Nicolás F. Lori

Histological validation of high-resolution DTI in human post mortem tissue

Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.

MRI diffusion tensor tracking of a new amygdalo‐fusiform and hippocampo‐fusiform pathway system in humans

To use MRI diffusion‐tensor tracking (DTT) to test for the presence of unknown neuronal fiber pathways interconnecting the mid‐fusiform cortex and anteromedial temporal lobe in humans. Such pathways are hypothesized to exist because these regions coactivate in functional MRI (fMRI) studies of emotion‐valued faces and words, suggesting a functional link that could be mediated by neuronal connections.

Visuo-auditory Multimodal Emotional Structure to Improve Human-Robot-Interaction

We propose an approach to analyze and synthesize a set of human facial and vocal expressions, and then use the classified expressions to decide the robot’s response in a human-robot-interaction. During a human-to-human conversation, a person senses the interlocutor’s face and voice, perceives her/his emotional expressions, and processes this information in order to decide which response to give. Moreover, observed emotions are taken into account and the response may be aggressive, funny (henceforth meaning humorous) or just neutral according to not only the observed emotions, but also the personality of the person. The purpose of our proposed structure is to endow robots with the capability to model human emotions, and thus several subproblems need to be solved: feature extraction, classification, decision and synthesis. In the proposed approach we integrate two classifiers for emotion recognition from audio and video, and then use a new method for fusion with the social behavior profile. To keep the person engaged in the interaction, after each iterance of analysis, the robot synthesizes human voice with both lips synchronization and facial expressions. The social behavior profile conducts the personality of the robot. The structure and work flow of the synthesis and decision are addressed, and the Bayesian networks are discussed. We also studied how to analyze and synthesize the emotion from the facial expression and vocal expression. A new probabilistic structure that enables a higher level of interaction between a human and a robot is proposed.

Obsessive–compulsive disorder as a visual processing impairment

OCD has been hypothesized to involve the failures in both cognitive and behavioral inhibitory processes. There is evidence that the hyperactivation of cortical-subcortical pathways may be involved in the failure of these inhibitory systems associated with OCD. Despite this consensus on the role of frontal-subcortical pathways in OCD, recent studies have been showing that brain regions other than the frontal-subcortical loops may be needed to understand the different cognitive and emotional deficits in OCD. Some studies have been finding evidence for decreased metabolic activity in areas such as left inferior parietal and parieto-occipital junction suggesting the possible existence of visual processing deficits. While there has been inconsistent data regarding visual processing in OCD, recent studies have been claiming that these patients have abnormal patterns of visual processing social rich stimuli, particularly emotional arousing stimuli. Thus, in this article, we hypothesize that the fronto-subcortical activation consistently found in OCD may be due to a deactivation of occipital/parietal regions associated with visual-perceptual processing of incoming social rich stimuli. Additionally, this dissociation may be more evident as the emotional intensity of the social stimulus increases.

Use of Shannon information in treatment of high resolution diffusion MRI

Diffusion MRI allows the obtaining of an approximation of the water displacements probability density function (PDF) and orientation distribution function (ODF). Examples of techniques used in obtaining these distributions being q-space imaging (QSI), and q-ball imaging (QBI), respectively. Shannon information quantifies the discriminative power of a symbol based on its probability. We quantified the information amount of a white matter fiber bundle being used to discriminate those fibers using specific diffusion MRI data treatment techniques. The equations developed are new and it is also described how they will help in future experimental calculations. An example of experimental ODF surfaces and ODF based white matter fiber tracking in living humans is also shown to highlight possible future advantages of Shannon information usage in describing crossing white matter fiber bundles.

Wormhole Approach to Control in Distributed Computing Has Direct Relation to Physics

The topic of Wormholes in distributed computing is about creating two different realms with different characteristics, the synchronous Wormholes and the asynchronous payload with the goal of using the wormholes to control the synchronism of the payload processes. We describe the characteristics of Wormholes in distributed computing, and relate them to issues in Physics, specifically, wormholes in general relativity and entanglement in quantum mechanics. The entanglement in quantum mechanics is about the existence of fixed relations between different physical systems as if they were still the same system. The entanglement is made evident by the occurrence of decoherence, which transform the multiple outcome possibilities of quantum systems into a single outcome “classical physics”-like objective reality. It is here presented the similarity between the decoherence process in quantum physics and the consensus problem in distributed computing. The approach to quantum mechanics used is quantum Darwinism, a Darwinian approach to decoherence where the environment controls the outcome of a measurement. It is here proposed that wormhole systems can be used to implement environment-based control of distributed computing systems.

Reducing computation time by Monte Carlo method : an application in determining axonal orientation distribution function

Diffusion MRI (dMRI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter (WM) anatomy using tractography, thus being an important component of health informatics. In clinical settings, the computation time is critical, and so finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI) dMRI data processing is extremely relevant. We analyse here a method for reducing the computation of the dMRI-based axonal orientation distribution function h by using a Monte Carlo sampling-based methods for voxel selection, and so obtained a reduction in required data sampling of about 20 %. In this work we show that the convergence to the correct value in this type of dMRI data-processing is linear and not exponential, implying that the Monte Carlo approach in this type of dMRI data processing improves its speed, but further improvements are needed.

Use of Shannon information to relate function and structure in the brain using diffusion spectrum imaging MRI

The purpose of this work is to develop improvements on the most modern forms of white matter fiber tracking in the human brain. This development requires the use of computer simulations to assess the limits of the fiber tracking technology, and then the results of that simulation are used to make a thorough assessment of the experimental results in living human brains. The used fiber tracking techniques are q-ball imaging and diffusion spectrum imaging.

Pipeline for spatial distortion correction in MRI

The recent advances in Magnetic Resonance Imaging gradient, regarding strength and computation speed, led to the development of Echo-Planar Imaging pulse-sequences with faster acquisition times. This kind of sequence is used in functional MRI and diffusion-weighted Magnetic Resonance Imaging and it presents more distortions than slower sequences. This work aims to compare different spatial distortion correction methods for Echo-Planar Imaging sequences with a new proposed pipeline which consists in performing a Field Map correction after a registration process.