scholarly journals Predicting cognitive scores with graph neural networks through sample selection learning

2021 ◽  
Author(s):  
Martin Hanik ◽  
Mehmet Arif Demirtaş ◽  
Mohammed Amine Gharsallaoui ◽  
Islem Rekik
Keyword(s):  
Neural Networks ◽  
Brain Connectivity ◽  
Sample Selection ◽  
Target Prediction ◽  
Autism Spectrum ◽  
Brain Connectome ◽  
Training Samples ◽  
Health And Disease ◽  
Iq Scores ◽  
Graph Neural Networks

AbstractAnalyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.

scholarly journals Predicting Cognitive Scores With Graph Neural Networks Through Sample Selection Learning

2021 ◽  
Author(s):  
Martin Hanik ◽  
Mehmet Arif Demirtaş ◽  
Mohammed Amine Gharsallaoui ◽  
Islem Rekik
Keyword(s):  
Neural Networks ◽  
Brain Connectivity ◽  
Sample Selection ◽  
Target Prediction ◽  
Autism Spectrum ◽  
Selection Method ◽  
Brain Connectome ◽  
Training Samples ◽  
Iq Scores ◽  
Graph Neural Networks

Abstract Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a \emph{learning-based sample selection} method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.

scholarly journals Graph Convolutional Networks by Architecture Search for PolSAR Image Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1404
Author(s):  
Hongying Liu ◽  
Derong Xu ◽  
Tianwen Zhu ◽  
Fanhua Shang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spatial Relations ◽  
Classification Problem ◽  
Search Method ◽  
Sample Distribution ◽  
Convolutional Networks ◽  
Training Samples ◽  
Graph Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with a large number of training samples. However, the performance of most convolutional neural networks (CNNs) degrades dramatically when only a few labeled training samples are available. As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples. As the number of layers grows in the network, the parameters dramatically increase. It is challenging to determine an optimal architecture manually. In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images. We construct a novel graph whose nodes combines both the physical features and spatial relations between pixels or samples to represent the image. Then we build a new searching space whose components are empirically selected from some graph neural networks for architecture search and develop the differentiable architecture search method to construction our ASGCN. Moreover, to address the training of large-scale images, we present a new weighted mini-batch algorithm to reduce the computing memory consumption and ensure the balance of sample distribution, and also analyze and compare with other similar training strategies. Experiments on several real-world PolSAR datasets show that our method has improved the overall accuracy as much as 3.76% than state-of-the-art methods.

scholarly journals Learning Cortical Parcellations Using Graph Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Kristian M. Eschenburg ◽  
Thomas J. Grabowski ◽  
David R. Haynor
Keyword(s):  
Neural Networks ◽  
Resting State ◽  
Network Architecture ◽  
Brain Connectivity ◽  
Model Performance ◽  
Disease Diagnosis ◽  
General Purpose ◽  
Tissue Segmentation ◽  
Graph Neural Networks ◽  
Resting State Connectivity

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

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