Co-evolution of Functional Brain Network at Multiple Scales during Early
Infancy
- URL: http://arxiv.org/abs/2009.06899v1
- Date: Tue, 15 Sep 2020 07:21:04 GMT
- Title: Co-evolution of Functional Brain Network at Multiple Scales during Early
Infancy
- Authors: Xuyun Wen, Liming Hsu, Weili Lin, Han Zhang, Dinggang Shen
- Abstract summary: This paper leveraged a longitudinal infant resting-state functional magnetic resonance imaging dataset from birth to 2 years of age.
By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales.
- Score: 52.4179778122852
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The human brains are organized into hierarchically modular networks
facilitating efficient and stable information processing and supporting diverse
cognitive processes during the course of development. While the remarkable
reconfiguration of functional brain network has been firmly established in
early life, all these studies investigated the network development from a
"single-scale" perspective, which ignore the richness engendered by its
hierarchical nature. To fill this gap, this paper leveraged a longitudinal
infant resting-state functional magnetic resonance imaging dataset from birth
to 2 years of age, and proposed an advanced methodological framework to
delineate the multi-scale reconfiguration of functional brain network during
early development. Our proposed framework is consist of two parts. The first
part developed a novel two-step multi-scale module detection method that could
uncover efficient and consistent modular structure for longitudinal dataset
from multiple scales in a completely data-driven manner. The second part
designed a systematic approach that employed the linear mixed-effect model to
four global and nodal module-related metrics to delineate scale-specific
age-related changes of network organization. By applying our proposed
methodological framework on the collected longitudinal infant dataset, we
provided the first evidence that, in the first 2 years of life, the brain
functional network is co-evolved at different scales, where each scale displays
the unique reconfiguration pattern in terms of modular organization.
Related papers
- Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging [8.610253537046692]
We present the Multitask Adversarial Variational Autoencoder, a custom deep learning framework designed to improve brain age predictions.
The model separates latent variables into generic and unique codes, isolating shared and modality-specific features.
By integrating multitask learning with sex classification as an additional task, the model captures sex-specific aging patterns.
arXiv Detail & Related papers (2024-11-15T10:50:36Z) - Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - MindBridge: A Cross-Subject Brain Decoding Framework [60.58552697067837]
Brain decoding aims to reconstruct stimuli from acquired brain signals.
Currently, brain decoding is confined to a per-subject-per-model paradigm.
We present MindBridge, that achieves cross-subject brain decoding by employing only one model.
arXiv Detail & Related papers (2024-04-11T15:46:42Z) - Functional2Structural: Cross-Modality Brain Networks Representation
Learning [55.24969686433101]
Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases.
We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder.
We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
arXiv Detail & Related papers (2022-05-06T03:45:36Z) - Multi-Scale Semantics-Guided Neural Networks for Efficient
Skeleton-Based Human Action Recognition [140.18376685167857]
A simple yet effective multi-scale semantics-guided neural network is proposed for skeleton-based action recognition.
MS-SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
arXiv Detail & Related papers (2021-11-07T03:50:50Z) - Improving Coherence and Consistency in Neural Sequence Models with
Dual-System, Neuro-Symbolic Reasoning [49.6928533575956]
We use neural inference to mediate between the neural System 1 and the logical System 2.
Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.
arXiv Detail & Related papers (2021-07-06T17:59:49Z) - Dynamics of specialization in neural modules under resource constraints [2.9465623430708905]
We use artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization.
We conclude that a static notion of specialization, based on structural modularity, is likely too simple a framework for understanding intelligence in situations of real-world complexity.
arXiv Detail & Related papers (2021-06-04T17:39:36Z) - Convolutional Neural Networks for cytoarchitectonic brain mapping at
large scale [0.33727511459109777]
We present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains.
It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between.
The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts.
arXiv Detail & Related papers (2020-11-25T16:25:13Z) - Self-organization of multi-layer spiking neural networks [4.859525864236446]
A key mechanism that enables the formation of complex architecture in the developing brain is the emergence of traveling-temporal waves of neuronal activity.
We propose a modular tool-kit in the form of a dynamical system that can be seamlessly stacked to assemble multi-layer neural networks.
Our framework leads to the self-organization of a wide variety of architectures, ranging from multi-layer perceptrons to autoencoders.
arXiv Detail & Related papers (2020-06-12T01:44:48Z)
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.