Biology subjects

Pais-Roldan, P.

Publications and source records attributed to Pais-Roldan, P..

6 recordsLinked to original sources

bioRxiv2025

Ultra-High Resolution TR-external EPIK with Deep Learning Image Reconstruction for Enhanced Characterisation of Cortical Depth-dependent Neural Activity

The detection of neural signals using functional MRI at the laminar or columnar level enables non-invasive exploration of fundamental brain function processing and the interconnected pathways within intracortical tissues. The growing interest in this research area is driven by advancements in fMRI acquisition techniques that enhance spatial resolution for high-fidelity mapping. However, the submillimetre voxel sizes commonly employed in layer-specific fMRI studies raise concerns about relatively low signal-to-noise ratios and increased image artefacts in reconstructed images, ultimately limiting the precise delineation of cortical depth-dependent functional activities. This work aims to address this issue by incorporating a deep learning technique for enhanced image reconstruction of the submillimetre fMRI data, acquired with echo-planar-imaging with keyhole (EPIK) combined with the repetition-time-external (TR-external) EPI phase correction scheme. Our network was trained in a self-supervised, scan-specific manner using the sampling strategy from zero-shot self-supervised learning (ZS-SSL) method, which has gained attention for high-resolution MR image reconstruction. Healthy volunteers participated in this study, and the performance of the developed method was evaluated in direct comparison to the conventional reconstruction method using datasets acquired at 7T. The deep learning reconstruction produced reconstructed images with significantly higher SNR than the conventional method, which was further quantitatively validated through histogram analysis. This enhancement was consistent across all slice locations, demonstrating the reliability of the scan-specific deep learning technique.

neuroscience↗

bioRxiv2023

Contribution of cortical layers to human dynamic functional connectivity

Transitory states of the brain, sometimes linked to particular mental disorders, can be identified by conducting a dynamic analysis of connectivity in functional magnetic resonance imaging (fMRI) data. Here, we investigated whether the connectivity between cortical laminae (in contrast to between cortical areas), can vary during the course of a resting-state scan and whether this could impact the identification of brain states in the healthy brain. We performed a sliding-window connectivity analysis of high-resolution fMRI data and found that the differential participation of the cortical layers in the overall cortical connectivity constitutes a more dynamic feature of the brain compared to region-to-region connectivity, with two main states (superficial vs. deep-laminae connectivity) fluctuating over time. Laminar connectivity restricted to the default mode network appeared relatively stable (superficial connectivity preferred), while the central executive and salience networks showed a fluctuating laminar preference. We anticipate that the dynamic analysis of connectivity focused on the different depths of the cerebral cortex could play an important role in characterizing healthy brain states as well as in identifying novel targets of psychiatric diseases.

neuroscience↗

bioRxiv2021

Characterizing brain state-dependent pupil dynamics based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain state changes can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and the whole brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional MRI (fMRI) data showed opposite correlation features to pupil dynamics, demonstrating an LH activity-dependent manner. Our results demonstrate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, indicating a role of the latter in brain state regulation.

neuroscience↗

bioRxiv2021

Mapping of Whole-Brain Resting-State Networks with Half-Millimetre Resolution

Resting-state fMRI has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 x 0.7 x 0.7 mm3, with only partial brain coverage. This work presents a novel fMRI method, TR-external EPI with keyhole (TR-external EPIK), which can provide a nominal spatial resolution of 0.51 x 0.51 x 1.00 mm3 (0.26 mm3 voxel) with whole-brain coverage. TR-external EPIK enabled the identification of various resting-state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high-resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.

neuroscience↗

bioRxiv2021

Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation

Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes, however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal components analysis (PCA) and characterized the cluster-specific pupil-fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil-fMRI relationship.

neuroscience↗

bioRxiv2020

Cortical depth-dependent human fMRI of resting-state networks using EPIK

Recent laminar-fMRI studies have substantially improved understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks spread over the whole brain has been less studied due to technical limitations. Animal research strongly suggests that the supragranular layers of the cortex play a critical role in maintaining communication within the default mode network (DMN); however, whether this is true in this and other human cortical networks remains unclear. Here, we used EPIK, which offers unprecedented coverage at sub-millimeter resolution, to investigate cortical broad resting-state dynamics with depth specificity in healthy volunteers. Our results suggest that human DMN connectivity is primarily supported by intermediate and superficial layers of the cortex, and furthermore, the preferred cortical depth used for communication can vary from one network to another. This has major implications for the characterization of network-related diseases and should occupy future high-resolution studies. In addition, the laminar connectivity profile of some networks showed a tendency to change upon engagement in a motor task. In line with these connectivity changes, we observed that the amplitude of the low-frequency-fluctuations (ALFF), as well as the regional homogeneity (ReHo), exhibited a different laminar slope when subjects were either performing a task or were in a resting state (less variation among laminae, i.e., lower slope, during task performance compared to rest). The varied laminar profiles concerning network connectivity, ALFF, and ReHo, observed across two brain states (task vs. rest) suggest the potential diagnostic value of laminar fMRI in psychiatric diseases, e.g., to differentiate the cortical dynamics associated with disease stages linked, or not linked, to behavioral changes. The evaluation of laminar-fMRI across the brain encompasses computational challenges; nonetheless, it enables the investigation of a new dimension of the human neocortex, which may be key to understanding neurological disorders from a novel perspective.

neuroscience↗