148,871 research outputs found
Adaptive Smoothing in fMRI Data Processing Neural Networks
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data
processing pipelines to accurately determine brain activity; among them, the
crucial step of spatial smoothing. These pipelines are commonly suboptimal,
given the local optimisation strategy they use, treating each step in
isolation. With the advent of new tools for deep learning, recent work has
proposed to turn these pipelines into end-to-end learning networks. This change
of paradigm offers new avenues to improvement as it allows for a global
optimisation. The current work aims at benefitting from this paradigm shift by
defining a smoothing step as a layer in these networks able to adaptively
modulate the degree of smoothing required by each brain volume to better
accomplish a given data analysis task. The viability is evaluated on real fMRI
data where subjects did alternate between left and right finger tapping tasks.Comment: 4 pages, 3 figures, 1 table, IEEE 2017 International Workshop on
Pattern Recognition in Neuroimaging (PRNI
Pattern Classification of Large-Scale Functional Brain Networks: Identification of Informative Neuroimaging Markers for Epilepsy
The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Recent applications of pattern recognition techniques on brain connectome
classification using functional connectivity (FC) neglect the non-Euclidean
topology and causal dynamics of brain connectivity across time. In this paper,
a deep probabilistic spatiotemporal framework developed based on variational
Bayes (DSVB) is proposed to learn time-varying topological structures in
dynamic brain FC networks for autism spectrum disorder (ASD) identification.
The proposed framework incorporates a spatial-aware recurrent neural network to
capture rich spatiotemporal patterns across dynamic FC networks, followed by a
fully-connected neural network to exploit these learned patterns for
subject-level classification. To overcome model overfitting on limited training
datasets, an adversarial training strategy is introduced to learn graph
embedding models that generalize well to unseen brain networks. Evaluation on
the ABIDE resting-state functional magnetic resonance imaging dataset shows
that our proposed framework significantly outperformed state-of-the-art methods
in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal
apparent group difference between ASD and healthy controls in network profiles
and switching dynamics of brain states
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the application at hand. In this work, we propose a novel metric
learning method to evaluate distance between graphs that leverages the power of
convolutional neural networks, while exploiting concepts from spectral graph
theory to allow these operations on irregular graphs. We demonstrate the
potential of our method in the field of connectomics, where neuronal pathways
or functional connections between brain regions are commonly modelled as
graphs. In this problem, the definition of an appropriate graph similarity
function is critical to unveil patterns of disruptions associated with certain
brain disorders. Experimental results on the ABIDE dataset show that our method
can learn a graph similarity metric tailored for a clinical application,
improving the performance of a simple k-nn classifier by 11.9% compared to a
traditional distance metric.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Identification and Differential Vulnerability of a Neural Network in Sleep Deprivation
The study aimed to identify task-related brain activation networks whose change in expression exhibits subject differences as a function of differential susceptibility to sleep deprivation. Brain activity during a non-verbal recognition memory task was investigated in an event-related functional MRI paradigm both prior to and after 48 h of sleep deprivation. Nineteen healthy subjects participated. Regional covariance analysis was applied to data. An activation network pattern was identified whose expression decreased from pre- to post-sleep deprivation in 15 out 19 subjects (P < 0.05). Differential decrease in expression correlated with worsening performance in recognition accuracy (P < 0.05). Sites of de-activation were found in the posterior cerebellum, right fusiform gyrus and precuneus, and left lingual and inferior temporal gyri; increased activation was found in the bilateral insula, claustrum and right putamen. A network whose expression decreased after sleep deprivation and correlated with memory performance was identified. We conclude that this activation network plays a role in cognitive function during sleep deprivation
Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons
In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Vision research has been shaped by the seminal insight that we can understand
the higher-tier visual cortex from the perspective of multiple functional
pathways with different goals. In this paper, we try to give a computational
account of the functional organization of this system by reasoning from the
perspective of multi-task deep neural networks. Machine learning has shown that
tasks become easier to solve when they are decomposed into subtasks with their
own cost function. We hypothesize that the visual system optimizes multiple
cost functions of unrelated tasks and this causes the emergence of a ventral
pathway dedicated to vision for perception, and a dorsal pathway dedicated to
vision for action. To evaluate the functional organization in multi-task deep
neural networks, we propose a method that measures the contribution of a unit
towards each task, applying it to two networks that have been trained on either
two related or two unrelated tasks, using an identical stimulus set. Results
show that the network trained on the unrelated tasks shows a decreasing degree
of feature representation sharing towards higher-tier layers while the network
trained on related tasks uniformly shows high degree of sharing. We conjecture
that the method we propose can be used to analyze the anatomical and functional
organization of the visual system and beyond. We predict that the degree to
which tasks are related is a good descriptor of the degree to which they share
downstream cortical-units.Comment: 16 pages, 5 figure
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