Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivity

• URL: http://arxiv.org/abs/2101.01973v1
• Date: Wed, 6 Jan 2021 11:17:49 GMT
• Title: Weighted Ensemble-model and Network Analysis: A method to predict fluid intelligence via naturalistic functional connectivity
• Authors: Xiaobo Liu, Su Yang
• Abstract summary: We propose a new method namely Weighted Ensemble-model and Network Analysis. It combines the machine learning and graph theory for improved fluid intelligence prediction. Our proposed methods achieved best performance with 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient.
• Score: 2.66512000865131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objectives: Functional connectivity triggered by naturalistic stimulus (e.g., movies) and machine learning techniques provide a great insight in exploring the brain functions such as fluid intelligence. However, functional connectivity are considered to be multi-layered, while traditional machine learning based on individual models not only are limited in performance, but also fail to extract multi-dimensional and multi-layered information from brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method namely Weighted Ensemble-model and Network Analysis, which combines the machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into auto-encoder parallelly for feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression model were automatically stacked and fused with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed methods achieved best performance with 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient, outperformed other state-of-the-art methods. It is also worth noting that, the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method not only outperforming the state-of-the-art reports, but also able to effectively capturing the biological patterns from functional connectivity during naturalistic movies state for potential clinical explorations.

Related papers

• Benchmark on Drug Target Interaction Modeling from a Structure Perspective [48.60648369785105]
  Drug-target interaction prediction is crucial to drug discovery and design. Recent methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets. We conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms.
  arXiv  Detail & Related papers  (2024-07-04T16:56:59Z)
• DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks [4.041732967881764]
  Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series.
  arXiv  Detail & Related papers  (2024-05-19T23:35:06Z)
• Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data [2.193937336601403]
  We present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized, and total intelligence) using graph neural networks on rsfMRI derived connectivity matrices. Our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization.
  arXiv  Detail & Related papers  (2023-11-06T20:58:07Z)
• Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches [10.003764827561238]
  This study aims to identify the most favorable material model for human brain tissue. We apply artificial neural network and multiple regression methods to a generalization of widely accepted classic models.
  arXiv  Detail & Related papers  (2023-10-16T18:49:59Z)
• On the Trade-off Between Efficiency and Precision of Neural Abstraction [62.046646433536104]
  Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. We employ formal inductive synthesis procedures to generate neural abstractions that result in dynamical models with these semantics.
  arXiv  Detail & Related papers  (2023-07-28T13:22:32Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• 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)
• Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity [9.015698823470899]
  We present an approach to model functional brain connectivity across space and time. We use the Human Connectome Project dataset on sex classification and fluid intelligence prediction. Results show a prediction accuracy of 94.4% for sex, and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately.
  arXiv  Detail & Related papers  (2021-09-07T14:23:34Z)
• Going Beyond Linear RL: Sample Efficient Neural Function Approximation [76.57464214864756]
  We study function approximation with two-layer neural networks. Our results significantly improve upon what can be attained with linear (or eluder dimension) methods.
  arXiv  Detail & Related papers  (2021-07-14T03:03:56Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Efficient Inference of Flexible Interaction in Spiking-neuron Networks [41.83710212492543]
  We use the nonlinear Hawkes process to model excitatory or inhibitory interactions among neurons. We show our algorithm can estimate the temporal dynamics of interaction and reveal the interpretable functional connectivity underlying neural spike trains.
  arXiv  Detail & Related papers  (2020-06-23T09:10:30Z)

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.