Variational dimensions of cingulate cortex functional connectivity and implications in neuropsychiatric disorders

Area of Science:

• Neuroscience
• Computational Psychiatry
• Network Science

Background:

• Brain functional connectivity exhibits significant inter-individual variability, complicating the identification of abnormalities in neuropsychiatric disorders.
• Understanding these variations is crucial for diagnosing and treating conditions like autism and schizophrenia.

Purpose of the Study:

• To develop and validate a new principal component analysis (PCA) approach for studying functional connectivity variations.
• To characterize normal variations in the cingulate cortices and identify alterations in autistic and schizophrenic subjects.

Main Methods:

• Applied a novel PCA method to functional magnetic resonance imaging (fMRI) data from healthy, autistic, and schizophrenic subjects.
• Analyzed intersubject variability of functional connectivity in the anterior and posterior cingulate cortices, key hubs of the salience and default mode networks.
• Utilized data from large neuroimaging databases (1000 Functional Connectomes Project, COBRE, ABIDE 1).

Main Results:

• Characterized normal variations of cingulate cortices along principal component analysis (PCA) dimensions.
• Demonstrated that functional connectivity variations are constrained by interactions with sensorimotor, associative, and limbic networks.
• Identified diffuse and subtle network changes in schizophrenic and autistic subjects along the same PCA dimensions, suggesting behavioral relevance.
• Showed that dynamic functional connectivity fluctuates along these principal components of connectivity variation.

Conclusions:

• The proposed PCA approach effectively addresses intrinsic variations in human brain network connectivity.
• This method can identify subtle network alterations in neuropsychiatric disorders, offering potential for improved diagnostic and therapeutic strategies.
• Findings highlight the significant behavioral implications of identified variational dimensions in brain networks.