Insights into brain network dynamics across ages using group-ICA functional parcellation

Franklin Open Pub Date : 2024-12-01 DOI:10.1016/j.fraope.2024.100178

Lorena Malinalli López-Medina , Omar Paredes , Sorpresa Lora-Castro , Rebeca Romo-Vázquez , J. Alejandro Morales , Hugo Vélez-Pérez

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Abstract

Aging is a natural and inevitable process in human life that involves changes in the brain. The brain becomes structurally, functionally, and interconnected differently. The evolution of brain activity and its anatomy follows separate paths, causing them to not always align. Independent Component Analysis (ICA), a Blind Source Separation (BSS) method, enables brain parcellation considering an individual's functional patterns. This is advantageous when studying brain function at different stages of life, allowing for a more comprehensive and flexible approach that accounts for age-related differences. This study aims to investigate the relationship between age and functional networks, exploring the differences in the functional parcellations of two different age groups and their networks. We propose a method utilizing group-ICA that identifies functional connectivity patterns to estimate coherent networks. We found variations in the distribution of the age-related networks and the brain portions they covered; some networks in old adults encompassed smaller areas in comparison to the networks in young adults.

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洞察大脑网络动态跨年龄使用群体ica功能包裹

衰老是人类生命中一个自然而不可避免的过程，涉及大脑的变化。大脑在结构、功能和连接上都变得不同。大脑活动的进化及其解剖学遵循不同的路径，导致它们并不总是一致的。独立分量分析（ICA）是一种盲源分离（BSS）方法，可以根据个体的功能模式对大脑进行分割。这在研究生命不同阶段的大脑功能时是有利的，允许更全面和灵活的方法来解释与年龄相关的差异。本研究旨在探讨年龄与功能网络之间的关系，探讨两个不同年龄群体的功能分布及其网络的差异。我们提出了一种利用群- ica识别功能连接模式来估计连贯网络的方法。我们发现与年龄相关的网络分布和它们覆盖的大脑部分存在差异；与年轻人的网络相比，老年人的一些网络包含的区域更小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Franklin Open

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