Connectivity Analysis is Essential to Understand Neurological Disorders.

Area of Science:

• Neuroimaging
• Systems Neuroscience
• Cognitive Neuroscience

Background:

• Neurological and neuropsychiatric disorders significantly contribute to global morbidity.
• Understanding higher-order cognitive dysfunctions requires analyzing distributed brain networks, not just regional effects.
• Traditional regional analyses are insufficient for complex brain disorders.

Purpose of the Study:

• To discuss considerations for selecting appropriate brain connectivity analysis methods.
• To compare various connectivity techniques applicable to functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data.
• To highlight the utility of connectivity analysis in understanding neurological and neuropsychiatric diseases.

Main Methods:

• Comparison of connectivity methods: psychophysiological interactions, structural equation modeling, dynamic causal modeling, and independent components analysis.
• Application of these methods to functional and structural neuroimaging data (fMRI, PET).
• Illustration with examples from structural and neurodegenerative brain disorders.

Main Results:

• Connectivity analysis demonstrates higher sensitivity to disease presence/severity and treatment effects compared to voxel-wise activation analyses.
• Functional and structural connectivity methods reveal insights into brain network alterations in various disorders.
• The study illustrates the advantages of network-level analysis over purely regional approaches.

Conclusions:

• Brain connectivity analysis is crucial for a comprehensive understanding of neurological and neuropsychiatric diseases.
• Connectivity methods complement, rather than replace, other neuroimaging and behavioral approaches.
• Analyzing brain networks is essential for evaluating therapeutic mechanisms and disease progression.