Biology subjects

Bruna, R.

Publications and source records attributed to Bruna, R..

4 recordsLinked to original sources

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↗