreening can achieve the power boost afforded by an increase of sample sizes from n = 30 to n = 100 at ∼5% of the cost. This perspective article briefly presents simulations yielding these results, discusses the strengths and limitations of prescreening and addresses some potential counter-arguments. Researchers can use the accompanying online code to simulate the expected power boost of prescreening for their own studies.

Resting-state brain oscillations predict trait-like cognitive styles

Anecdotal reports suggest the existence of individual differences in peoples' cognitive styles for solving problems, in particular, the tendency to rely on insight (the "aha" phenomenon) versus deliberate analytical thought. We hypothesized that such stable individual differences exist and are associated with trait-like individual differences in resting-state brain activity. We tested this idea by recording participants' resting-state electroencephalograms (RS-EEGs) on 4 occasions over approximately 7 weeks and then tasking them with solving anagrams and compound remote associates problems that are solvable by either strategy. We found that peoples' tendency to solve problems consistently by insight or by analysis spans both tasks and time. Moreover, we discovered trait-like individual differences in the balance between frontal and posterior resting-state brain activity and in temporal-lobe hemispheric asymmetries that predict, at least weeks in advance, the tendency to solve by insight versus analysis. The discovery of an insight-analytic dimension of cognitive style and its neural basis in resting state brain activity suggests new avenues for the development of neuroscience-based methods for intellectual, educational, and vocational assessment.

Choice of Magnetometers and Gradiometers after Signal Space Separation

BACKGROUND

Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer.

METHODS

First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS.

RESULTS

SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r² = 0.3-0.8 before SSS and r² > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r² > 0.8).

CONCLUSIONS

After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments.

Attention training improves aberrant neural dynamics during working memory processing in veterans with PTSD

Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory (WM). Recent studies suggest that attention training reduces PTSD symptomatology, but the underlying neural mechanisms are unknown. We used high-density magnetoencephalography (MEG) to evaluate whether attention training modulates brain regions serving WM processing in PTSD. Fourteen veterans with PTSD completed a WM task during a 306-sensor MEG recording before and after 8 sessions of attention training treatment. A matched comparison sample of 12 combat-exposed veterans without PTSD completed the same WM task during a single MEG session. To identify the spatiotemporal dynamics, each group's data were transformed into the time-frequency domain, and significant oscillatory brain responses were imaged using a beamforming approach. All participants exhibited activity in left hemispheric language areas consistent with a verbal WM task. Additionally, veterans with PTSD and combat-exposed healthy controls each exhibited oscillatory responses in right hemispheric homologue regions (e.g., right Broca's area); however, these responses were in opposite directions. Group differences in oscillatory activity emerged in the theta band (4-8 Hz) during encoding and in the alpha band (9-12 Hz) during maintenance and were significant in right prefrontal and right supramarginal and inferior parietal regions. Importantly, following attention training, these significant group differences were reduced or eliminated. This study provides initial evidence that attention training improves aberrant neural activity in brain networks serving WM processing.

Movement-related beta oscillations show high intra-individual reliability

Oscillatory activity in the beta frequency range (15-30Hz) recorded from human sensorimotor cortex is of increasing interest as a putative biomarker of motor system function and dysfunction. Despite its increasing use in basic and clinical research, surprisingly little is known about the test-retest reliability of spectral power and peak frequency measures of beta oscillatory signals from sensorimotor cortex. Establishing that these beta measures are stable over time in healthy populations is a necessary precursor to their use in the clinic. Here, we used scalp electroencephalography (EEG) to evaluate intra-individual reliability of beta-band oscillations over six sessions, focusing on changes in beta activity during movement (Movement-Related Beta Desynchronization, MRBD) and after movement termination (Post-Movement Beta Rebound, PMBR). Subjects performed visually-cued unimanual wrist flexion and extension. We assessed Intraclass Correlation Coefficients (ICC) and between-session correlations for spectral power and peak frequency measures of movement-related and resting beta activity. Movement-related and resting beta power from both sensorimotor cortices was highly reliable across sessions. Resting beta power yielded highest reliability (average ICC=0.903), followed by MRBD (average ICC=0.886) and PMBR (average ICC=0.663). Notably, peak frequency measures yielded lower ICC values compared to the assessment of spectral power, particularly for movement-related beta activity (ICC=0.386-0.402). Our data highlight that power measures of movement-related beta oscillations are highly reliable, while corresponding peak frequency measures show greater intra-individual variability across sessions. Importantly, our finding that beta power estimates show high intra-individual reliability over time serves to validate the notion that these measures reflect meaningful individual differences that can be utilised in basic research and clinical studies.

The Contribution of Increased Gamma Band Connectivity to Visual Non-Verbal Reasoning in Autistic Children: A MEG Study

Some individuals with autism spectrum (AS) perform better on visual reasoning tasks than would be predicted by their general cognitive performance. In individuals with AS, mechanisms in the brain’s visual area that underlie visual processing play a more prominent role in visual reasoning tasks than they do in normal individuals. In addition, increased connectivity with the visual area is thought to be one of the neural bases of autistic visual cognitive abilities. However, the contribution of such brain connectivity to visual cognitive abilities is not well understood, particularly in children. In this study, we investigated how functional connectivity between the visual areas and higher-order regions, which is reflected by alpha, beta and gamma band oscillations, contributes to the performance of visual reasoning tasks in typically developing (TD) (n = 18) children and AS children (n = 18). Brain activity was measured using a custom child-sized magneto-encephalograph. Imaginary coherence analysis was used as a proxy to estimate the functional connectivity between the occipital and other areas of the brain. Stronger connectivity from the occipital area, as evidenced by higher imaginary coherence in the gamma band, was associated with higher performance in the AS children only. We observed no significant correlation between the alpha or beta bands imaginary coherence and performance in the both groups. Alpha and beta bands reflect top-down pathways, while gamma band oscillations reflect a bottom-up influence. Therefore, our results suggest that visual reasoning in AS children is at least partially based on an enhanced reliance on visual perception and increased bottom-up connectivity from the visual areas.