Fast-Marching Tractography for Connection Matrix (Fast-TraC)

Although high angular resolution diffusion MRI techniques are able to solve multiple intra-voxel fiber orientations, the usual streamline Diffusion Spectrum Imaging (DSI) tractography algorithms present some limitations in their ability to map complex fiber-crossings in the brain white matter because they select locally only the most linear trajectories. In this work, we present a fast marching tractography algorithm for DSI, called Fast-TraC, which 1) is able to efficiently address this issue, 2) creates fiber trajectories between 1000 small cortical ROIs covering the entire brain and 3) builds a whole brain connection matrix. We also see selected tracts that are accurately reconstructed

Entrainment of Voluntary Movement to Undetected Auditory Regularities

In physics “entrainment” refers to the synchronization of two coupled oscillators with similar fundamental frequencies. In behavioral science, entrainment refers to the tendency of humans to synchronize their movements with rhythmic stimuli. Here, we asked whether human subjects performing a tapping task would entrain their tapping to an undetected auditory rhythm surreptitiously introduced in the guise of ambient background noise in the room. Subjects performed two different tasks, one in which they tapped their finger at a steady rate of their own choosing and one in which they performed a single abrupt finger tap on each trial after a delay of their own choosing. In both cases we found that subjects tended to tap in phase with the inducing modulation, with some variability in the preferred phase across subjects, consistent with prior research. In the repetitive tapping task, if the frequency of the inducing stimulus was far from the subject’s own self-paced frequency, then entrainment was abolished, consistent with the properties of entrainment in physics. Thus, undetected ambient noise can influence self-generated movements. This suggests that uncued decisions to act are never completely endogenous, but are subject to subtle unnoticed influences from the sensory environment

Endothelium and subendothelial matrix mechanics modulate cancer cell transendothelial migration

Cancer cell extravasation, a key step in the metastatic cascade, involves cancer cell arrest on the endothelium, transendothelial migration (TEM), followed by the invasion into the subendothelial extracellular matrix (ECM) of distant tissues. While cancer research has mostly focused on the biomechanical interactions between tumor cells (TCs) and ECM, particularly at the primary tumor site, very little is known about the mechanical properties of endothelial cells and the subendothelial ECM and how they contribute to the extravasation process. Here, an integrated experimental and theoretical framework is developed to investigate the mechanical crosstalk between TCs, endothelium and subendothelial ECM during in vitro cancer cell extravasation. It is found that cancer cell actin-rich protrusions generate complex push–pull forces to initiate and drive TEM, while transmigration success also relies on the forces generated by the endothelium. Consequently, mechanical properties of the subendothelial ECM and endothelial actomyosin contractility that mediate the endothelial forces also impact the endothelium's resistance to cancer cell transmigration. These results indicate that mechanical features of distant tissues, including force interactions between the endothelium and the subendothelial ECM, are key determinants of metastatic organotropism.Peer ReviewedPostprint (published version

A Method to Study Alterations in Networks of Structural Connectivity

The global structural connectivity of the brain can be explored in vivo with a connectivity matrix derived from diffusion MRI tractography [1]. In such a matrix, M, every index i or j represents a small region of interest (ROI) at the white-gray matter (WGM) interface and every entry M(i,j) provides a measure of connectivity derived from tractography. Once the matrix computed, it is easy to obtain connectional information betwee

Thalamic Nuclei Clustering on High Angular Resolution Diffusion Images.

Thalamic nuclei can be distinguished by their characteristic fiber orientations, which influence the diffusion. Fiber orientations are relatively aligned within a nucleus due to the fact that the cerebrocortical striations within a nucleus all target the same region of cortex. The number of thalamic nuclei reported with histological methods varies with the method employed, although most cyto/myeloarchitec stains identify 14 major nuclei. We present a new approach for thalamic nuclei segmentation on High Angular Diffusion Resolution Images (HARDI), performed with a constrained k-means clustering. As described by John D.Carew[1], it is possible to classify HARDI data based on the shape of the diffusion, thanks to the complex information coming from them. Mette R. Wiegell [2] proposed a thalamic nuclei clustering with k- means on diffusion tensor images, using a combination of a voxel distance and a diffusion tensor distance. In the same way, we use the k-mean algorithm with a weighted sum of two distances to cluster the thalamic nuclei on HARDI data

Intrahemispheric cortico-cortical connections of the human auditory cortex.

The human auditory cortex comprises the supratemporal plane and large parts of the temporal and parietal convexities. We have investigated the relevant intrahemispheric cortico-cortical connections using in vivo DSI tractography combined with landmark-based registration, automatic cortical parcellation and whole-brain structural connection matrices in 20 right-handed male subjects. On the supratemporal plane, the pattern of connectivity was related to the architectonically defined early-stage auditory areas. It revealed a three-tier architecture characterized by a cascade of connections from the primary auditory cortex to six adjacent non-primary areas and from there to the superior temporal gyrus. Graph theory-driven analysis confirmed the cascade-like connectivity pattern and demonstrated a strong degree of segregation and hierarchy within early-stage auditory areas. Putative higher-order areas on the temporal and parietal convexities had more widely spread local connectivity and long-range connections with the prefrontal cortex; analysis of optimal community structure revealed five distinct modules in each hemisphere. The pattern of temporo-parieto-frontal connectivity was partially asymmetrical. In conclusion, the human early-stage auditory cortical connectivity, as revealed by in vivo DSI tractography, has strong similarities with that of non-human primates. The modular architecture and hemispheric asymmetry in higher-order regions is compatible with segregated processing streams and lateralization of cognitive functions

Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals

While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice and poorly trusted by clinicians due to insufficient interpretability of neural network models. We have tackled this issue by developing interpretable DL models in the context of online detection of epileptic seizure, based on EEG signal. This has conditioned the preparation of the input signals, the network architecture, and the post-processing of the output in line with the domain knowledge. Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method. Results show that the kernel size in the first layer determines the interpretability of the extracted features and the sensitivity of the trained models, even though the final performance is very similar after post-processing. Also, we found that amplitude is the main feature leading to an ictal prediction, suggesting that a larger patient population would be required to learn more complex frequency patterns. Still, our methodology was successfully able to generalize patient inter-variability for the majority of the studied population with a classification F1-score of 0.873 and detecting 90% of the seizures.Comment: 28 pages, 11 figures, 12 table