37,001 research outputs found
Reservoir Memory Machines as Neural Computers
Differentiable neural computers extend artificial neural networks with an
explicit memory without interference, thus enabling the model to perform
classic computation tasks such as graph traversal. However, such models are
difficult to train, requiring long training times and large datasets. In this
work, we achieve some of the computational capabilities of differentiable
neural computers with a model that can be trained very efficiently, namely an
echo state network with an explicit memory without interference. This extension
enables echo state networks to recognize all regular languages, including those
that contractive echo state networks provably can not recognize. Further, we
demonstrate experimentally that our model performs comparably to its
fully-trained deep version on several typical benchmark tasks for
differentiable neural computers.Comment: In print at the special issue 'New Frontiers in Extremely Efficient
Reservoir Computing' of IEEE TNNL
Leave Graphs Alone: Addressing Over-Squashing without Rewiring
Recent works have investigated the role of graph bottlenecks in preventing
long-range information propagation in message-passing graph neural networks,
causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring
mechanisms have been proposed as preprocessing steps. Graph Echo State Networks
(GESNs) are a reservoir computing model for graphs, where node embeddings are
recursively computed by an untrained message-passing function. In this paper,
we show that GESNs can achieve a significantly better accuracy on six
heterophilic node classification tasks without altering the graph connectivity,
thus suggesting a different route for addressing the over-squashing problem.Comment: Extended Abstract. Presented at the First Learning on Graphs
Conference (LoG 2022), Virtual Event, December 9-12, 202
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Ejection fraction (EF) is a key indicator of cardiac function, allowing
identification of patients prone to heart dysfunctions such as heart failure.
EF is estimated from cardiac ultrasound videos known as echocardiograms (echo)
by manually tracing the left ventricle and estimating its volume on certain
frames. These estimations exhibit high inter-observer variability due to the
manual process and varying video quality. Such sources of inaccuracy and the
need for rapid assessment necessitate reliable and explainable machine learning
techniques. In this work, we introduce EchoGNN, a model based on graph neural
networks (GNNs) to estimate EF from echo videos. Our model first infers a
latent echo-graph from the frames of one or multiple echo cine series. It then
estimates weights over nodes and edges of this graph, indicating the importance
of individual frames that aid EF estimation. A GNN regressor uses this weighted
graph to predict EF. We show, qualitatively and quantitatively, that the
learned graph weights provide explainability through identification of critical
frames for EF estimation, which can be used to determine when human
intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN
achieves EF prediction performance that is on par with state of the art and
provides explainability, which is crucial given the high inter-observer
variability inherent in this task.Comment: Published in MICCAI 202
Disrupted functional brain network organization in patients with obstructive sleep apnea.
IntroductionObstructive sleep apnea (OSA) subjects show impaired autonomic, affective, executive, sensorimotor, and cognitive functions. Brain injury in OSA subjects appears in multiple sites regulating these functions, but the integrity of functional networks within the regulatory sites remains unclear. Our aim was to examine the functional interactions and the complex network organization of these interactions across the whole brain in OSA, using regional functional connectivity (FC) and brain network topological properties.MethodsWe collected resting-state functional magnetic resonance imaging (MRI) data, using a 3.0-Tesla MRI scanner, from 69 newly diagnosed, treatment-naïve, moderate-to-severe OSA (age, 48.3 ± 9.2 years; body mass index, 31 ± 6.2 kg/m(2); apnea-hypopnea index (AHI), 35.6 ± 23.3 events/h) and 82 control subjects (47.6 ± 9.1 years; body mass index, 25.1 ± 3.5 kg/m(2)). Data were analyzed to examine FC in OSA over controls as interregional correlations and brain network topological properties.ResultsObstructive sleep apnea subjects showed significantly altered FC in the cerebellar, frontal, parietal, temporal, occipital, limbic, and basal ganglia regions (FDR, P < 0.05). Entire functional brain networks in OSA subjects showed significantly less efficient integration, and their regional topological properties of functional integration and specialization characteristics also showed declined trends in areas showing altered FC, an outcome which would interfere with brain network organization (P < 0.05; 10,000 permutations). Brain sites with abnormal topological properties in OSA showed significant relationships with AHI scores.ConclusionsOur findings suggest that the dysfunction extends to resting conditions, and the altered FC and impaired network organization may underlie the impaired responses in autonomic, cognitive, and sensorimotor functions. The outcomes likely result from the prominent structural changes in both axons and nuclear structures, which occur in the condition
Recommended from our members
Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.
BACKGROUND: The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. METHOD: Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. RESULTS: Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. CONCLUSIONS: Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.The study was funded by the Wellcome Trust Fellowship grant for V.V. (093705/Z/10/Z) and the Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre. We thank Dr Ameera Patel for providing the H-O470 brain parcellation atlas in the present study.This is the final version of the article. It first appeared from Cambridge University Press via https://doi.org/10.1017/S003329171600264
- …