ReviewDepression, Neuroimaging and Connectomics: A Selective Overview
Section snippets
Measurement of Brain Connectivity
There are three classes of brain connectivity: functional, effective, and structural connectivity (Table S1 in Supplement 1) (26, 27, 28, 29, 30, 31, 32, 57, 58, 59). Structural connectivity includes gray matter (GM) structural covariance and white matter (WM) anatomical connectivity (28, 29, 59). Effective connectivity is not commonly used for graph-based brain network analysis due to its complexity and will not be detailed here. Based on the brain connectivity information extracted from
Disrupted Functional Connectomics in Depression
Several resting-state functional MRI (R-fMRI) studies reported aberrant topological organization of whole-brain functional networks in adult depressive patients involving the global, modular, and nodal properties (Table 1). In the first such study, Zhang et al. (38) measured partial correlation coefficients of R-fMRI signals between 90 cortical and subcortical regions in 30 first-episode, drug-naive depressive patients. They observed that the depressed group showed altered global properties
Disrupted Structural Connectomics in Depression
Two structural MRI studies by Singh et al. (44) and Ajilore et al. (45) investigated topological organization of depressive GM networks (with 90 or 82 nodes) (Table 1). Singh et al. (44) reported that the depressed patients had smaller clustering coefficients in their GM networks, which suggests a less specialized or segregated topological organization. Higher regional connectivity was primarily found in the components of the prefrontal-limbic circuit (amygdala and ventral mPFC) (Figure 4C).
Genetic Effects on Depressive Brain Networks
Depression is a highly heritable disorder with a reported heritability of 31% to 42% (89). Specifically, several susceptibility genes are relevant to depression, including apolipoprotein E, dopamine receptor D4, dopamine transporter, and serotonin transporter (90). Neuroimaging studies have demonstrated that these genetic variations are associated with different brain connectivity patterns. In a large sample of healthy human subjects, Pezawas et al. (91) reported that compared with individuals
Developing Diagnostic Biomarkers Using Connectome-based Metrics
Early diagnosis of depression is important, as treatment is most effective in the early stages. However, depression is traditionally diagnosed mainly focused on clinical interviews and patient ratings and underrecognized and often misdiagnosed (114). Recent advances in machine learning and neuroimaging techniques provide potential for the clinical diagnosis of this disorder. Of these, multivariate pattern analysis based on support vector machine is one of the most popular machine learning
Understanding Treatment Mechanisms and Identifying Potential Therapeutic Targets from a Connectome Perspective
Numerous antidepressant treatments are currently available in clinical practice, including pharmacologic and psychotherapeutic interventions and brain stimulation therapies (e.g., electroconvulsive therapy, repetitive transcranial magnetic stimulation, and deep brain stimulation). Although the biological mechanisms of action underlying their therapeutic effects remain incompletely understood, one possible explanation is that these treatment methods selectively modulate the activities of
Addressing Sample Heterogeneity
As described above, there have been mixed results showing decreased, increased, and unchanged global and regional properties in depressive brain networks. The heterogeneity of the patient samples may be a major reason for these discrepancies (Table 1). Researchers need to select more homogeneous samples with detailed consideration of demographic variables (e.g., age, gender, medication, socioeconomic status, childhood experiences, and disease duration) and symptom dimensions.
Relationship between Structural and Functional Connectivity
Depression is
Acknowledgments and Disclosures
This study was supported by the National Key Basic Research Program of China (973 Project, No. 2014CB846102), the National Natural Science Foundation (Grant Nos. 81030028, 31221003, 81030027, 81227002, and 81220108013), the National Science Fund for Distinguished Young Scholars (No. 81225012), and Beijing Funding for Training Talents (Grant No. 2012D009012000003). Dr. Gong acknowledges his Visiting Adjunct Professor appointments in the Department of Radiology at the University of Illinois
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