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Adequate fluid intake is essential for maintaining cognitive health in older adults. However, two key questions remain unanswered before recommending increased fluid consumption: (1) whether the relationship between fluid intake and cognitive improvement is linear and (2) the underlying mechanisms that mediate this association. Thirty-three older adults residing in a geriatric health service facility and receiving nursing care were enrolled in this study. Fluid intake was recorded as part of routine clinical practice. Cognitive function was assessed twice during their stay using the Japanese version of the Mini-Mental State Examination (MMSE-J). Additionally, cerebral blood flow was evaluated bilaterally in the common carotid arteries using ultrasonography, with assessments conducted approximately 82.6 ± 14.9 days apart. Relationships among fluid intake per lean body mass (LBM), changes in MMSE-J scores, and ultrasonographic parameters were analysed using Spearman's linear correlation analysis with non-parametric bootstrapping. Correlation analyses revealed a positive linear association between fluid intake and improvement in MMSE-J scores [P(FDR) = 0.012] when the intake was less than 42 mL/LBM (kg) per day. Furthermore, fluid intake was inversely correlated with the resistance index in the right common carotid artery [P(FDR) = 0.046], indicating altered cerebral blood dynamics. The main limitations of our study include (1) the inability to evaluate baseline hydration status or fluid intake prior to facility admission due to clinical constraints and (2) the observational design precluding causal inference between fluid intake, cognitive changes, and cerebral blood flow parameters. Within moderate intake ranges, fluid consumption was linearly associated with cognitive improvement, an effect that appears to be mediated by changes in cerebral haemodynamic.

Non-pharmacological treatment (NPT) improves cognitive functions and behavioural disturbances in patients with dementia, but the underlying neural mechanisms are unclear. In this observational study, 21 patients with dementia received NPTs for several months. Patients were scanned using magnetoencephalography twice during the NPT period to evaluate NPT effects on resting-state brain activity. Additionally, cognitive functions and behavioural disturbances were measured using the Mini-Mental State Examination (MMSE-J) and a short version of the Dementia Behaviour Disturbance Scale (DBD-13) at the beginning and the end of the NPT period. In contrast to the average DBD-13 score, the average MMSE-J score improved after the NPT period. Magnetoencephalography data revealed a reduced alpha activity in the right temporal lobe and fusiform gyrus, as well as an increased low-gamma activity in the right angular gyrus. DBD-13 score changes were correlated with beta activity in the sensorimotor area. These findings corroborate previous studies confirming NPT effects on brain activity in healthy participants and people at risk of dementia. Our results provide additional evidence that brains of patients with dementia have the capacity for plasticity, which may be responsible for the observed NPT effects. In dementia, NPT might lead to improvements in the quality of life.

A healthy lifestyle, including regular physical activity, prevents cognitive decline and dementia. Evaluating the influence of regular physical activity on the brain is essential for properly assessing patients' conditions and designing effective therapeutic strategies. We aimed to investigate whether and how electrophysiological brain activity reflects the influence of regular physical activity. Clinical records from 327 patients who visited our outpatient department for dementia were analysed retrospectively. Patients were classified into two groups: 'Active' for those who engaged in regular physical activity and 'Nonactive' for patients who did not. Electrophysiological brain activity was recorded using magnetoencephalography and quantitatively evaluated using three spectral parameters: median frequency, individual alpha frequency, and Shannon's spectral entropy. Cognitive state was assessed using three neuropsychological assessments: the Japanese version of Mini-Mental State Examination (MMSE-J), Frontal Assessment Battery (FAB-J), and Alzheimer's Disease Assessment Scale-Cognitive section (ADAS-J cog). The effects of group ('Active' or 'Nonactive') on the spectral parameters were examined using an analysis of covariance with one of the neuropsychological assessments as a covariate. The size of contribution was quantified in the unit of neuropsychological assessments using a regression model. A main effect of group was observed for all three spectral parameters. The size of contribution was equivalent to approximate changes of 3-11 points in MMSE-J, 3-7 points in FAB-J, and 10-14 points in ADAS-J cog scores. The main limitations of our study are: (1) this study was conducted in a single site; (2) possibility of reverse causality; and (3) some potential confounding factors, such as genetic factors, were not considered. Electrophysiological brain activity reflects the influence of regular physical activity as well as current cognitive states. Such insights are valuable for physicians to design effective therapeutic strategies and provide clinical advice to patients with cognitive impairment and dementia.

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.

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.

Cerebral hypoperfusion impairs brain activity and leads to cognitive impairment. Left and right common carotid arteries (CCA) are the major source of cerebral blood supply. It remains unclear whether blood flow in both CCA contributes equally to brain activity. Here, CCA blood flow was evaluated using ultrasonography in 23 patients with cerebrovascular diseases. Resting-state brain activity and cognitive status were also assessed using magnetoencephalography and a cognitive subscale of the Functional Independence Measure, respectively, to explore the relationships between blood flow, functional brain activity, and cognitive status. Our findings indicated that there was an association between blood flow and resting-state brain activity, and between resting-state brain activity and cognitive status. However, blood flow was not significantly associated with cognitive status directly. Furthermore, blood velocity in the right CCA correlated with resting-state brain activity, but not with the resistance index. In contrast, the resistance index in the left CCA correlated with resting-state brain activity, but not with blood velocity. Our findings suggest that hypoperfusion is important in the right CCA, whereas cerebral microcirculation is important in the left CCA for brain activity. Hence, this asymmetry should be considered when designing appropriate therapeutic strategies.

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. 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-' ', ' ', and ' '-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved. 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. 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.

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the automatic detection of spikes using deep learning in single center. In this study, we performed a multi-center study using six MEG centers to improve the performance of the automated detection of neuromagnetically recorded epileptic spikes, which we previously developed using deep learning. Data from four centers were used for training and evaluation (internal data), and the remaining two centers were used for evaluation only (external data). We used a five-fold subject-wise cross-validation technique to train and evaluate the models. A comparison showed that the multi-center model outperformed the single-center model in terms of performance. The multi-center model achieved an average ROC-AUC of 0.9929 and 0.9426 for the internal and external data, respectively. The median distance between the neurophysiologist-analyzed and automatically analyzed dipoles was 4.36 and 7.23 mm for the multi-center model for internal and external data, respectively, indicating accurate detection of epileptic spikes. By training data from multiple centers, automated analysis can improve spike detection and reduce the analysis workload for neurophysiologists. This study suggests that the multi-center model has the potential to detect spikes within 1 cm of a neurophysiologist’s analysis. This multi-center model can significantly reduce the number of hours required by neurophysiologists to detect spikes.

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. MEG and pre-rating data were collected from 23 participants. The pre-rating included visual analogue scales for , , , and scores, in addition to ' ,' 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. 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. 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.

The characterization of dynamic functional connectivity (dFC) patterns, known as meta-states, which activate in the brain during rest provides valuable insights into the underlying organization of time-varying neural activity. High temporal resolution neurophysiological techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), enable the analysis of fast-evolving meta-state activation sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer’s disease (AD) have been shown to disrupt dFC between different brain regions; however, their impact on the temporal organization of meta-state activation sequences remains unclear. The aim of this study was to confirm the existence of recurrent brain meta-state activations during the resting-state and to investigate how this recurrent structure is affected by MCI and AD. To fulfill this goal, the autocorrelation of the meta-state temporal activation sequence was computed from 60-second M/EEG resting-state signals; afterwards, different metrics were calculated to quantify its properties. Three databases were considered in the analysis to evaluate the generalizability of the findings across different acquisition sites and neurophysiological modalities: EEG1 (38 controls, 64 MCI, and 74 AD), EEG2 (38 controls, 38 MCI, and 70 AD), and MEG (52 controls, 32 MCI, and 52 AD). The results provide evidence of an underlying cyclical structure in meta-state activation sequences, which becomes progressively disrupted in MCI and AD, leading to a decrease in recurrence and an increase in randomness. These findings suggest that the loss of temporal organization in meta-state dynamics may be indicative of neurodegenerative conditions. •Temporal meta-states sequencing reveals a recurrent structure during resting-state.•The cyclical organization of meta-state sequencing is disrupted by MCI and AD.•Alterations in the alpha band are consistent across EEG and MEG datasets.