Poster No:
144
Submission Type:
Abstract Submission
Authors:
Tamrin Barta1, Leonardo Novelli2, Nellie Georgiou-Karistianis1, Julie Stout1, Samantha Loi3, Yifat Glikmann-Johnston1, Adeel Razi4
Institutions:
1Monash University, Clayton, Victoria, 2Monash University, Melbourne, Victoria, 3University of Melbourne, Parkville, Victoria, 4Monash University, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Depression is one of the most common and impactful features in premanifest Huntington's Disease (pre-HD), many years prior to the onset of clinically meaningful motor signs, with symptoms fluctuating over disease progression. While depression appears most common in late premanifest period, its severity has been suggested to be unrelated to disease progression. The dorsal striatum, showing early atrophy in HD (Tabrizi et al., 2012; Wilson et al., 2018), is part of frontostriatal circuits that show aberrant connectivity in depression (Cheng et al., 2016; B. Li et al., 2018). Depression is increasingly conceptualized as a dysconnection syndrome (B. Li et al., 2018), with the default mode network (DMN) being a reliable neural marker of major depression in non-neurological populations (Mulders et al., 2015). We investigated if longitudinal changes in effective connectivity between the striatum and DMN contributes to longitudinal fluctuations in depression in pre-HD, using Dynamic Causal Modelling (DCM).
Methods:
We analyzed 3T resting-state fMRI data from 93 pre-HD participants (51.6% females; mean age = 42.7±8.9 years; mean CAG repeat length = 43.2±2.4). Participants were grouped based on depression history (diagnosed depressive disorder, including anxiety and psychosis comorbidity, both remitted and current [n=22, 23.7%] versus never diagnosed [n=71, 76.3%]). Within the depression group, 14 participants had current depression (mean duration = 2,743±1,526 days) and 8 were in remission. Brain regions included medial prefrontal cortex (MPFC [3,54,-2]), posterior cingulate cortex (PCC [0,-52,26]), bilateral hippocampi (HPC left [-29,-18,-16], right [29,-18,-16]), bilateral caudate (left [-10,14,0], right [10,14,0]), and bilateral putamen (left [-28,2,0], right [-28,2,0]). Spectral DCM was used to estimate subject-level connectivity, and Parametric Empirical Bayes for group-level effective connectivity changes between participants with different depression histories across timepoints. Medication use, sex, and disease staging (using HD-ISS staging) were controlled for.
Results:
Retention across timepoints was 88.2% (n=82) at Visit 2 and 76.3% (n=71) at Visit 3. Depression group membership remained stable, with 76.8% (n=63) no history and 23.2% (n=19) with history at Visit 2, and 76.1% (n=54) no history and 23.9% (n=17) with history at Visit 3. Clinical scores showed consistently higher symptom levels in the depression history group across visits (Visit 1: BDI-II 15.2±9.4, HADS-D 8.1±4.7; Visit 2: BDI-II 14.8±9.1, HADS-D 7.8±4.5; Visit 3: BDI-II 14.5±8.9, HADS-D 7.6±4.3) compared to the no history group (Visit 1: BDI-II 5.3±4.2, HADS-D 2.8±2.1; Visit 2: BDI-II 5.1±4.0, HADS-D 2.6±2.0; Visit 3: BDI-II 4.9±3.9, HADS-D 2.5±1.9). Increased excitatory connectivity from MPFC to PCC (L. Li et al., 2017) and hippocampus (Wang et al., 2015) would parallel patterns seen in non-neurological depression while reduced effective connectivity from MPFC to putamen and caudate would suggest HD-specific network dysfunction (Grieve et al., 2013). Differential patterns in left versus right hippocampal connectivity would align with previous findings in remitted depression (Wang et al., 2015). Leave-one-out cross-validation of these connections would indicate whether network changes could predict depression status in pre-HD, particularly focusing on striatal and DMN self-connections as markers of synaptic activity (Bastos-Leite et al., 2015; Friston et al., 2003).
Conclusions:
This longitudinal investigation demonstrates how network dysconnection patterns in pre-HD evolve over time in relation to depression history. These findings suggest large-scale network dysfunction may contribute to the expression and maintenance of depression in Huntington's disease, potentially independent of disease progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Affective Disorders
Aging
Basal Ganglia
Computational Neuroscience
Degenerative Disease
Modeling
Movement Disorder
Psychiatric Disorders
Other - Depression
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Other, Please list
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FMRIPREP; MRIQC
Provide references using APA citation style.
Bastos-Leite, A. J., et al. (2015). Dysconnectivity Within the Default Mode in First-Episode Schizophrenia: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging. Schizophrenia Bulletin, 41(1), 144–153. https://doi.org/10.1093/schbul/sbu080
Cheng, W., et al. (2016). Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain, 139(12), 3296–3309. https://doi.org/10.1093/brain/aww255
Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. https://doi.org/10.1016/S1053-8119(03)00202-7
Grieve, S. M., et al. (2013). Widespread reductions in gray matter volume in depression. NeuroImage: Clinical, 3, 332–339. https://doi.org/10.1016/j.nicl.2013.08.016
Li, B., et al. (2018). A brain network model for depression: From symptom understanding to disease intervention. CNS Neuroscience & Therapeutics, 24(11), 1004–1019. https://doi.org/10.1111/cns.12998
Li, L., et al. (2017). Abnormal resting state effective connectivity within the default mode network in major depressive disorder: A spectral dynamic causal modeling study. Brain and Behavior, 7(7), e00732. https://doi.org/10.1002/brb3.732
Mulders, P. C., et al. (2015). Resting-state functional connectivity in major depressive disorder: A review. Neuroscience & Biobehavioral Reviews, 56, 330–344. https://doi.org/10.1016/j.neubiorev.2015.07.014
Tabrizi, S. J., et al. (2012). Potential endpoints for clinical trials in premanifest and early Huntington’s disease in the TRACK-HD study: Analysis of 24 month observational data. The Lancet Neurology, 11(1), 42–53. https://doi.org/10.1016/S1474-4422(11)70263-0
Wang, Z., et al. (2015). Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression: A longitudinal resting-state study. Neuroscience Bulletin, 31(1), 13–21. https://doi.org/10.1007/s12264-014-1489-1
Wilson, H., et al. (2018). Chapter Nine—Structural Magnetic Resonance Imaging in Huntington’s Disease. In M. Politis (Ed.), International Review of Neurobiology (Vol. 142, pp. 335–380). Academic Press. https://doi.org/10.1016/bs.irn.2018.09.006
No