An enriched granger causal model allowing variable static anatomical constraints

• Published in: NeuroImage clinical 2019-01, Vol.21, p.101592-101592, Article 101592
• Main Authors: Bi, Kun, Luo, Guoping, Tian, Shui, Zhang, Siqi, Liu, Xiaoxue, Wang, Qiang, Lu, Qing, Yao, Zhijian
• Format: Article
• Language: English
• Subjects: Adult; Anatomical priors; Bayes Theorem; Brain - pathology; Brain - physiopathology; Brain Mapping - methods; Depression; Depression - physiopathology; Depressive Disorder - physiopathology; Diffusion Tensor Imaging - methods; DTI; Effective connectivity; Emotions - physiology; Enriched granger causal model; Female; Humans; Magnetoencephalography - methods; Male; MEG; Middle Aged; Young Adult
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2018.11.002

The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition. •An enriched granger causal model (GCM) with anatomical priors is proposed•The anatomical connectivity is converted to prior variance via transformation model•The anatomical priors are integrated into GCM by variational bayesian model•The model reflects special anatomical constraint on function in the abrupt variation stage•The anatomical constraint for effective connectivity might help better understand depression