Biology subjects

Bruna, R.

Publications and source records attributed to Bruna, R..

10 recordsLinked to original sources

bioRxiv2024

EEG Electrodes and Where to Find Them: Automated Localization From 3D Scans

ObjectiveThe accurate localization of electroencephalography (EEG) electrode positions is crucial for accurate source localization. Recent advancements have proposed alternatives to labor-intensive, manual methods for spatial localization of the electrodes, employing technologies such as 3D scanning and laser scanning. These novel approaches often integrate Magnetic Resonance Imaging (MRI) as part of the pipeline in localizing the electrodes. The limited global availability of MRI data restricts its use as a standard modality in several clinical scenarios. This limitation restricts the use of these advanced methods. ApproachIn this paper, we present a novel, versatile approach that utilizes 3D scans to localize EEG electrode positions with high accuracy. Importantly, while our method can be integrated with MRI data if available, it is specifically designed to be highly effective even in the absence of MRI, thus expanding the potential for advanced EEG analysis in various resource-limited settings. Our solution implements a two-tiered approach involving landmark/fiducials localization and electrode localization, creating an end-to-end framework. Main resultsThe efficacy and robustness of our approach have been validated on an extensive dataset containing over 400 3D scans from 278 subjects. The framework identifies pre-auricular points and achieves correct electrode positioning accuracy in the range of 85.7% to 91.0%. Additionally, our framework includes a validation tool that permits manual adjustments and visual validation if required. SignificanceThis study represents, to the best of the authors knowledge, the first validation of such a method on a substantial dataset, thus ensuring the robustness and generalizability of our innovative approach. Our findings focus on developing a solution that facilitates source localization, contributing to the critical discussion on balancing cost effectiveness with methodological accuracy to promote wider adoption in both research and clinical settings.

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

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↗

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

The more, the merrier: multivariate phase synchronization methods excel pairwise ones in estimating brain connectivity from reconstructed neural sources

The estimation of functional connectivity (FC) from electro-(EEG) or magnetoencephalographic (MEG) recordings suffers from low spatial resolution, being one of the reasons for the reduced number of sensors compared to the number of reconstructed sources of activity. This problem can be avoided by estimating FC between larger regions instead of individual sources. However, combining all the sources in each area to produce a single time series per region is far from trivial. We have used simultaneous EEG/MEG data from 11 participants and compared the FC estimates from both techniques by using different multivariate approaches. Since the underlying generators are identical for EEG and MEG, the more similar the FC estimation from both techniques is, the more accurate it is likely to be. The results show that using either the average or the root-mean-square of the bivariate source-to-source FC estimates consistently outperforms the use of a representative time series from each area. We concluded that the reconstructed activity in each brain region is too complex to be reduced to a single representative time series and that full multivariate approaches must be used to describe between-area FC from electrophysiological recordings accurately. Moreover, the high correlation between the FC values estimated from EEG and MEG suggests that the results found in the high-sensitivity, low-noise MEG can be transferable to the more affordable EEG, at least when high-quality source reconstruction is used.

bioengineering↗

bioRxiv2022

Tactile attention modulates occipital alpha oscillations in early blindness

Alpha oscillatory activity is thought to contribute to the cueing of visual attention through the engagement of task-relevant occipital regions. In early blindness, occipital alpha oscillations are systematically reduced, suggesting that occipital alpha depends on visual experience. However, it is still unknown if alpha activity could serve attentional cueing in non-visual modalities in blind people, especially considering previous research that showed the recruitment of the occipital cortex for non-visual processing. To test this idea, we used electroencephalography to answer whether alpha oscillations reflected a differential recruitment of task-relevant regions between expected and unexpected conditions in two (texture and shape discrimination) haptic tasks. As expected, time frequency analyses showed that alpha suppression in parieto-occipital sites was significantly reduced in early blind individuals. The source reconstruction analysis revealed that group differences originated in the middle occipital cortex. In that region, expected trials evoked higher alpha desynchronization than unexpected trials in the EB group only. Our results support the role of alpha rhythms in the recruitment of occipital areas also in early blind participants, and for the first time we show that even if posterior alpha activity is reduced in blindness, it is however sensitive to task-dependent factors. Our findings therefore suggest that occipital alpha activity may be involved in tactile attention in blind individuals, maintaining the function proposed for visual attention in sighted population but switched to the tactile modality. Altogether, our results indicate that attention-dependent modulation of alpha oscillatory activity does not depend on visual experience. SIGNIFICANCE STATEMENTAre posterior alpha oscillations and their role in attention dependent on visual experience? Our results show that tactile attention can modulate posterior alpha activity in blind (but not sighted) individuals through the engagement of occipital regions, suggesting that in early blindness, alpha oscillations maintain their proposed role in visual attention but subserve tactile processing. Our findings bring a new understanding to the role that alpha oscillatory activity plays in blindness, contrasting with the view supporting that alpha activity is rather task unspecific.

neuroscience↗

bioRxiv2022

Modified MRI anonymization (de-facing) for improved MEG coregistration

Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing of structural MRI data often requires removal of the face to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach, and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.

molecular biology↗

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↗

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↗