Investigating neural primacy in Major Depressive Disorder: multivariate Granger causality analysis of resting-state fMRI time-series data

Abstract

Major Depressive Disorder (MDD) has been conceptualized as a neural network-level disease. Few studies of the neural bases of depression, however, have used analytical techniques that are capable of testing network-level hypotheses of neural dysfunction in this disorder. Moreover, of those that have, fewer still have attempted to determine the directionality of influence within functionally abnormal networks of structures. We used multivariate GC analysis, a technique that estimates the extent to which preceding neural activity in one or more seed regions predicts subsequent activity in target brain regions, to analyze blood-oxygen-level-dependent (BOLD) data collected during eyes-closed rest from depressed and never-depressed persons. We found that activation in the hippocampus predicted subsequent increases in ventral anterior cingulate cortex (vACC) activity in depression, and that activity in the medial prefrontal cortex and vACC were mutually reinforcing in MDD. Hippocampal and vACC activation in depressed participants predicted subsequent decreases in dorsal cortical activity. This study shows that, on a moment-by-moment basis, there is increased excitatory activity among limbic and paralimbic structures, as well as increased inhibition in the activity of dorsal cortical structures, by limbic structures in depression; these aberrant patterns of effective connectivity implicate disturbances in the mesostriatal dopamine system in depression. These findings advance the neural theory of depression by detailing specific patterns of limbic excitation in MDD, by making explicit the primary role of limbic inhibition of dorsal cortex in the cortico-limbic relation posited to underlie depression, and by presenting an integrated neurofunctional account of altered dopamine function in this disorder.

Figure 1

Depiction of imaging volume used to collect BOLD data.

Figure 2

Ventral ACC ROI derived from comparison of default-mode networks of depressed with control groups.

Figure 3

T-statistic map of MDD-versus-control comparison of vACC-to-whole brain GC analysis fit coefficients. Increased activity in the hippocampus and MPFC predicts subsequent increases in vACC activity more in MDD than in control participants. Activity increases in DLPFC predict activity decreases in the vACC more in MDD. Group mean coefficients are averaged from suprathreshold cluster. Group means in bold are significantly different from zero (two-tailed test, all ps < .01, uncorrected).

Figure 4

T-statistic map of MDD-versus-control comparison of whole brain-to-vACC GC analysis fit coefficients. Increased activity in the vACC predicts subsequent decreases in activity in the PCC, DMPFC and ventral striatum, bilaterally more in MDD than in control participants. Group mean coefficients are averaged from suprathreshold cluster, ventral striatum clusters averaged together. Group means in bold are significantly different from zero (two-tailed test, all ps < .01, uncorrected).

Figure 5

Statistical map of between-groups comparison of path coefficients from multivariate GC analysis including vACC, ventral striatum (vSTR), hippocampus (HPC), MPFC, DLPFC, DMPFC, and PCC. Blue/red arrows indicate significantly greater inhibition/activation of subsequent target activity in MDD versus control where a within-group effect was also present in the MDD group (two-tailed tests, all ps < .05, uncorrected). Table values are group mean path coefficients with prediction going from row to column. Group means in bold are significantly different from zero.