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Background: This study investigates (1) whether alterations in magnetic resonance imaging (MRI)-based structural global network organization is impaired in patients with major depressive disorder (MDD), (2) whether in-patient treatment including pharmacological, psychological and neurostimulation interventions is linked to changes in structural brain connectivity and (3) whether brain structural changes relate to changes in depression symptomatology.
Methods: One hundred seventy-eight subjects – 109 subjects diagnosed with current MDD and 55 healthy controls (HC) - participated in the present study (baseline + 6-weeks follow up). Fifty-six depressed patients were treated with electroconvulsive therapy (ECT) and 67 received in-patient treatment without ECT. Here, grey matter T1-weighted MRI was used to define nodes and DWI-based tractography to define the connections – or edges – between the nodes creating a structural connectome. Changes over time in depressions symptom severity was measured with the Hamilton Depression Ratings Scale.
Results: MDD patients showed reduced connectivity strength at baseline compared to healthy controls. MDD patients showed a significant increase of connectivity strength over time, an effect that was not detected in HC. An increase of connectivity strength was associated with a decrease in depression symptom severity. These effects were independent of treatment choice, suggesting a nonspecific effect that cannot be traced back to ECT.
Conclusion: We demonstrate an alleviation of structural brain dysconnectivity in MDD patients after successful antidepressive treatment, which is most prominent in those patients that show the greatest reduction in depressive symptomatology. This pattern of results suggests neuroplastic mechanisms involved in the successful treatment of depression and should be investigated as a potential treatment target in future studies.
Research Category and Technology and Methods: Clinical Research: 2. Electroconvulsive Therapy (ECT)
Connectomic analysis of apical dendrite innervation in pyramidal neurons of mouse cerebral cortex
(2020)
The central goal of this study was to generate synapse-resolution maps of local and long-range innervation on apical dendrites (AD) in mouse cerebral cortex. We used three-dimensional electron microscopy (3D-EM) to first measure the cell-type specific balance in the excitatory and inhibitory input on ADs. Further, we found two inhibitory axon populations with preference for apical dendrites originating from layer 2 and 3/5. Additionally, we used a combination of large-scale volumetric light and electron microscopy to investigate the innervation preference of long-range cortical projections onto ADs. To generate such large-scale 3D-EM datasets, we also developed a software package to automate aberration adjustment.
The balance of excitation and inhibition defines the computational properties of neurons. We, therefore, generated 6 datasets and annotated 26,548 excitatory and inhibitory synapses to map the relative inhibitory strength on the AD of pyramidal neurons in layers 1 and 2 (L1 and 2) of the cortex. We found consistent and cell-type specific patterns of inhibitory strength along the apical dendrite of L2-5 pyramidal neurons in primary somatosensory (S1), secondary visual (V2), posterior parietal (PPC) and anterior cingulate (ACC) cortices. L2 and L5 pyramidal neurons had inhibitory hot-zones at their main bifurcation and distal apical dendrite tuft, respectively. In contrast, L3 neurons had a baseline (~10%) level of inhibition along their apical dendrite. As controls, we quantified the effect of synapse strength (size), dendrite diameter, AD classification and synapse identification methods on the cell-type specific synapse densities. To classify L5 pyramidal subtypes, we performed hierarchical clustering using morphological properties that were described to differentiate slender- and thick-tufted L5 neurons.
We also investigated the distance to soma as a predictor of fractional inhibition around the main bifurcation of apical dendrites. Interestingly, we found a strong exponential relationship that was absent in density of either synapse type. This suggests a distance dependent control mechanism designed specifically for the balance (in synapse numbers) of excitation and inhibition.
Next, we focused on the inhibitory innervation preference for apical dendrite of pyramidal neuron. We, therefore, annotated 5,448 output synapses of AD-targeting inhibitory axons and found two populations specific for either L2 or L3/5 apical dendrites. Together with previous findings on preferential innervation of sub-cellular structures by inhibitory axons, this suggests two distinct inhibitory circuits for control of AD activity in L2 vs. deep-layer pyramidal neurons. This innervation preference was surprisingly consistent across S1, V2, PPC and ACC cortices.
3D-EM data acquisition is a laborious process that is made easier and more popular everyday by technical progress in the laboratory and industrial settings. To make data acquisition robust using our custom-built 3D-EM microscopes, an automatic aberration software was implemented to adjust the objective lens and the stigmators of the electron microscope. This method was used in multiple month-long experiments across 2 microscopes and 10 datasets. The aberration adjustment used the reduction in image details (high-frequency elements) to estimate the level of deviation from optimal focus and stigmator parameters. However, large objects in EM micrographs such as blood vessel and nuclei cross-sections generated anomalous results. We, therefore, added image processing routines based on edge detection combined with morphological operations to exclude such large objects.
Finally, we performed a correlative three-dimensional (3D) light (LM) and electron (EM) microscopy experiment to map the long-range primary visual (V1) and secondary motor (M2) cortical input to ADs in layer 1 of PPC using the “FluoEM” approach. This method allows for identification of the long-range source of projection axons in EM volumes without the need for EM-dense label conversion or heat-induced markings. The long-range source of an axon in EM is identified based on the fluorescent protein that is expressed in its LM counterpart. In comparison to M2 input, Long-range axons from V1 had a higher tendency to target L3 pyramidal neurons in PPC according to our preliminary analysis. In combination with the difference observed in the synapse composition of L2 and L3 apical dendrites, this suggests the need for separate functional and structural analysis of L2 and 3 pyramidal neurons.
Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features.
Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.