End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence

• URL: http://arxiv.org/abs/2407.12553v1
• Date: Wed, 17 Jul 2024 13:34:05 GMT
• Title: End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence
• Authors: Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi,
• Abstract summary: We propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data. This representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools.
• Score: 42.52549987351643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data derived from magnetic resonance imaging. Ultimately, this representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools. Stroke is one of the leading causes of mortality and morbidity worldwide, and it demands precise diagnostic tools for timely intervention and improved patient outcomes. Neuroimaging data, with their rich structural and functional information, provide a fertile ground for biomarker discovery. However, the complexity and variability of information flow in the brain requires advanced analysis, especially if we consider the case of disrupted networks as those given by the brain connectome of stroke patients. To address the needs given by this complex scenario we proposed an end-to-end pipeline. This pipeline begins with reservoir computing causality, to define effective connectivity of the brain. This allows directed graph network representations which have not been fully investigated so far by graph convolutional network classifiers. Indeed, the pipeline subsequently incorporates a classification module to categorize the effective connectivity (directed graphs) of brain networks of patients versus matched healthy control. The classification led to an area under the curve of 0.69 with the given heterogeneous dataset. Thanks to explainable tools, an interpretation of disrupted networks across the brain networks was possible. This elucidates the effective connectivity biomarker's contribution to stroke classification, fostering insights into disease mechanisms and treatment responses.

Related papers

• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Graph Neural Operators for Classification of Spatial Transcriptomics Data [1.408706290287121]
  We propose a study incorporating various graph neural network approaches to validate the efficacy of applying neural operators towards prediction of brain regions in mouse brain tissue samples. We were able to achieve an F1 score of nearly 72% for the graph neural operator approach which outperformed all baseline and other graph network approaches.
  arXiv  Detail & Related papers  (2023-02-01T18:32:06Z)
• Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition [0.0]
  Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. We explore the impact of synaptic and membrane leakages in spiking neurons.
  arXiv  Detail & Related papers  (2022-11-14T21:34:02Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling [9.85779770378859]
  Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling.
  arXiv  Detail & Related papers  (2022-05-22T06:11:21Z)
• Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications [52.65389473899139]
  Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Recent works have shown that considering relationships between input data samples have a positive regularizing effect for the downstream task. We propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications.
  arXiv  Detail & Related papers  (2022-04-01T10:01:37Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling [0.0]
  We propose a dynamic adaptivetemporal graph convolution (DASTGCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module. We evaluate our pipeline on the UKBiobank for age and gender classification tasks from resting-state functional scans.
  arXiv  Detail & Related papers  (2021-09-26T07:19:47Z)
• AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction [20.19380805655623]
  We propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution. We also propose a novel graph convolution network architecture using multi-layer aggregation mechanism.
  arXiv  Detail & Related papers  (2021-06-16T12:13:23Z)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.