Structural brain networks in remitted psychotic depression

Abstract

Major depressive disorder with psychotic features (psychotic depression) is a severe disorder. Compared with other psychotic disorders such as schizophrenia, relatively few studies on the neurobiology of psychotic depression have been pursued. Neuroimaging studies investigating psychotic depression have provided evidence for distributed structural brain abnormalities implicating the insular cortex and limbic system. We examined structural brain networks in participants (N = 245) using magnetic resonance imaging. This sample included healthy controls (n = 159) and the largest cross-sectional sample of patients with remitted psychotic depression (n = 86) collected to date. All patients participated in the Study of Pharmacotherapy of Psychotic Depression II randomized controlled trial. We used a novel, whole-brain, data-driven parcellation technique-non-negative matrix factorization-and applied it to cortical thickness data to derive structural covariance networks. We compared patients with remitted psychotic depression to healthy controls and found that patients had significantly thinner cortex in five structural covariance networks (insular-limbic, occipito-temporal, temporal, parahippocampal-limbic, and inferior fronto-temporal), confirming our hypothesis that affected brain networks would incorporate cortico-limbic regions. We also found that cross-sectional depression and severity scores at the time of scanning were associated with the insular-limbic network. Furthermore, the insular-limbic network predicted future severity scores that were collected at the time of recurrence of psychotic depression or sustained remission. Overall, decreased cortical thickness was found in five structural brain networks in patients with remitted psychotic depression and brain-behavior relationships were observed, particularly between the insular-limbic network and illness severity.

Fig. 1. Non-negative matrix factorization (NMF).

In this figure, X represents the original data matrix as the product of two matrices, B and C. X contains the cortical thickness (CT) data (visualized above the X matrix) for each vertex (rows) and for all participants (columns). B is a matrix that contains, in each column, the loading for each vertex on one of the K networks derived by NMF (an example is visualized above the B matrix). C is a matrix that contains, in each row, the participant-specific coefficients (CT Scores) for each network derived from NMF. The histogram above the C matrix illustrates a sample row of the C matrix with scores for all participants in one network. Both B and C are greater than or equal to 0, thus elements of the factorization are non-negative. Matrices are shown with the following dimensions: V = number of vertices, N = number of participants; K = number of networks.

Fig. 2. Structural covariance networks derived using non-negative matrix factorization (NMF).

Structural covariance networks are shown for the 14-network NMF solution. The spatial distribution of each network is indicated by loadings at each vertex in arbitrary units. Structural covariance networks include: (1) fronto-parietal; (2) insular-limbic; (3) occipital; (4) fronto-polar; (5) dorso-lateral prefrontal; (6) occipito-temporal; (7) pre-central; (8) temporal; (9) post-central; (10) temporo-polar; (11) parahippocampal-limbic; (12) cingular-post-central; (13) inferior fronto-temporal; and (14) inferior frontal networks.

Fig. 3. Patients demonstrated thinner cortex in five structural covariance networks.

When compared with healthy control participants, patients with remitted psychotic depression demonstrated significant (p < 0.05, False Discovery Rate corrected) reductions in cortical thickness (CT) in five structural networks, including: insular-limbic (Network 2); occipito-temporal (Network 6); temporal (Network 8); parahippocampal-limbic (Network 11); and inferior fronto-temporal (Network 13) networks. Composite network boundaries were obtained by assigning each vertex to the network with the highest loading for that vertex (from the B matrix) across all 14 networks.