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Resting-state changes along the cortical depth detected during evolution of major depressive disorder1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany, 2Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH, Aachen, Germany, 3JARA - BRAIN - Translational Medicine, Aachen, Germany, 4Department of Neurology, RWTH Aachen University, Aachen, Germany, 5Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany
Synopsis
Keywords: Psychiatric Disorders, Brain Connectivity, Laminar connectivity, depression
Motivation: The relative contribution of each cortical depth to the network disturbances coupled with depression remains unexplored.
Goal(s): We aim to identify changes in connectivity along the cortical thickness during depression recovery.
Approach: We use high-resolution, large-coverage EPIK to functionally map the brain of patients before and after treatment and perform cortical-depth specific analysis of network connectivity.
Results: Changes in connectivity linked to depression treatment were observed at multiple depths of the cortex in two resting-state networks.
Impact: The demonstration that network connectivity does not change homogeneously in a patient population along the cortical depth suggests the importance of adding this variable to the analysis of psychiatric disorders to improve understanding of the mechanisms behind network disturbances.
Introduction
Major depressive disorder has been greatly investigated from a network perspective in the past ten years, and multiple systems have been identified as potential targets (e.g., default mode, executive control and salience networks), with their level of connectivity being related to the mental state of patients1-7. Recent advances in functional MRI (fMRI) have enabled the whole cerebrum to be mapped with sub-millimetre resolution8, 9. High-resolution, large-FOV studies employing these novel methods have suggested that resting-state networks are unequally segregated along the cortical depth, i.e., their functional connections engage superficial, intermediate and deep layers of the cortex differently10. To our knowledge, the depth-dependent features of network alterations during depression remains completely unexplored. In this study, we start from the premise that the connectivity of resting-state cortical networks can vary over the course of depression (as observed at low-resolution) and aim to characterise these changes along the cortical depth using high-resolution, whole-brain fMRI in patients undergoing depression treatment. Our objective is, hence, to decipher the depth(s) at which functional connections are weakened or enhanced during recovery from depression.Methods
Fifteen adult patients (eight males, age: 34 ± 11) diagnosed with major depression disorder (Uniklink Aachen) were imaged at a 7T scanner before and after exposure to treatment (measurements 4-6 weeks apart). One functional and one structural MRI scan per subject were used in the current study. For fMRI, 138 volumes were acquired in a resting state using GE-EPIK (EPI with Keyhole) with TR-external phase-correction10-17 (TR/TE = 3500/22 ms, FA = 85°, partial Fourier = 5/8, 3-fold in-plane/3-fold inter-plane (multi-band) acceleration, bandwidth = 875 Hz/Px, Matrix = 336×336×123 (0.63×0.63×0.63 mm3) and αPC/αMain = 9°/90°). Magnitude and phase images were reconstructed. The structural scan was an MP2RAGE, with TR/TE = 4500/2 ms, matrix = 208×300×320 (0.75×0.75×0.75 mm3). Pre-processing of the functional images (magnitude and phase) was conducted using SPM, FSL and AFNI, and included slice timing correction, realignment, temporal filtering, regression of 12 motion parameters, regression of the mean time course of CSF and WM voxels and regression of physiological signals (respiratory and cardiac signals, measured along with fMRI). To correct for the vein-related BOLD signal bias, the pre-processed phase signal was additionally regressed out from the pre-processed magnitude signal18. The structural image was used to segment the cortical ribbon and generate six surfaces along the cortical depth using Freesurfer. The functional image was mapped to these surfaces in each cerebral hemisphere, and the time courses of vertices lying within 23 ROIs were averaged, resulting in 276 time courses (23×6×2). For each fMRI session, functional connectivity was calculated as the temporal correlation between pairs of time courses. ROI-depth-to-ROI-depth connectivity matrices were generated for six different networks by selecting network ROIs. For each network, the statistical significance of changes in connectivity was assessed with a paired t-test between the connectivity matrices of subjects before and after treatment. Connectivity graphs, including ROI and cortical depth information (cortical depth colour-coded), were produced using Matlab for visualisation purposes. To ease the interpretation of the network results, the network connectivity matrices, which mapped connections between ROIs and depths, were simplified by averaging across ROIs. This yielded a depth-to-depth connectivity matrix per cortical network. These matrices were normalised for each subject by subtracting the mean value and dividing by the standard deviation (to make the analysis sensitive to differences pertaining to the involvement of different depths and not to the overall level of connectivity) and were further simplified by averaging across one dimension to render one cortical depth-dependent connectivity profile per network. Differences in the connectivity of each cortical depth before and after treatment were assessed using a paired t-test.Results
Preliminary results showed a relative decrease in the connectivity at intermediate depths in the executive control network and within the intermediate/superficial layers in the sensorimotor network (Figure 1). Other networks seemed to maintain a similar level of involvement at all cortical depths in terms of network connectivity before and after treatment. However, increases and decreases in connectivity affecting particular ROIs and depths could be observed in most evaluated networks (Figure 2). However, this did not always lead to observable changes in the simplified depth-connectivity network profile.Conclusions
Our results suggest that depression treatment can modify both the general level of network connectivity and the relative involvement of cortical depths. Ongoing analysis aims to: a) assess the potential correlation between the observed laminar connectivity changes and the evolution of subjects’ psychological scores; b) incorporate data from healthy matched controls; and c) examine potential laminar interactions between the evaluated networks.Acknowledgements
We thank Ms Petra Engels, Ms Elke Bechholz and Ms Anita Köth for technical support during MRI acquisition; Ms Rick Claire for abstract corrections, Ms. Malsbenden for figure editing, and all the patients for their excellent cooperation.
References
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