Biology subjects

Maestu, F.

Publications and source records attributed to Maestu, F..

16 recordsLinked to original sources

bioRxiv2026

Neurodevelopment Trajectory of Electrophysiological Functional Connectivity Following Alcohol Use Initiation

BackgroundAdolescence is characterized by profound neurodevelopmental changes that shape large-scale brain network organization and may confer vulnerability to risk-taking behaviors, including alcohol use. While cross-sectional and prospective studies have examined functional connectivity (FC) alterations before and after consumption, there is little evidence of how networks evolve during adolescence. MethodsThe present longitudinal study investigated electrophysiological FC trajectories during alcohol initiation using resting-state magnetoencephalography (MEG). 61 alcohol-naive adolescents (mean age at baseline = 14.4) were assessed and re-evaluated two years later (mean age = 16.4). ResultsAt baseline, stronger FC in theta (4-8 Hz), alpha (8-12 Hz), and high-beta (20-30 Hz) bands predicted greater alcohol consumption at follow-up, replicating previous findings. Longitudinal analyses with linear mixed-effects models revealed significant stage x SAUs interactions across all three frequency bands. Adolescents with low-to-moderate alcohol use showed normative increases in FC over time, consistent with typical neurodevelopmental maturation. In contrast, heavier drinkers exhibited stabilization or reduction of FC, suggesting a divergence from normative trajectories. Notably, theta-band hyperconnectivity persisted after alcohol initiation and remained positively associated with current alcohol consumption, particularly across anteroposterior connections. ConclusionThese findings indicate heterogeneous neurodevelopmental trajectories associated with alcohol use severity. Elevated pre-consumption connectivity, especially in the theta band, may reflect a vulnerability marker rather than solely a consequence of alcohol exposure. Overall, results highlight the importance of considering individual variability in brain maturation when examining adolescent substance use and suggest that early hyperconnectivity may signal increased risk for heavier alcohol involvement.

neuroscience↗

bioRxiv2024

Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories

Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neurodegenerative disorders, such as Alzheimers disease. In this study, we developed a Fully Hyperbolic Neural Network (FHNN) to embed functional brain connectivity graphs derived from magnetoencephalography (MEG) data into low dimensions on a Lorentz model of hyperbolic space. Using this model, we computed hyperbolic embeddings of the MEG brain networks of 587 individuals from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Notably, we leveraged a unique metric--the radius of the node embeddings--which effectively captures the hierarchical organization of the brain, to characterize subtle hierarchical organizational changes in various brain subnetworks attributed to the aging process. Our findings revealed that a considerable number of subnetworks exhibited a reduction in hierarchy during aging, with some showing gradual changes and others undergoing rapid transformations in the elderly. Moreover, we demonstrated that hyperbolic features outperform traditional graph-theoretic measures in capturing age-related information in brain networks. Overall, our study represents the first evaluation of hyperbolic embeddings in MEG brain networks for studying aging trajectories, shedding light on critical regions undergoing significant age-related alterations in the large cohort of the Cam-CAN dataset.

bioinformatics↗

bioRxiv2024

Individual alpha frequency tACS reduces functional connectivity across the default mode network

Structured AbstractO_ST_ABSObjectivesC_ST_ABSResearch on the influence of transcranial alternating current stimulation over alpha functional connectivity (FC) is scarce, and at the same time poses as a potential treatment for various diseases. This study aimed to investigate the effects of individualized alpha frequency tACS (IAF-tACS) on FC within the default mode network (DMN) in healthy individuals, particularly focusing on the precuneus (PCU) as a major hub within the network. Materials and Methods27 healthy participants were recruited, which underwent a 20-minute IAF-tACS session and three magnetoencephalography (MEG) recordings: two pre-stimulation and one post-stimulation. Participants were randomly assigned to either the stimulation or sham group. FC was evaluated through the corrected imaginary phase locking value (ci-PLV) and leakage corrected amplitude envelope correlation (AEC-c). Statistical analyses compared both Pre-Post FC ratio between groups through ratio t-tests and intragroup FC changes through repeated measures t-tests, with FDR correction applied to account for multiple comparisons. An additional analysis simulated the influence of the cortical folding on the effect of tACS over FC. ResultsIAF-tACS significantly decreased AEC-c within the PCU and DMN in the stimulation group compared to the sham group, especially influencing antero-posterior links between hubs of the DMN. No significant changes were observed in ci-PLV connectivity metrics. Negative correlations were found between AEC-c FC changes and power alterations in posterior DMN areas, suggesting a complex interaction between cortical folding and electric field direction. ConclusionsAgainst our initial hypothesis, IAF-tACS reduced FC in the DMN, possibly due to phase disparities introduced by cortical gyrification. These findings suggest that tACS might modulate FC in a more complex manner than previously thought, highlighting the need for further research into the personalized application of neuromodulation techniques, as well as its potential therapeutic implications for conditions like Alzheimers disease.

neuroscience↗

bioRxiv2024

Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants

INTRODUCTIONElectrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimers disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults. METHODS73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each sources strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters connectivity. RESULTSFrontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity. DISCUSSIONThese results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.

neuroscience↗

bioRxiv2024

From electrophysiology to drink: Adolescent alcohol consumption predicted by differences in functional connectivity and neuroanatomy

Alcohol consumption during adolescence has been associated with neuroanatomical abnormalities and the appearance of future disorders. However, the latest advances in this field point to the existence of risk profiles which may lead to some individuals into an early consumption. To date, some studies have established predictive models of consumption based on sociodemographic, behavioural, and anatomical-functional variables using MRI. However, the neuroimaging variables employed are usually restricted to local and hemodynamic phenomena. Given the potential of connectome approaches, and the high temporal dynamics of electrophysiology, we decided to explore the relationship between future alcohol consumption and electrophysiological connectivity measured by MEG in a cohort of 83 individuals aged 14 to 16. We calculated predictive models throughout multiple linear regressions based on behavioural, anatomical, and functional connectivity variables. As a result, we found a positive correlation between alcohol consumption and the functional connectivity in frontal, parietal, and frontoparietal connections. Also, we identified negative relationships of alcohol consumption with neuroanatomical variables. Finally, the linear regression analysis determined the importance of anatomical and functional variables in the prediction of alcohol consumption but failed to find associations with impulsivity, sensation-seeking, and executive function scales. As conclusion, the predictive traits obtained in these models were closely associated with changes occurring during neurodevelopment, suggesting the existence of different paths in neurodevelopment that have the potential to influence adolescents relationship with alcohol consumption. Significance statementTo understand the onset of heavy drinking habits and develop prevention strategies, we need to characterize predisposition profiles at early ages. This longitudinal work provides important evidence by showing how adolescents at risk for engaging in alcohol behaviors showed resting-state functional connectivity and grey matter differences years before. The combination of these metrics allows us to establish predictive models of future alcohol episodes. In addition, differences in functional connectivity showed a positive relationship with behavioral variables such as lower executive functions and higher on the sensation-seeking. These predisposition phenotypes may rely on divergent neurodevelopmental pathways and deeper neurobiological abnormalities, such as dysfunctions of inhibitory neurotransmission and/or a genetic background of vulnerability.

neuroscience↗

bioRxiv2024

Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning analysis

BackgroundThe demand for fresh strategies to analyze intricate multidimensional data in neuroscience is increasingly evident. One of the most complex events during our neurodevelopment is adolescence, where our nervous system suffers constant changes, not only in neuroanatomical traits, but also in neurophysiological components. One of the most impactful factors we deal with during this time is our environment, especially when encountering external factors such as social behaviors or substance consumption. Binge Drinking (BD) has emerged as an extended pattern of alcohol consumption in teenagers, not only affecting their future lifestyle, but changing their neurodevelopment. Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. MethodsIn this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had two years later. We used hierarchical agglomerative UML techniques based on Wards minimum variance criterion to clusterize relations between power spectrum and functional connectivity and alcohol consumption, based on similarity in their correlations, in frequency bands from theta to gamma. ResultsWe found that all frequency bands studied had a pattern of clusterization based on anatomical regions of interest related to neurodevelopment and cognitive and behavioral aspects of addiction, highlighting the dorsolateral and medial prefrontal, the sensorimotor, the medial posterior and the occipital cortices. All this patterns, of great cohesion and coherence, showed an abnormal electrophysiological activity, representing a dysregulation in the development of core resting-state networks. The clusters found maintained not only plausibility in nature, but robustness, making this a great example of the usage of UML in the analysis of electrophysiological activity, a new perspective into analysis that, while contributing to classical statistics, can clarify new characteristics of the variables of interest.

neuroscience↗

bioRxiv2024

Dynamics of Brain Connectivity across the Alzheimer's Disease Spectrum: a magnetoencephalography study

Alzheimers disease (AD) represents a major challenge in neurodegenerative disease research, as it is characterized by a complex pathophysiology that involves not only structural but also functional changes in the brain. While changes in static functional connectivity have already been linked to AD, there is still a lack of research studying dynamic functional connectivity (dFC) across the AD continuum, which could be crucial for identifying potential biomarkers for early diagnosis and tracking disease progression. This study leverages the high temporal resolution of MEG to dissect the dynamics of brain connectivity alterations across various stages of AD and their association with cognitive decline and structural brain changes. 321 participants were included in this study, categorized into healthy control, subjective cognitive decline (SCD), and mild cognitive impairment (MCI) groups. Amplitude envelope correlation with leakage correction was calculated over MEG signals using a sliding window, and the correlation across epochs was studied to assess dFC at whole-brain and node level. Finally, we explored dFC associations with cognitive scores, grey matter volume, and white matter fractal anisotropy. The study unveils a significant reduction in whole-brain dFC, especially within the alpha and beta frequency bands, as individuals advance along the AD continuum. Notably, the frontal and temporal lobes, as well as regions within the default mode network, exhibited pronounced dFC reductions. Finally, dFC significantly correlated with cognitive performance and changes in structural brain, suggesting the potential of the proposed dFC metric as sensitive indicator for monitoring disease progression. This investigation provides crucial insights into the temporal dynamics of brain connectivity alterations in the early stages of the AD spectrum, underlining the importance of dFC changes as reflective of cognitive and anatomical degeneration. The findings hint towards a strong relationship between connectivity profiles and white matter integrity, especially for high frequency activity in the association cortices. Key PointsO_LIDynamic functional connectivity declines over the AD spectrum. C_LIO_LIDynamic functional connectivity reductions are most prominent in orbitofrontal, temporal, and DMN-related areas. C_LIO_LICognitive performance, brain volumetrics, and white matter integrity parameters positively correlate with dynamic functional connectivity over the whole AD spectrum, and most significantly among mild cognitive impairment participants. C_LI

neuroscience↗

bioRxiv2024

Effects of Alzheimer's disease plasma marker levels on multilayer centrality in healthy individuals

Finding early and non-invasive biomarkers that help identify individuals in the earliest stages of the Alzheimers disease continuum is paramount. Electrophysiology and plasma biomarkers are great candidates in this pursuit. Furthermore, the combination of functional connectivity metrics with graph-theory analyses allows for a deeper understanding of network alterations. Despite this, this is the first MEG study to assess multilayer centrality considering inter-band connectivity in an unimpaired population at high risk of Alzheimers disease. Our objective is twofold. First, to address the relationship between a compound centrality score designed to overcome previous inconsistencies stemming from the use of various individual metrics, and plasma pathology markers of Alzheimers disease in unimpaired individuals with elevated levels of the latter. Lastly, to evaluate whether hubs centrality is more affected by the pathology. 33 individuals with available MEG recordings and elevated plasma pathology markers were included. A compound centrality score for each brain source of every subject was calculated combining widely used centrality metrics, considering intra- and inter-band connections. Spearman correlations were carried out to address the association between each nodes centrality score and biomarkers levels. Next, to test whether greater associations were found in hubs, a correlation between the obtained rho and the grand-average of the centrality score was carried out. Increasing concentrations of p-tau231 were associated with greater centrality within the network of posterior areas, which increased their connectedness in the theta range with the remaining areas, regardless of the latters frequency range. The opposite relationship was found for left areas, that decreased their connectedness in the gamma frequency range. Hubs centrality was significantly more affected by p-tau231 levels. Our results expand previous literature demonstrating early network reorganizations associated with elevated plasma p-tau231 in cognitively unimpaired individuals. Multilayer centrality increases in the theta band in posterior regions are congruent with previous results and theoretical models, that predict a longitudinal evolution towards a loss of centrality. On the other hand, the changes in multilayer centrality found in the gamma band could be associated with inhibitory neuron dysfunction, classical in AD pathology. Lastly, hubs were more likely to increase their centrality in association to p-tau231, thus corroborating hubs vulnerability.

neuroscience↗

bioRxiv2023

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimers disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric--the radius of the node embeddings--which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimers disease.

bioinformatics↗

bioRxiv2023

A multiscale closed-loop neurotoxicity model of Alzheimer's disease progression explains functional connectivity alterations

While the accumulation of amyloid-beta (A{beta}) and hyperphosphorylated-tau (hp-tau) as two classical histopathological biomarkers are crucial in Alzheimers disease (AD), their detailed interaction with the electrophysiological changes at the meso- and macroscale are not yet fully understood. We developed a mechanistic mequltiscale model of AD progression, linking proteinopathy to its effects on neural activity and vice-versa. We integrated a heterodimer model of prion-like protein propagation, and a network of Jansen-Rit electrical oscillators whose model parameters varied due to neurotoxicity. Changes in inhibition guided the electrophysiological alterations found in AD, and both A{beta} and hp-tau-related inhibition changes were able to produce similar effects independently. Additionally, we found a causal disconnection between cellular hyperactivity and interregional hypersynchrony. Finally, we demonstrated that early A{beta} and hp-tau depositions location determine the spatiotemporal profile of the proteinopathy. The presented model combines the molecular effects of bothA{beta} and hp-tau together with a mechanistic protein propagation model and network effects within a unique closed-loop model. This holds the potential to enlighten the interplay between AD mechanisms on various scales, aiming to develop and test novel hypotheses on the contribution of different AD-related variables to the disease evolution. Significance StatementThis research presents a groundbreaking closed-loop model of AD mechanisms, bridging the gap between protein distribution and neural activity. Contrary to prior assumptions, the study reveals that interregional hyper-synchrony and cellular hyperactivity are not directly linked. Notably, the model identifies neural inhibition as a potential causal factor in neurophysiological AD alterations and posits early depositions of A{beta} as a determinant of the spatiotemporal profile of proteinopathy. The significance of this mechanistic disease framework lies in its potential to produce insights into AD evolution and to guide novel treatment strategies. It underscores the importance of further experiments and modelling efforts to refine our understanding of AD, offering hope for more effective treatments and personalized care in the fight against dementia.

neuroscience↗

bioRxiv2023

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our results reveal complex, non-linear trajectories of power changes that challenge the linear model traditionally reported. Moreover, these trajectories also exhibit variations across cortical regions. Remarkably, we observed that increases in slow wave activity are associated with a better cognitive performance across the lifespan, as well as with larger gray matter volume for elderlies, while fast wave activity decreases with adulthood. These results suggest that elevated power in low-frequency resting-state activity during aging may reflect a proxy for deterioration, rather than serving as a compensatory mechanism, as usually interpreted. In addition, it enhances our comprehension of both neurodevelopment and the aging process by highlighting the complexity and regional specificity of changes in brain rhythms. Furthermore, our findings have potential implications for understanding cognitive performance and structural integrity.

neuroscience↗

bioRxiv2023

Modeling the role of the thalamus in resting-state functional connectivity: nature or structure

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus. and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate models performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level. Author summarySynchrony between brain regions is an essential aspect of coordinated brain function and serves as a biomarker of health and disease. The thalamus, due to its centrality and widespread connectivity with the cortex, is a crucial structure that may contribute to this synchrony by allowing distant brain regions to work together. In this study, we used computational models to investigate the thalamuss role in generating brain synchrony at rest. Our findings suggest that the structural connectivity of the thalamus is not its primary contribution to brain synchrony. Instead, we found that the thalamus plays a critical role in driving cortical activity, and when it is not driving this activity, its impact on brain synchrony is null. Our study provides valuable insights into the thalamocortical networks role in shaping brain dynamics and FC in resting state, laying the groundwork for further research in this area.

neuroscience↗

bioRxiv2022

E/I unbalance and aberrant oscillation dynamics predict preclinical Alzheimer's disease

Alzheimers disease (AD) is a chronic, nonlinearly progressive neurodegenerative disease that affects multiple domains of behaviour and is the most common form of dementia. However, there is scarce understanding of its biological basis nor there are reliable markers for the earliest disease stages preceding AD. Here we investigated whether AD progression is predicted by increasingly aberrant critical brain dynamics driven by underlying E/I imbalance using magnetoencephalography (MEG) data from cross-sectional (N=343) and longitudinal (N=45) cohorts. As a hallmark of brain criticality, we quantified long-range temporal correlations (LRTCs) in neuronal oscillations and tracked changes in neuronal excitability. We demonstrate that attenuation and progressive changes of LRTCs characterize the earliest stages of disease progression and yield accurate classification to individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Our data indicate that pathological brain critical dynamics in AD progression provide a clinical marker for targeting specific treatments to individuals at increased risk.

neuroscience↗

bioRxiv2019

Cluster-permutation statistical analysis for high-dimensional brain-wide functional connectivity mapping

Brain functional connectivity (FC) analyses based on magnetoencephalographic (MEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, which leads to conservative hypothesis testing. We removed such constraint by extending cluster-permutation statistics for high-dimensional MEG-FC analyses. We demonstrated the feasibility of this approach by identifying MEG-FC resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimers disease. We found dense clusters of increased connectivity strength in MCI compared to healthy controls (hypersynchronization), in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These clusters mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere and could potentially be used as neuromarkers of early progression in Alzheimers disease. Our novel approach can be used to generate high-resolution statistical FC maps for neuroimaging studies in general.

neuroscience↗

bioRxiv2019

Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

It is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimers disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.\n\nThe activity of different brain rhythms {{delta}, {theta}, 1, 2, {beta}1, {beta}2, {gamma}1, {gamma}2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.\n\nWe succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

neuroscience↗

bioRxiv2018

Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease

Understanding how the heart influences brain dynamics will suppose a deep change for the neuroscience, psychology and medicine. A mainstay questions is the heart modulation of resting state brain networks and its relation with both the cardiac dynamics and the cognitive status. We evaluated the heart evoked basal networks for controls and two groups of mild cognitive impairment patients, stable and progressive to Alzheimers disease without cardiovascular alteration symptoms. Our results in controls show that a healthy cognitive performance correlates with the heart modulation of brain dynamics in areas of the default mode network, and that the heart influence on brain networks varies along the cardiac cycle and the spectral band. However, the cognitive deficit produced by dementia correlates with the lack of heart modulation on brain activity. The heart influence on brain networks is disrupted in patients by producing hypersynchronization, accompanied by decreased cardiac complexity. We designed a surrogate and predictive procedure based on machine learning to compare the heart evoked results with the neural activity no locked to heartbeats. Based on our longitudinal data, we conclude that the prediction to progression to Alzheimers disease is higher when considering the heart - brain interaction than when taking into account only the brain dynamics. We can conclude that brain networks in control subjects were more responsive to the heart cycle, allowing a wealthier, more complex pattern of oscillations. Our results highlight the role of heart in cognitive neuroscience by showing that basal brain networks are modulated by the cardiac dynamics.

neuroscience↗