Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses.

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Decreased beta-band activity in left supramarginal gyrus reflects cognitive decline: Evidence from a large clinical dataset in patients with dementia.

Cognitive impairment is a major concern in clinical medicine. It is usually evaluated with neuropsychological assessments, which have inherent limitations. To compensate for them, magnetoencephalography has already come into clinical use to evaluate the level of cognitive impairment. It evaluates global changes in the frequency of resting-state brain activity, which are associated with cognitive status. However, it remains unclear what neural mechanism causes the frequency changes. To understand this, it is important to identify cortical regions that mainly contribute to these changes. We retrospectively analysed the clinical records from 310 individuals with cognitive impairment who visited the outpatient department at our hospital. The analysis included resting-state magnetoencephalography, neuropsychological assessment, and clinical diagnosis data. Regional oscillatory intensities were estimated from the magnetoencephalography data, which were statistically analysed, along with neuropsychological assessment scores, and the severity of cognitive impairment associated with clinical diagnosis. The regional oscillatory intensity covering a wide range of regions and frequencies was significantly associated with neuropsychological assessment scores and differed between healthy individuals and patients with cognitive impairment. However, these associations and differences in all conditions were overlapped by a single change in beta frequency in the left supramarginal gyrus. High frequency oscillatory intensity in the left supramarginal gyrus is associated with cognitive impairment levels among patients who were concerned about dementia. It provides new insights into cognitive status measurements using magnetoencephalography, which is expected to develop as an objective index to be used alongside traditional neuropsychological assessments.

Mouth magnetoencephalography: A unique perspective on the human hippocampus.

Traditional magnetoencephalographic (MEG) brain imaging scanners consist of a rigid sensor array surrounding the head; this means that they are maximally sensitive to superficial brain structures. New technology based on optical pumping means that we can now consider more flexible and creative sensor placement. Here we explored the magnetic fields generated by a model of the human hippocampus not only across scalp but also at the roof of the mouth. We found that simulated hippocampal sources gave rise to dipolar field patterns with one scalp surface field extremum at the temporal lobe and a corresponding maximum or minimum at the roof of the mouth. We then constructed a fitted dental mould to accommodate an Optically Pumped Magnetometer (OPM). We collected data using a previously validated hippocampal-dependant task to test the empirical utility of a mouth-based sensor, with an accompanying array of left and right temporal lobe OPMs. We found that the mouth sensor showed the greatest task-related theta power change. We found that this sensor had a mild effect on the reconstructed power in the hippocampus (~10% change) but that coherence images between the mouth sensor and reconstructed source images showed a global maximum in the right hippocampus. We conclude that augmenting a scalp-based MEG array with sensors in the mouth shows unique promise for both basic scientists and clinicians interested in interrogating the hippocampus.

Early visual cortical responses produced by checkerboard pattern stimulation.

Visual evoked potentials have been traditionally triggered with flash or reversing checkerboard stimuli and recorded with electroencephalographic techniques, largely but not exclusively in clinical or clinically related settings. They have been crucial in determining the healthy functioning or otherwise of the visual pathways up to and including the cerebral cortex. They have typically given early response latencies of 100ms, the source of which has been attributed to V1, with the prestriate cortex being secondarily activated somewhat later. On the other hand, magnetoencephalographic studies using stimuli better tailored to the physiology of individual, specialized, visual areas have given early latencies of <50ms with the sources localized in both striate (V1) and prestriate cortex. In this study, we used the reversing checkerboard pattern as a stimulus and recorded cortical visual evoked magnetic fields with magnetoencephalography, to establish whether very early responses can be traced to (estimated) in both striate and prestriate cortex, since such a demonstration would enhance considerably the power of this classical approach in clinical investigations. Our results show that cortical responses evoked by checkerboard patterns can be detected before 50ms post-stimulus onset and that their sources can be estimated in both striate and prestriate cortex, suggesting a strong parallel input from the sub-cortex to both striate and prestriate divisions of the visual cortex.

Binocularly suppressed stimuli induce brain activities related to aesthetic emotions.

Introduction: Aesthetic emotions are a class of emotions aroused by evaluating aesthetically appealing objects or events. While evolutionary aesthetics suggests the adaptive roles of these emotions, empirical assessments are lacking. Previous neuroscientific studies have demonstrated that visual stimuli carrying evolutionarily important information induce neural responses even when presented non-consciously. To examine the evolutionary importance of aesthetic emotions, we conducted a neuroscientific study using magnetoencephalography (MEG) to measure induced neural responses to non-consciously presented portrait paintings categorised as biological and non-biological and examined associations between the induced responses and aesthetic ratings. Methods: MEG and pre-rating data were collected from 23 participants. The pre-rating included visual analogue scales for object saliency, facial saliency, liking, and beauty scores, in addition to 'biologi-ness,' which was used for subcategorising stimuli into biological and non-biological. The stimuli were presented non-consciously using a continuous flash suppression paradigm or consciously using binocular presentation without flashing masks, while dichotomic behavioural responses were obtained (beauty or non-beauty). Time-frequency decomposed MEG data were used for correlation analysis with pre-rating scores for each category. Results: Behavioural data revealed that saliency scores of non-consciously presented stimuli influenced dichotomic responses (beauty or non-beauty). MEG data showed that non-consciously presented portrait paintings induced spatiotemporally distributed low-frequency brain activities associated with aesthetic ratings, which were distinct between the biological and non-biological categories and conscious and non-conscious conditions. Conclusion: Aesthetic emotion holds evolutionary significance for humans. Neural pathways are sensitive to visual images that arouse aesthetic emotion in distinct ways for biological and non-biological categories, which are further influenced by consciousness. These differences likely reflect the diversity in mechanisms of aesthetic processing, such as processing fluency, active elaboration, and predictive processing. The aesthetic processing of non-conscious stimuli appears to be characterised by fluency-driven affective processing, while top-down regulatory processes are suppressed. This study provides the first empirical evidence supporting the evolutionary significance of aesthetic processing.

Dorsal brain activity reflects the severity of menopausal symptoms.

OBJECTIVE: The severity of menopausal symptoms, despite being triggered by hormonal imbalance, does not directly correspond to hormone levels in the blood; thus, the level of unpleasantness is assessed using subjective questionnaires in clinical practice. To provide better treatments, alternative objective assessments have been anticipated to support medical interviews and subjective assessments. This study aimed to develop a new objective measurement for assessing unpleasantness. METHODS: Fourteen participants with menopausal symptoms and two age-matched participants who visited our outpatient section were enrolled. Resting-state brain activity was measured using magnetoencephalography. The level of unpleasantness of menopausal symptoms was measured using the Kupperman Kohnenki Shogai Index. The blood level of follicle-stimulating hormone and luteinizing hormone were also measured. Correlation analyses were performed between the oscillatory power of brain activity, index score, and hormone levels. RESULTS: The level of unpleasantness of menopausal symptoms was positively correlated with high-frequency oscillatory powers in the parietal and bordering cortices (alpha; P = 0.016, beta; P = 0.015, low gamma; P = 0.010). The follicle-stimulating hormone blood level was correlated with high-frequency oscillatory powers in the dorsal part of the cortex (beta; P = 0.008, beta; P = 0.005, low gamma; P = 0.017), whereas luteinizing hormone blood level was not correlated. CONCLUSION: Resting-state brain activity can serve as an objective measurement of unpleasantness associated with menopausal symptoms, which aids the selection of appropriate treatment and monitors its outcome.

Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment.

Background: Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records. Methods: Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved. Results: MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse. Conclusion: MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.

Resting-state brain activity distinguishes patients with generalised epilepsy from others.

PURPOSE: Epilepsy is a prevalent neurological disorder characterised by repetitive seizures. It is categorised into three types: generalised epilepsy (GE), focal epilepsy (FE), and combined generalised and focal epilepsy. Correctly subtyping the epilepsy is important to select appropriate treatments. The types are mainly determined (i.e., diagnosed) by their semiologies supported by clinical examinations, such as electroencephalography and magnetoencephalography (MEG). Although these examinations are traditionally based on visual inspections of interictal epileptic discharges (IEDs), which are not always visible, alternative analyses have been anticipated. We examined if resting-state brain activities can distinguish patients with GE, which would help us to diagnose the type of epilepsy. METHODS: The 5 min resting-state brain activities acquired using MEG were obtained retrospectively from 15 patients with GE. The cortical source of the activities was estimated at each frequency band from delta to high-frequency oscillation (HFO). These estimated activities were compared with reference datasets from 133 healthy individuals and control data from 29 patients with FE. RESULTS: Patients with GE showed larger theta in the occipital, alpha in the left temporal, HFO in the rostral deep regions, and smaller HFO in the caudal ventral regions. Their area under the curves of the receiver operating characteristic curves was around 0.8-0.9. The distinctive pattern was not found for data from FE. CONCLUSION: Patients with GE show distinctive resting-state brain activity, which could be a potential biomarker and used complementarily to classical analysis based on the visual inspection of IEDs.

Monitoring the outcomes of non-pharmacological treatments for cognitive impairment using magnetoencephalography: A case series.

Key Clinical Message: Cognitive impairment associated dementia is treatable non-pharmacologically. Monitoring tools are important to provide proper treatment. The present study showed that the resting-state brain activity measured using magnetoencephalography reflects their outcomes and captures clinical impressions better than neuropsychological assessments, which have inherent limitations such as the practice effect. Abstract: Mild cognitive impairment (MCI) is a prodromal phase of dementia caused by brain diseases. Non-pharmacological treatments are sometimes effective in improving patient's cognition and quality of life. To provide better treatments, monitoring the treatment outcomes, which is done using neuropsychological assessments, is important. However, these assessments have inherent limitations, such as practice effects. Therefore, complementary assessments are anticipated. Magnetoencephalography (MEG) is a neuroimaging technique that is sensitive to changes in brain activity associated with cognitive impairment. It represents the state of brain activity in terms of MEG spectral parameters associated with neuropsychological assessment scores. MEG spectral parameters could reasonably be used to monitor treatment outcomes without the aforementioned limitations. However, few published longitudinal reports have assessed MEG spectral parameters during the non-pharmacological treatment period for cognitive impairment associated with dementia. In this study, we retrospectively examined the clinical records of two patients with MCI. Changes in neuropsychological assessment scores and MEG spectral parameters were qualitatively evaluated along with the patients' conditions, as described in the medical records during non-pharmacological treatments provided for more than 2 years. The changes in neuropsychological assessment scores and MEG spectral parameters showed comparable trends, with some discrepancies. Changes in MEG spectral parameters were more consistent with the subjective reports from caregivers and medical staff in the medical records. Our results suggest that MEG is a promising tool for monitoring patient conditions during treatment.

Reliability of Spectral Features of Resting-State Brain Activity: A Magnetoencephalography Study.

Background Cognition is a vital sign and its deterioration is a major concern in clinical medicine. It is usually evaluated using neuropsychological assessments, which have innate limitations such as the practice effect. To compensate for these assessments, the oscillatory power of resting-state brain activity has recently become available. The power is obtained noninvasively using magnetoencephalography and is summarized by spectral parameters such as the median frequency (MF), individual alpha frequency (IAF), spectral edge frequency 95 (SEF95), and Shannon's spectral entropy (SSE). As these parameters are less sensitive to practice effects, they are suitable for longitudinal studies. However, their reliability remains unestablished, hindering their proactive use in clinical practice. Therefore, we aimed to quantify the within-participant reliability of these parameters using repeated measurements of healthy participants to facilitate their clinical use and to evaluate the observed changes/differences in these parameters reported in previous studies. Methodology Resting-state brain activity with eyes closed was recorded using magnetoencephalography for five minutes from 15 healthy individuals (29.3 ± 4.6 years old: ranging from 23 to 28 years old). The following four spectral parameters were calculated: MF, IAF, SEF95, and SSE. To quantify reliability, the minimal detectable change (MDC) and intraclass correlation coefficient (ICC) were computed for each parameter. In addition, we used MDCs to evaluate the changes and differences in the spectral parameters reported in previous longitudinal and cross-sectional studies. Results The MDC at 95% confidence interval (MDC95) of MF, IAF, SEF95, and SSE were 0.61 Hz, 0.44 Hz, 2.91 Hz, and 0.028, respectively. The ICCs of these parameters were 0.96, 0.92, 0.94, and 0.83, respectively. The MDC95 of these parameters was smaller than the mean difference in the parameters between cognitively healthy individuals and patients with dementia, as reported in previous studies. Conclusions The spectral parameter changes/differences observed in prior studies were not attributed to measurement errors but rather reflected genuine effects. Furthermore, all spectral parameters exhibited high ICCs (>0.8), underscoring their robust within-participant reliability. Our results support the clinical use of these parameters, especially in the longitudinal monitoring and evaluation of the outcomes of interventions.

Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS: To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS: Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION: This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.

Parallelism in the brain's visual form system.

We used magnetoencephalography (MEG) to determine whether increasingly complex forms constituted from the same elements (lines) activate visual cortex with the same or different latencies. Twenty right-handed healthy adult volunteers viewed two different forms, lines and rhomboids, representing two levels of complexity. Our results showed that the earliest responses produced by lines and rhomboids in both striate and prestriate cortex had similar peak latencies (40 ms) although lines produced stronger responses than rhomboids. Dynamic causal modeling (DCM) showed that a parallel multiple input model to striate and prestriate cortex accounts best for the MEG response data. These results lead us to conclude that the perceptual hierarchy between lines and rhomboids is not mirrored by a temporal hierarchy in latency of activation and thus that a strategy of parallel processing appears to be used to construct forms, without implying that a hierarchical strategy may not be used in separate visual areas, in parallel.

Two types of mental fatigue affect spontaneous oscillatory brain activities in different ways.

BACKGROUND: Fatigue has a multi-factorial nature. We examined the effects of two types of mental fatigue on spontaneous oscillatory brain activity using magnetoencephalography (MEG). METHODS: Participants were randomly assigned to two groups in a single-blinded, crossover fashion to perform two types of mental fatigue-inducing experiments. Each experiment consisted of a 30-min fatigue-inducing 0- or 2-back test session and two evaluation sessions performed just before and after the fatigue-inducing mental task session. RESULTS: After the 0-back test, decreased alpha power was indicated in the right angular gyrus and increased levels in the left middle and superior temporal gyrus, left postcentral gyrus, right superior frontal gyrus, left inferior frontal gyrus, and right medial frontal gyrus. After the 2-back test, decreased alpha power was indicated in the right middle and superior frontal gyrus and increased levels in the left inferior parietal and superior parietal lobules, right parahippocampal gyrus, right uncus, left postcentral gyrus, left middle frontal gyrus, and right inferior frontal gyrus. For beta power, increased power following the 0-back test was indicated in the left middle temporal gyrus, left superior frontal gyrus, left cingulate gyrus, and left precentral gyrus. After the 2-back test, decreased power was suggested in the left superior frontal gyrus and increased levels in the left middle temporal gyrus and left inferior parietal lobule. Some of these brain regions might be associated with task performance during the fatigue-inducing trials. CONCLUSIONS: Two types of mental fatigue may produce different alterations of the spontaneous oscillatory MEG activities. Our findings would provide new perspectives on the neural mechanisms underlying mental fatigue.

Quantification of the robustness of functional neural networks: application to the characterization of Alzheimer's disease continuum.

Objective.Brain connectivity networks are usually characterized in terms of properties coming from the complex network theory. Using new measures to summarize the attributes of functional connectivity networks can be an important step for their better understanding and characterization, as well as to comprehend the alterations associated with neuropsychiatric and neurodegenerative disorders. In this context, the main objective of this study was to introduce a novel methodology to evaluate network robustness, which was subsequently applied to characterize the brain activity in the Alzheimer's disease (AD) continuum.Approach.Functional connectivity networks were built using 478 electroencephalographic and magnetoencephalographic resting-state recordings from three different databases. These functional connectivity networks computed in the conventional frequency bands were modified simulating an iterative attack procedure using six different strategies. The network changes caused by these attacks were evaluated by means of Spearman's correlation. The obtained results at the conventional frequency bands were aggregated in a correlation surface, which was characterized in terms of four gradient distribution properties: mean, variance, skewness, and kurtosis.Main results.The new proposed methodology was able to consistently quantify network robustness. Our results showed statistically significant differences in the inherent ability of the network to deal with attacks (i.e. differences in network robustness) between controls, mild cognitive impairment subjects, and AD patients for the three different databases. In addition, we found a significant correlation between mini-mental state examination scores and the changes in network robustness.Significance.To the best of our knowledge, this is the first study which assesses the robustness of the functional connectivity network in the AD continuum. Our findings consistently evidence the loss of network robustness as the AD progresses for the three databases. Furthermore, the changes in this complex network property may be related with the progressive deterioration in brain functioning due to AD.

Effect of mental fatigue on the central nervous system: an electroencephalography study.

BACKGROUND: Fatigue can be classified as mental and physical depending on its cause, and each type of fatigue has a multi-factorial nature. We examined the effect of mental fatigue on the central nervous system using electroencephalography (EEG) in eighteen healthy male volunteers. METHODS: After enrollment, subjects were randomly assigned to two groups in a single-blinded, crossover fashion to perform two types of mental fatigue-inducing experiments. Each experiment consisted of four 30-min fatigue-inducing 0- or 2-back test sessions and two evaluation sessions performed just before and after the fatigue-inducing sessions. During the evaluation session, the participants were assessed using EEG. Eleven electrodes were attached to the head skin, from positions F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2. RESULTS: In the 2-back test, the beta power density on the Pz electrode and the alpha power densities on the P3 and O2 electrodes were decreased, and the theta power density on the Cz electrode was increased after the fatigue-inducing mental task sessions. In the 0-back test, no electrodes were altered after the fatigue-inducing sessions. CONCLUSIONS: Different types of mental fatigue produced different kinds of alterations of the spontaneous EEG variables. Our findings provide new perspectives on the neural mechanisms underlying mental fatigue.

Two Distinct Neural Mechanisms Underlying Acupuncture Analgesia.

Acupuncture analgesia is a traditional treatment with a long history, although it lacks scientific evidence. It is reportedly associated with the central nervous system, including various brain regions, from the cortices to the brain stem. However, it remains unclear whether the distributed regions behave as a single unit or consist of multiple sub-units playing different roles. Magnetoencephalography is a neuroimaging technique that can measure the oscillatory frequency of neural signals and brain regions. The frequency band of neural signals allows further understanding of the characteristics of the acupuncture-related neural systems. This study measured resting-state brain activity using magnetoencephalography in 21 individuals with chronic pain before and after acupuncture treatment. The subjective level of pain was assessed using a visual analog scale, and brain activity was compared to identify the brain regions and the frequencies associated with acupuncture analgesia. Here, we categorized the changes in resting-state brain activity into two groups: low-frequency oscillatory activity (<3 Hz) in the left middle occipital and right superior partial lobule and high-frequency oscillatory activity (81-120 Hz) on both sides of the prefrontal, primary sensory, and right fusiform gyri. These findings suggest that acupuncture analgesia influences two or more sub-units of the neural systems, which helps us understand the neural mechanisms underlying acupuncture analgesia.

Distinctive effects of executive dysfunction and loss of learning/memory abilities on resting-state brain activity.

Dementia is a syndrome characterised by cognitive impairments, with a loss of learning/memory abilities at the earlier stages and executive dysfunction at the later stages. However, recent studies have suggested that impairments in both learning/memory abilities and executive functioning might co-exist. Cognitive impairments have been primarily evaluated using neuropsychological assessments, such as the Mini-Mental State Examination (MMSE). Recently, neuroimaging techniques such as magnetoencephalography (MEG), which assess changes in resting-state brain activity, have also been used as biomarkers for cognitive impairment. However, it is unclear whether these changes reflect dysfunction in executive function as well as learning and memory. In this study, parameters from the MEG for brain activity, MMSE for learning/memory, and Frontal Assessment Battery (FAB) for executive function were compared within 207 individuals. Three MEG parameters were used as representatives of resting-state brain activity: median frequency, individual alpha frequency, and Shannon's spectral entropy. Regression analysis showed that median frequency was predicted by both the MMSE and FAB scores, while individual alpha frequency and Shannon's spectral entropy were predicted by MMSE and FAB scores, respectively. Our results indicate that MEG spectral parameters reflect both learning/memory and executive functions, supporting the utility of MEG as a biomarker of cognitive impairment.

Low visual information-processing speed and attention are predictors of fatigue in elementary and junior high school students.

BACKGROUND: Fatigue is a common complaint among elementary and junior high school students, and is known to be associated with reduced academic performance. Recently, we demonstrated that fatigue was correlated with decreased cognitive function in these students. However, no studies have identified cognitive predictors of fatigue. Therefore, we attempted to determine independent cognitive predictors of fatigue in these students. METHODS: We performed a prospective cohort study. One hundred and forty-two elementary and junior high school students without fatigue participated. They completed a variety of paper-and-pencil tests, including list learning and list recall tests, kana pick-out test, semantic fluency test, figure copying test, digit span forward test, and symbol digit modalities test. The participants also completed computerized cognitive tests (tasks A to E on the modified advanced trail making test). These cognitive tests were used to evaluate motor- and information-processing speed, immediate and delayed memory function, auditory and visual attention, divided and switching attention, retrieval of learned material, and spatial construction. One year after the tests, a questionnaire about fatigue (Japanese version of the Chalder Fatigue Scale) was administered to all the participants. RESULTS: After the follow-up period, we confirmed 40 cases of fatigue among 118 students. In multivariate logistic regression analyses adjusted for grades and gender, poorer performance on visual information-processing speed and attention tasks was associated with increased risk of fatigue. CONCLUSIONS: Reduced visual information-processing speed and poor attention are independent predictors of fatigue in elementary and junior high school students.

Exploring the Interactions Between Neurophysiology and Cognitive and Behavioral Changes Induced by a Non-pharmacological Treatment: A Network Approach.

Dementia due to Alzheimer's disease (AD) is a neurological syndrome which has an increasing impact on society, provoking behavioral, cognitive, and functional impairments. AD lacks an effective pharmacological intervention; thereby, non-pharmacological treatments (NPTs) play an important role, as they have been proven to ameliorate AD symptoms. Nevertheless, results associated with NPTs are patient-dependent, and new tools are needed to predict their outcome and to improve their effectiveness. In the present study, 19 patients with AD underwent an NPT for 83.1 ± 38.9 days (mean ± standard deviation). The NPT was a personalized intervention with physical, cognitive, and memory stimulation. The magnetoencephalographic activity was recorded at the beginning and at the end of the NPT to evaluate the neurophysiological state of each patient. Additionally, the cognitive (assessed by means of the Mini-Mental State Examination, MMSE) and behavioral (assessed in terms of the Dementia Behavior Disturbance Scale, DBD-13) status were collected before and after the NPT. We analyzed the interactions between cognitive, behavioral, and neurophysiological data by generating diverse association networks, able to intuitively characterize the relationships between variables of a different nature. Our results suggest that the NPT remarkably changed the structure of the association network, reinforcing the interactions between the DBD-13 and the neurophysiological parameters. We also found that the changes in cognition and behavior are related to the changes in spectral-based neurophysiological parameters. Furthermore, our results support the idea that MEG-derived parameters can predict NPT outcome; specifically, a lesser degree of AD neurophysiological alterations (i.e., neural oscillatory slowing, decreased variety of spectral components, and increased neural signal regularity) predicts a better NPT prognosis. This study provides deeper insights into the relationships between neurophysiology and both, cognitive and behavioral status, proving the potential of network-based methodology as a tool to further understand the complex interactions elicited by NPTs.

Effect of segment length, sampling frequency, and imaging modality on the estimation of measures of brain meta-state activation: an MEG/EEG study.

The main objective of this study was to examine the influence that recording length, sampling frequency, and imaging modality have on the estimation and characterization of spontaneous brain meta-states during rest. To this end, a recently developed method of meta-state extraction and characterization was applied to a subset of 16 healthy elderly subjects from two independent electroencephalographic and magnetoencephalographic (EEG/MEG) databases. The recordings were segmented into the first 5, 10, 15, 20, 25, 30, 60 and 90-s of artifact-free activity and meta-states were extracted. Temporal activation sequence (TAS) complexity, which characterizes the complexity of the metastateactivation sequences during rest, was calculated. Then, its stability as a function of segment length, sampling frequency, and imaging modality was assessed. The results showed that, in general, the minimum segment length needed to fully characterize resting-state meta-state activation complexity ranged from 15 to 25 seconds. Moreover, it was found that the sampling frequency has a slight influence on the complexity measure, and that results were similar across EEG and MEG. The findings indicate that the proposed methodology can be applied to both EEG and MEG recordings and displays stable behavior with relatively short segments. However, methodological choices, such as sampling frequency, should be carefully considered.