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Abstract Background Alzheimer’s Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies. Methods Our study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI. Results The results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions’ activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions. Conclusions Our findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Abstract Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN’s alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Abstract Background The earliest pathological features of Alzheimer’s disease (AD) appear decades before the clinical symptoms. The pathology affects the brain and the eye, leading to retinal structural changes and functional visual alterations. Healthy individuals at high risk of developing AD present alterations in these ophthalmological measures, as well as in resting-state electrophysiological activity. However, it is unknown whether the ophthalmological alterations are related to the visual-related electrophysiological activity. Elucidating this relationship is paramount to understand the mechanisms underlying the early deterioration of the system and an important step in assessing the suitability of these measures as early biomarkers of disease. Methods In total, 144 healthy subjects: 105 with family history of AD and 39 without, underwent ophthalmologic analysis, magnetoencephalography recording, and genotyping. A subdivision was made to compare groups with less demographic and more risk differences: 28 high-risk subjects (relatives/APOEɛ4 +) and 16 low-risk (non-relatives/APOEɛ4 −). Differences in visual acuity, contrast sensitivity, and macular thickness were evaluated. Correlations between each variable and visual-related electrophysiological measures (M100 latency and time–frequency power) were calculated for each group. Results High-risk groups showed increased visual acuity. Visual acuity was also related to a lower M100 latency and a greater power time–frequency cluster in the high-risk group. Low-risk groups did not show this relationship. High-risk groups presented trends towards a greater contrast sensitivity that did not remain significant after correction for multiple comparisons. The highest-risk group showed trends towards the thinning of the inner plexiform and inner nuclear layers that did not remain significant after correction. The correlation between contrast sensitivity and macular thickness, and the electrophysiological measures were not significant after correction. The difference between the high- and low- risk groups correlations was no significant. Conclusions To our knowledge, this paper is the first of its kind, assessing the relationship between ophthalmological and electrophysiological measures in healthy subjects at distinct levels of risk of AD. The results are novel and unexpected, showing an increase in visual acuity among high-risk subjects, who also exhibit a relationship between this measure and visual-related electrophysiological activity. These results have not been previously explored and could constitute a useful object of research as biomarkers for early detection and the evaluation of potential interventions’ effectiveness.

The alpha rhythm is often associated with relaxed wakefulness or idling and is altered by various factors. Abnormalities in the alpha rhythm have been linked to several neurological and psychiatric disorders, including Alzheimer's disease. Transcranial alternating current stimulation (tACS) has been proposed as a potential tool to restore a disrupted alpha rhythm in the brain by stimulating at the individual alpha frequency (IAF), although some research has produced contradictory results. In this study, we applied an IAF-tACS protocol over parieto-occipital areas to a sample of healthy subjects and measured its effects over the power spectra. Additionally, we used computational models to get a deeper understanding of the results observed in the experiment. Both experimental and numerical results showed an increase in alpha power of 8.02% with respect to the sham condition in a widespread set of regions in the cortex, excluding some expected parietal regions. This result could be partially explained by taking into account the orientation of the electric field with respect to the columnar structures of the cortex, showing that the gyrification in parietal regions could generate effects in opposite directions (hyper-/depolarization) at the same time in specific brain regions. Additionally, we used a network model of spiking neuronal populations to explore the effects that these opposite polarities could have on neural activity, and we found that the best predictor of alpha power was the average of the normal components of the electric field. To sum up, our study sheds light on the mechanisms underlying tACS brain activity modulation, using both empirical and computational approaches. Non-invasive brain stimulation techniques hold promise for treating brain disorders, but further research is needed to fully understand and control their effects on brain dynamics and cognition. Our findings contribute to this growing body of research and provide a foundation for future studies aimed at optimizing the use of non-invasive brain stimulation in clinical settings.

Background: This study aimed to explore the association between a verbal learning task that evaluates the potential mutual dependency between memory and executive functions (i.e., the Test of Memory Strategies, TMS) and cerebrospinal fluid (CSF) Alzheimer’s Disease (AD) biomarkers. Methods: A sample of 47 mild cognitive impairment (MCI) participants from Poland and Spain were classified according to the Erlangen Score Diagnostic Algorithm (ESA) into CSF- (n = 16) and CSF+ (n = 31) groups. Correlation analyses between TMS word-list conditions and CSF biomarkers were conducted. Additionally, an analysis of covariance was performed to define the effect on ESA classification in the sample, using as a covariable the country of origin of the participants. Results: Significant associations between the TMS-3 condition and Aβ42, t-tau, and p-tau were observed for the whole sample. In addition, the CSF- participants obtained higher cognitive performance in TMS-3 compared to the CSF+ group. This outcome persisted if the groups were based on Aβ42 scores, but not t-tau or p-tau values. Conclusions: These findings could indicate that poor performance on verbal learning tests may be affected by executive dysfunctions. Therefore, future intervention plans focused on training executive functions would be of interest to improve the ability of MCI patients to encode and organize information.

Background: Mineral intake may protect against cognitive impairment (CI) and all-cause dementia, which affects a large number of adults worldwide. The aim of this study was to investigate the association between mineral intake and Montreal Cognitive Assessment (MoCA), which is a sensitive and specific test. Methods: In total, 201 adults were included in a cross-sectional study. They completed a three-day dietary record to estimate their average daily intake of minerals. Contributions to dietary reference intakes (DRIs) were also calculated. The participants were divided into tertiles according to their mineral intake. CI classifications were determined via the MoCA (score < 26). Apolipoprotein E (APOE) genotyping was carried out, and the patients’ anthropometric measurements and physical activity, health and personal data were collected. Results: The prevalence of CI in this selective sample was 54.2% (34.3% females and 19.9% males). In women, being in the third tertiles of iron and manganese intake was associated with lower odds of having CI (OR [95% CI]: 0.32 [0.11 ± 0.93]; 0.33 [0.12 ± 0.93], p < 0.05). No significant differences were observed for any of the nutrients studied in men. Conclusions: These findings suggest that a low mineral intake, especially low iron and manganese intake in women, is associated with a worse cognition as assessed by MoCA.

IntroductionHeavy drinking (HD) prevalent pattern of alcohol consumption among adolescents, particularly concerning because of their critical vulnerability to the neurotoxic effects of ethanol. Adolescent neurodevelopment is characterized by critical neurobiological changes of the prefrontal, temporal and parietal regions, important for the development of executive control processes, such as inhibitory control (IC). In the present Magnetoencephalography (MEG) study, we aimed to describe the relationship between electrophysiological Functional Connectivity (FC) during an IC task and HD development, as well as its impact on functional neuromaturation.MethodsWe performed a two-year longitudinal protocol with two stages. In the first stage, before the onset of HD, we recorded brain electrophysiological activity from a sample of 67 adolescents (mean age = 14.6 ± 0.7) during an IC task. Alcohol consumption was measured using the AUDIT test and a semi-structured interview. Two years later, in the second stage, 32 of the 67 participants (mean age 16.7 ± 0.7) completed a similar protocol. As for the analysis in the first stage, the source-space FC matrix was calculated, and then, using a cluster-based permutation test (CBPT) based on Spearman’s correlation, we calculated the correlation between the FC of each cortical source and the number of standard alcohol units consumed two years later. For the analysis of longitudinal change, we followed a similar approach. We calculated the symmetrized percentage change (SPC) between FC at both stages and performed a CBPT analysis, analyzing the correlation between FC change and the level of alcohol consumed in a regular session.ResultsThe results revealed an association between higher beta-band FC in the prefrontal and temporal regions and higher consumption years later. Longitudinal results showed that greater future alcohol consumption was associated with an exacerbated reduction in the FC of the same areas.DiscussionThese results underline the existence of several brain functional differences prior to alcohol misuse and their impact on functional neuromaturation.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth's surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and is associated with a variety of structural and psychological alterations. Recently, there has been renewed interest in using brain tissue resected during epilepsy surgery, in particular ‘non-epileptic’ brain samples with normal histology that can be found alongside epileptic tissue in the same epileptic patients — with the aim being to study the normal human brain organization using a variety of methods. An important limitation is that different medical characteristics of the patients may modify the brain tissue. Thus, to better determine how ‘normal’ the resected tissue is, it is fundamental to know certain clinical, anatomical and psychological characteristics of the patients. Unfortunately, this information is frequently not fully available for the patient from which the resected tissue has been obtained — or is not fully appreciated by the neuroscientists analyzing the brain samples, who are not necessarily experts in epilepsy. In order to present the full picture of TLE in a way that would be accessible to multiple communities (e.g., basic researchers in neuroscience, neurologists, neurosurgeons and psychologists), we have reviewed 34 TLE patients, who were selected due to the availability of detailed clinical, anatomical, and psychological information for each of the patients. Our aim was to convey the full complexity of the disorder, its putative anatomical substrates, and the wide range of individual variability, with a view toward: (1) emphasizing the importance of considering critical patient information when using brain samples for basic research and (2) gaining a better understanding of normal and abnormal brain functioning. In agreement with a large number of previous reports, this study (1) reinforces the notion of substantial individual variability among epileptic patients, and (2) highlights the common but overlooked psychopathological alterations that occur even in patients who become “seizure-free” after surgery. The first point is based on pre- and post-surgical comparisons of patients with hippocampal sclerosis and patients with normal-looking hippocampus in neuropsychological evaluations. The second emerges from our extensive battery of personality and projective tests, in a two-way comparison of these two types of patients with regard to pre- and post-surgical performance.

BackgroundDown syndrome (DS) is considered the most frequent cause of early-onset Alzheimer’s disease (AD), and the typical pathophysiological signs are present in almost all individuals with DS by the age of 40. Despite of this evidence, the investigation on the pre-dementia stages in DS is scarce. In the present study we analyzed the complexity of brain oscillatory patterns and neuropsychological performance for the characterization of mild cognitive impairment (MCI) in DS.Materials and methodsLempel-Ziv complexity (LZC) values from resting-state magnetoencephalography recordings and the neuropsychological performance in 28 patients with DS [control DS group (CN-DS) (n = 14), MCI group (MCI-DS) (n = 14)] and 14 individuals with typical neurodevelopment (CN-no-DS) were analyzed.ResultsLempel-Ziv complexity was lowest in the frontal region within the MCI-DS group, while the CN-DS group showed reduced values in parietal areas when compared with the CN-no-DS group. Also, the CN-no-DS group exhibited the expected pattern of significant increase of LZC as a function of age, while MCI-DS cases showed a decrease. The combination of reduced LZC values and a divergent trajectory of complexity evolution with age, allowed the discrimination of CN-DS vs. MCI-DS patients with a 92.9% of sensitivity and 85.7% of specificity. Finally, a pattern of mnestic and praxic impairment was significantly associated in MCI-DS cases with the significant reduction of LZC values in frontal and parietal regions (p = 0.01).ConclusionBrain signal complexity measured with LZC is reduced in DS and its development with age is also disrupted. The combination of both features might assist in the detection of MCI within this population.

Background Previous literature has shown that executive functions (EF) are related to performance in memory (M) tasks. Nevertheless, there is a shortage of psychometric tests that examine these two constructs simultaneously. The Test of Memory Strategies (TMS; previously validated in Spain and Portugal) could be a useful verbal learning task that evaluates these two constructs at once. In this study, we aimed to evaluate the psychometric properties of the TMS in an Italian adult sample. Method One hundred twenty-one healthy volunteers (74 F, Mean age = 45.9 years old, SD = 20.4) who underwent a neuropsychological examination participated in this study. We conducted a Confirmatory factor analysis (CFA) to evaluate the structural validity of the TMS. We conducted a latent variable analysis to examine convergent and discriminant validity of the TMS sub-scale scores reflecting executive functions and memory. We also examined the TMS reliability in terms of internal consistency through the McDonald’s omega. Results The CFA confirmed the expectation that the TMS-1 and TMS-2 subtests reflect a factor and that the TMS-3, TMS-4, and TMS-5 subtests reflect a different factor. This result is in line with the prediction that TMS-1 and TMS-2 require the use of executive functions and memory simultaneously, and therefore we called this factor executive functions (EF); whereas the TMS-3, TMS-4, and TMS-5 subtests require less involvement of executive functions, thus reflecting a construct that we named memory (M). The TMS subtests for EF and M showed convergent validity with the test scores using a traditional neuropsychological battery, assessing memory and executive functions separately. Finally, the reliability of the subtests was good. Conclusions These preliminary findings suggest that TMS is a valid and reliable scale to simultaneously assess M and EF while among Italian healthy adults.

This study aimed to analyze the evolution of visual changes in cognitively healthy individuals at risk for Alzheimer’s disease (AD). Participants with a first-degree family history of AD (FH+) and carrying the Ε4+ allele for the ApoE gene (ApoE ε4+) underwent retinal thickness analysis using optical coherence tomography (OCT) and visual function assessments, including visual acuity (VA), contrast sensitivity (CS), color perception, perception digital tests, and visual field analysis. Structural analysis divided participants into FH+ ApoE ε4+ and FH− ApoE ε4− groups, while functional analysis further categorized them by age (40–60 years and over 60 years). Over the 27-month follow-up, the FH+ ApoE ε4+ group exhibited thickness changes in all inner retinal layers. Comparing this group to the FH− ApoE ε4− group at 27 months revealed progressing changes in the inner nuclear layer. In the FH+ ApoE ε4+ 40–60 years group, no progression of visual function changes was observed, but an increase in VA and CS was maintained at 3 and 12 cycles per degree, respectively, compared to the group without AD risk at 27 months. In conclusion, cognitively healthy individuals at risk for AD demonstrated progressive retinal structural changes over the 27-month follow-up, while functional changes remained stable.

Abstract Neural modelling tools are increasingly employed to describe, explain, and predict the human brain’s behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.

Abstract Neuroscience has studied deductive reasoning over the last 20 years under the assumption that deductive inferences are not only de jure but also de facto distinct from other forms of inference. The objective of this research is to verify if logically valid deductions leave any cerebral electrical trait that is distinct from the trait left by non-valid deductions. 23 subjects with an average age of 20.35 years were registered with MEG and placed into a two conditions paradigm (100 trials for each condition) which each presented the exact same relational complexity (same variables and content) but had distinct logical complexity. Both conditions show the same electromagnetic components (P3, N4) in the early temporal window (250–525 ms) and P6 in the late temporal window (500–775 ms). The significant activity in both valid and invalid conditions is found in sensors from medial prefrontal regions, probably corresponding to the ACC or to the medial prefrontal cortex. The amplitude and intensity of valid deductions is significantly lower in both temporal windows (p = 0.0003). The reaction time was 54.37% slower in the valid condition. Validity leaves a minimal but measurable hypoactive electrical trait in brain processing. The minor electrical demand is attributable to the recursive and automatable character of valid deductions, suggesting a physical indicator of computational deductive properties. It is hypothesized that all valid deductions are recursive and hypoactive.

Abstract Background Electrophysiological studies show that reductions in power within the alpha band are associated with the Alzheimer’s disease (AD) continuum. Physical activity (PA) is a protective factor that has proved to reduce AD risk and pathological brain burden. Previous research has confirmed that exercise increases power in the alpha range. However, little is known regarding whether other non-modifiable risk factors for AD, such as increased age or APOE ε4 carriage, alter the association between PA and power in the alpha band. Methods The relationship between PA and alpha band power was examined in a sample of 113 healthy adults using magnetoencephalography. Additionally, we explored whether ε4 carriage and age modulate this association. The correlations between alpha power and gray matter volumes and cognition were also investigated. Results We detected a parieto-occipital cluster in which PA positively correlated with alpha power. The association between PA and alpha power remained following stratification of the cohort by genotype. Younger and older adults were investigated separately, and only younger adults exhibited a positive relationship between PA and alpha power. Interestingly, when four groups were created based on age (younger-older adult) and APOE (E3/E3-E3/E4), only younger E3/E3 (least predicted risk) and older E3/E4 (greatest predicted risk) had associations between greater alpha power and higher PA. Among older E3/E4, greater alpha power in these regions was associated with improved memory and preserved brain structure. Conclusion PA could protect against the slowing of brain activity that characterizes the AD continuum, where it is of benefit for all individuals, especially E3/E4 older adults.

Abstract Background Neuronal hyperexcitability and hypersynchrony have been described as key features of neurophysiological dysfunctions in the Alzheimer’s disease (AD) continuum. Conversely, physical activity (PA) has been associated with improved brain health and reduced AD risk. However, there is controversy regarding whether AD genetic risk (in terms of APOE ε4 carriage) modulates these relationships. The utilization of multiple outcome measures within one sample may strengthen our understanding of this complex phenomenon. Method The relationship between PA and functional connectivity (FC) was examined in a sample of 107 healthy older adults using magnetoencephalography. Additionally, we explored whether ε4 carriage modulates this association. The correlation between FC and brain structural integrity, cognition, and mood was also investigated. Results A relationship between higher PA and decreased FC (hyposynchrony) in the left temporal lobe was observed among all individuals (across the whole sample, in ε4 carriers, and in ε4 non-carriers), but its effects manifest differently according to genetic risk. In ε4 carriers, we report an association between this region-specific FC profile and preserved brain structure (greater gray matter volumes and higher integrity of white matter tracts). In this group, decreased FC also correlated with reduced anxiety levels. In ε4 non-carriers, this profile is associated with improved cognition (working and episodic memory). Conclusions PA could mitigate the increase in FC (hypersynchronization) that characterizes preclinical AD, being beneficial for all individuals, especially ε4 carriers.

BackgroundStroke 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.MethodsFor 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.FindingsAfter 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).

Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer’s Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.

Abstract Introduction An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting‐state acquisition and analysis to facilitate the pooling of data from different sites. Methods Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI. Results The spectral analyses confirmed frequency‐specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results. Discussion This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large‐scale collaborative projects.

BackgroundCognitive stimulation therapy appears to show promising results in the rehabilitation of impaired cognitive processes in attention deficit hyperactivity disorder. ObjectiveEncouraged by this evidence and the ever-increasing use of technology and artificial intelligence for therapeutic purposes, we examined whether cognitive stimulation therapy implemented on a mobile device and controlled by an artificial intelligence engine can be effective in the neurocognitive rehabilitation of these patients. MethodsIn this randomized study, 29 child participants (25 males) underwent training with a smart, digital, cognitive stimulation program (KAD_SCL_01) or with 3 commercial video games for 12 weeks, 3 days a week, 15 minutes a day. Participants completed a neuropsychological assessment and a preintervention and postintervention magnetoencephalography study in a resting state with their eyes closed. In addition, information on clinical symptoms was collected from the child´s legal guardians. ResultsIn line with our main hypothesis, we found evidence that smart, digital, cognitive treatment results in improvements in inhibitory control performance. Improvements were also found in visuospatial working memory performance and in the cognitive flexibility, working memory, and behavior and general executive functioning behavioral clinical indexes in this group of participants. Finally, the improvements found in inhibitory control were related to increases in alpha-band power in all participants in the posterior regions, including 2 default mode network regions of the interest: the bilateral precuneus and the bilateral posterior cingulate cortex. However, only the participants who underwent cognitive stimulation intervention (KAD_SCL_01) showed a significant increase in this relationship. ConclusionsThe results seem to indicate that smart, digital treatment can be effective in the inhibitory control and visuospatial working memory rehabilitation in patients with attention deficit hyperactivity disorder. Furthermore, the relation of the inhibitory control with alpha-band power changes could mean that these changes are a product of plasticity mechanisms or changes in the neuromodulatory dynamics. Trial RegistrationISRCTN Registry ISRCTN71041318; https://www.isrctn.com/ISRCTN71041318

Abstract Background Alzheimer’s disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. Methods Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. Results The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. Conclusions According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.

Objective: The role of the central nervous system in the pathophysiology of frailty is controversial. We used magnetoencephalography (MEG) to search for abnormalities in the ongoing oscillatory neural activity of frail individuals without global cognitive impairment.Methods: Fifty four older (≥70 years) and cognitively healthy (Mini-Mental State Examination ≥24) participants were classified as robust (0 criterion, n = 34) or frail (≥ 3 criteria, n = 20) following Fried's phenotype. Memory, language, attention, and executive function were assessed through well-validated neuropsychological tests. Every participant underwent a resting-state MEG and a T1-weighted magnetic resonance imaging scan. We performed MEG power spectral analyses to compare the electrophysiological profiles of frail and robust individuals. We used an ensemble learner to investigate the ability of MEG spectral power to discriminate frail from robust participants.Results: We identified increased relative power in the frail group in the mu (p < 0.05) and sensorimotor (p < 0.05) frequencies across right sensorimotor, posterior parietal, and frontal regions. The ensemble learner discriminated frail from robust participants [area under the curve = 0.73 (95% CI = 0.49–0.98)]. Frail individuals performed significantly worse in the Trail Making Test, Digit Span Test (forward), Rey-Osterrieth Complex Figure, and Semantic Fluency Test.Interpretation: Frail individuals without global cognitive impairment showed ongoing oscillatory alterations within brain regions associated with aspects of motor control, jointly to failures in executive function. Our results suggest that some physical manifestations of frailty might partly arise from failures in central structures relevant to sensorimotor and executive processing.

The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.

The progressive aging of the population represents a challenge for society. In particular, a strong increase in the number of people over 90 is expected in the next two decades. As this phenomenon will lead to an increase in illness and age-related dependency, the study of long-lived people represents an opportunity to explore which lifestyle factors are associated with healthy aging and which with the emergence of age-related diseases, especially Alzheimer's type dementia. The project "Factors associated with healthy and pathologically aging in a sample of elderly people over 90 in the city of Madrid" (MADRID+90) brings together a multidisciplinary research team in neurodegenerative diseases that includes experts in epidemiology, neurology, neuropsychology, neuroimaging and computational neuroscience. In the first phase of the project, a stratified random sampling was carried out according to the census of the city of Madrid followed by a survey conducted on 191 people aged 90 and over. This survey gathered information on demographics, clinical data, lifestyles and cognitive status. Here, the main results of that survey are showed. The second phase of the project aims to characterize individual trajectories in the course of either healthy and pathological aging, from a group of 50 subjects over 90 who will undergo a comprehensive clinical examination comprised of neurological and cognitive testing, MRI and EEG. The ultimate goal of the project is to characterize the biophysical and clinical profiles of a population that tends to receive little attention in the literature. A better understanding of the rapidly increasing group of nonagenarians will also help to design new policies that minimize the impact and future social and economic consequences of rapidly aging societies.

Frailty is a common representation of cumulative age-related decline that may precede disability in older adults. In our study, we used magnetoencephalography (MEG) to explore the existence of abnormalities in the synchronization patterns of frail individuals without global cognitive impairment. Fifty-four older (≥70 years) and cognitively healthy (Mini-Mental State Examination ≥24) adults, 34 robust (not a single positive Fried criterion) and 20 frail (≥3 positive Fried criteria) underwent a resting-state MEG recording and a T1-weighted magnetic resonance imaging scan. Seed-based functional connectivity (FC) analyses were used to explore group differences in the synchronization of fronto-parietal areas relevant to motor function. Additionally, we performed group comparisons of intra-network FC for key resting-state networks such as the sensorimotor, fronto-parietal, default mode, and attentional (dorsal and ventral) networks. Frail participants exhibited reduced FC between posterior regions of the parietal cortex (bilateral supramarginal gyrus, right superior parietal lobe, and right angular gyrus) and widespread clusters spanning mainly fronto-parietal regions. Frail participants also demonstrated reduced intra-network FC within the fronto-parietal, ventral attentional, and posterior default mode networks. All the FC results concerned the upper beta band, a frequency range classically linked to motor function. Overall, our findings reveal the existence of abnormalities in the synchronization patterns of frail individuals within central structures important for accurate motor control. This study suggests that alterations in brain connectivity might contribute to some motor impairments associated with frailty.

Abstract Amyloid-β (Aβ) deposition is known to starts decades before the onset of clinical symptoms of Alzheimer’s disease (AD), however, the detailed pathophysiological processes underlying this preclinical period are not well understood. This study aimed to investigate functional network alterations in cognitively intact elderly individuals at risk for AD, and assessed the association between these network alterations and changes in Aβ deposition, glucose metabolism, and brain structure. Forty-five cognitively normal elderly subjects, who were classified into Aβ-positive (CN+) and Aβ-negative (CN−) groups using 11C-Pittsburgh compound B PET, underwent resting state magnetoencephalography measurements, 18F-fluorodeoxyglucose PET (FDG-PET) and structural MRI. Results demonstrated that in the CN+ group, functional connectivity (FC) within the precuneus was significantly decreased, whereas it was significantly enhanced between the precuneus and the bilateral inferior parietal lobules in the low-frequency bands (theta and delta). These changes were suggested to be associated with local cerebral Aβ deposition. Most of Aβ+ individuals in this study did not show any metabolic or anatomical changes, and there were no significant correlations between FC values and FDG-PET or MRI volumetry data. These results demonstrate that functional network alterations, which occur in association with Aβ deposition, are detectable using magnetoencephalography before metabolic and anatomical changes are seen.

Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.

Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD.In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network.Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease. Keywords: Mild cognitive impairment, Conversion, Magnetoencephalography, Source reconstruction, Dynamic functional connectivity analysis, Connectomic biomarker

Purpose: Juvenile myoclonic epilepsy (JME) is one of the most common generalized idiopathic epilepsies of childhood and adolescence. In some patients with JME, mathematical calculus and praxis may induce myoclonic seizures. Methods: A reflex myoclonic seizure was recorded by simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) when a generalized spike–wave synchronous pattern at 3 Hz was observed. Results: Source reconstruction localized the epileptogenic area to the right premotor frontal cortex. Conclusions: The present study demonstrates that the origin of epileptiform activity in JME can be localized in brain areas associated with the premotor frontal cortex.

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer’s Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multi-neuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, which allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multi-dimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor-inhibitory cognitive task. The results show that out method exhibits similar performance compared with other classification methods, indicating the effectiveness of our approach. In addition, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication, or early detection of diseases.

The pathophysiological processes undermining brain functioning decades before the onset of the clinical symptoms associated with dementia are still not well understood. Several heritability studies have reported that the Brain Derived Neurotrophic Factor (BDNF) Val66Met genetic polymorphism could contribute to the acceleration of cognitive decline in aging. This mutation may affect brain functional connectivity (FC), especially in those who are carriers of the BDNF Met allele. The aim of this work was to explore the influence of the BDNF Val66Met polymorphism in whole brain eyes-closed, resting-state magnetoencephalography (MEG) FC in a sample of 36 cognitively intact (CI) older females. All of them were ε3ε3 homozygotes for the apolipoprotein E (APOE) gene and were divided into two subgroups according to the presence of the Met allele: Val/Met group (n = 16) and Val/Val group (n = 20). They did not differ in age, years of education, Mini-Mental State Examination scores, or normalized hippocampal volumes. Our results showed reduced antero-posterior gamma band FC within the Val/Met genetic risk group, which may be caused by a GABAergic network impairment. Despite the lack of cognitive decline, these results might suggest a selective brain network vulnerability due to the carriage of the BDNF Met allele, which is linked to a potential progression to dementia. This neurophysiological signature, as tracked with MEG FC, indicates that age-related brain functioning changes could be mediated by the influence of particular genetic risk factors.

Background: Neuroimaging techniques are a cornerstone for diagnosing and investigating cognitive decline and dementia in the elderly. In frailty research, the physical as opposed to the cognitive domain of the aging process, neuroimaging studies are less common. Here we systematically review the use of neuroimaging techniques in frailty research.Methods: We searched PUBMED for any publication reporting the association between neuroimaging markers and frailty, following Fried's original definition, as well as its determining phenotypes: gait speed, grip strength, fatigue and recent weight loss in the non-diseased population older than 65 years.Results: The search returned a total of 979 abstracts which were independently screened by 3 reviewers. In total, 17 studies met the inclusion criteria. Of these, 12 studies evaluated gait speed, 2 grip strength, and 3 frailty (2 Fried Frailty, 1 Frailty Index). An association between increased burden of white matter lesions, lower fractional anisotropy, and higher diffusivity has been associated consistently to frailty and worse performance in the different frailty components.Conclusions: White matter lesions were significantly associated to frailty and frailty components thus highlighting the potential utility of neuroimaging in unraveling the underlying mechanisms of this state. However, considering small sample size and design effects, it is not possible to completely rule out reverse causality between frailty and neuroimaging findings. More studies are needed to clarify this important clinical question.

The ever increasing proportion of aged people in modern societies is leading to a substantial increase in the number of people affected by dementia, and Alzheimer’s Disease (AD) in particular, which is the most common cause for dementia. Throughout the course of the last decades several different compounds have been tested to stop or slow disease progression with limited success, which is giving rise to a strong interest toward the early stages of the disease. Alzheimer’s disease has an extended an insidious preclinical stage in which brain pathology accumulates slowly until clinical symptoms are observable in prodromal stages and in dementia. For this reason, the scientific community is focusing into investigating early signs of AD which could lead to the development of validated biomarkers. While some CSF and PET biomarkers have already been introduced in the clinical practice, the use of non-invasive measures of brain function as early biomarkers is still under investigation. However, the electrophysiological mechanisms and the early functional alterations underlying preclinical Alzheimer’s Disease is still scarcely studied. This work aims to briefly review the most relevant findings in the field of electrophysiological brain changes as measured by magnetoencephalography (MEG). MEG has proven its utility in some clinical areas. However, although its clinical relevance in dementia is still limited, a growing number of studies highlighted its sensitivity in these preclinical stages. Studies focusing on different analytical approaches will be reviewed. Furthermore, their potential applications to establish early diagnosis and determine subsequent progression to dementia are discussed.

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.

Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5–40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated.

Synaptic disruption is an early pathological sign of the neurodegeneration of Dementia of the Alzheimer's type (DAT). The changes in network synchronization are evident in patients with Mild Cognitive Impairment (MCI) at the group level, but there are very few Magnetoencephalography (MEG) studies regarding discrimination at the individual level. In an international multicenter study, we used MEG and functional connectivity metrics to discriminate MCI from normal aging at the individual person level. A labeled sample of features (links) that distinguished MCI patients from controls in a training dataset was used to classify MCI subjects in two testing datasets from four other MEG centers. We identified a pattern of neuronal hypersynchronization in MCI, in which the features that best discriminated MCI were fronto-parietal and interhemispheric links. The hypersynchronization pattern found in the MCI patients was stable across the five different centers, and may be considered an early sign of synaptic disruption and a possible preclinical biomarker for MCI/DAT.

Background: Previous studies have reported anomalous resting brain activity in the electroencephalogram (EEG) of alcoholics, often reflected as increased power in the beta and theta frequency bands. The effects of binge drinking, the most common pattern of excessive alcohol consumption during adolescence and youth, on brain activity at rest is still poorly known. In this study, we sought to assess the pattern of resting-state EEG oscillations in college-aged binge drinkers (BDs).Methods: Resting-state brain activity during eyes-open and eyes-closed conditions was recorded from 60 channels in 80 first-year undergraduate students (40 controls and 40 BDs). Cortical sources activity of EEG rhythms was estimated using exact Low-Resolution Electromagnetic Tomography (eLORETA) analysis.Results: EEG-source localization analysis revealed that BDs showed, in comparison with controls, significantly higher intracranial current density in the beta frequency band over the right temporal lobe (parahippocampal and fusiform gyri) during eyes-open resting state as well as higher intracranial current density in the theta band over the bilateral occipital cortex (cuneus and lingual gyrus) during eyes-closed resting condition.Conclusions: These findings are in line with previous results observing increased beta and/or theta power following chronic or heavy alcohol drinking in alcohol-dependent subjects and BDs. Increased tonic beta and theta oscillations are suggestive of an augmented cortical excitability and of potential difficulties in the information processing capacity in young BDs. Furthermore, enhanced EEG power in these frequency bands may respond to a neuromaturational delay as a result of excessive alcohol consumption during this critical brain developmental period.

Functional connectivity (FC) alterations represent a key feature in Alzheimer's Disease (AD) and provide a useful tool to characterize and predict the course of the disease. Those alterations have been also described in Mild Cognitive Impairment (MCI), a prodromal stage of AD. There is a growing interest in detecting AD pathology in the brain in the very early stages of the disorder. Subjective Cognitive Decline (SCD) could represent a preclinical asymptomatic stage of AD but very little is known about this population. In the present work we assessed whether FC disruptions are already present in this stage, and if they share any spatial distribution properties with MCI alterations (a condition known to be highly related to AD). To this end, we measured electromagnetic spontaneous activity with MEG in 39 healthy control elders, 41 elders with SCD and 51 MCI patients. The results showed FC alterations in both SCD and MCI compared to the healthy control group. Interestingly, both groups exhibited a very similar spatial pattern of altered links: a hyper-synchronized anterior network and a posterior network characterized by a decrease in FC. This decrease was more pronounced in the MCI group. These results highlight that elders with SCD present FC alterations. More importantly, those disruptions affected AD typically related areas and showed great overlap with the alterations exhibited by MCI patients. These results support the consideration of SCD as a preclinical stage of AD and may indicate that FC alterations appear very early in the course of the disease.

Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.