Detecting language network alterations in mild cognitive impairment using task-based fMRI and resting-state fMRI: A...

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Medical Imaging Analysis

Background:

• Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia.
• Understanding the neural underpinnings of MCI is crucial for early diagnosis and intervention.
• Functional magnetic resonance imaging (fMRI) and independent component analysis (ICA) are advanced neuroimaging techniques.

Purpose of the Study:

• To investigate functional brain changes in individuals with MCI.
• To compare brain activity during resting-state and task-based (word generation) fMRI in MCI patients and controls.
• To utilize ICA to identify and analyze specific brain networks associated with MCI.

Main Methods:

• 17 patients with MCI and 17 healthy controls underwent resting-state and task-based fMRI.
• Independent Component Analysis (ICA) was employed to identify independent components (ICs) and their corresponding brain networks.
• The Dice Coefficient was used to assess the relevance of identified ICs to predefined networks.

Main Results:

• ICA identified networks related to language, visual processing, and the dorsal attention network (DAN).
• Increased activation in the language network was observed in MCI patients, particularly in the bilateral lingual gyrus.
• Decreased activation was noted in the DAN and visual networks during the word generation task in MCI patients.

Conclusions:

• Task-based fMRI combined with ICA provides a sophisticated and reliable method for assessing cognitive impairments, especially in language processing.
• The findings support the neural mechanisms underlying cognitive deficits observed in mild cognitive impairment.
• This approach aids in understanding the brain's functional alterations in MCI.