Anatomically compliant modes of variations: New tools for brain connectivity

Area of Science:

• Neuroscience
• Computational Biology
• Medical Imaging

Background:

• Analyzing brain connectivity is challenging due to anatomical complexity and high data dimensionality.
• The interplay between functional and anatomical brain connections is crucial but often overlooked.

Purpose of the Study:

• To develop and apply Smooth Functional Principal Component Analysis (sFPCA) for dimensionality reduction and variability exploration in brain connectivity.
• To investigate differences in functional connectivity patterns between healthy controls and individuals with schizophrenia.

Main Methods:

• Utilized fMRI data from healthy controls and schizophrenia patients during rest and a task-switching paradigm.
• Applied sFPCA to identify common modes of variation in functional connectivity maps.
• Compared the expression of these modes between the two subject groups.

Main Results:

• Identified significant differences (p < 0.001) in principal components between healthy and pathological subjects in both rest and task conditions.
• The second and third principal components during rest revealed altered Default Mode and Executive Network balance in schizophrenia.

Conclusions:

• sFPCA effectively reduces dimensionality and explores variability in complex brain connectivity data.
• This approach highlights distinct functional connectivity differences relevant to schizophrenia, particularly network imbalances.