Depression is one of the most common and impactful features in premanifest Huntington's disease (HD). Depression is increasingly being conceptualised as a dysconnection syndrome and two large-scale networks surmised to contribute to the expression of depressive symptoms in premanifest HD are the striatum and the default mode network. Existing neuroimaging studies are limited and relied on functional connectivity: an inherently undirected measure of connectivity. Dynamic causal modelling allows testing of neurobiologically plausible models of connectivity changes in pre-specified networks. We investigated default mode network and striatal effective connectivity and depression in premanifest HD, using these model-based methods. We analysed 3T resting state fMRI data from 93 premanifest HD participants (51.6% females; Mage = 42.7). Behavioural measures included history of depression, Beck Depression Inventory, 2nd Edition (BDI-II) and Hospital Anxiety and Depression Scale, depression subscale (HADS-D). A cut-off score recommended for use in HD categorised clinically significant depressive symptoms. Regions of interest (ROIs) included medial prefrontal cortex, posterior cingulate, hippocampus, caudate, and putamen. Each ROI time series was calculated as the first principal component of the voxels' activity within an 8 mm sphere for medial prefrontal cortex and posterior cingulate and a 6 mm sphere for all other regions and was further constrained within masks. Spectral dynamic causal modelling was used to estimate subject-level connectivity and parametric empirical bayes was employed to estimate group-level effective connectivity between participants with self-report depression history and those without. Leave-one-out cross-validation was performed for connections that reached this criterion. Model estimation was excellent, with average variance-explained of 89.70%. Having a depression diagnosis was associated with aberrant excitatory influence of both posterior and anterior DMN to hippocampi and striatal areas. No aberrant connections were found from medial prefrontal cortex to caudate or posterior cingulate. The present study demonstrates that aberrant connectivity patterns for premanifest HD with a history of depression is associated with coupling differences in depressive symptoms. Leave-one-out cross-validation accurately predicted clinically elevated depressive symptoms. Correct classification reached significance for HADS-D cut-off scores, corr(91) = -0.29: p = 0.002, and BDI-II scores, corr(91) = 0.36, p < 0.001. These findings suggest network dysconnection as a neural basis for depression in premanifest HD. Aberrant effective connections were associated with self-reported depression history, which was differentially associated with coupling changes in depressive symptoms. This adds to our understanding of the pathophysiology of HD and suggests defining functional networks of neuropsychiatric features plays an important role in understanding the disease.

The authors have declared no competing interest.

AR and LN are funded by the Australian Research Council (DP200100757) and AR by Australian National Health and Medical Research Council Investigator Grant (Ref: 1194910). AR is affiliated with The Wellcome Centre for Human Neuroimaging supported by core funding from Wellcome [203147/Z/16/Z]. AR is a CIFAR Azrieli Global Scholar in the Brain, Mind, and Consciousness Program.

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This study used data from Track-On HD, which was approved by local ethics committees at all four participating sites: Leiden (Netherlands), London (United Kingdom), Paris (France), and Vancouver (Canada), and has been reported in Kloppel et al. (2015) and Tabrizi et al. (2009).

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