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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.
Longitudinal changes of cortical microstructure in Parkinson's disease assessed with T1 relaxometry
(2016)
Background: Histological evidence suggests that pathology in Parkinson's disease (PD) goes beyond nigrostriatal degeneration and also affects the cerebral cortex. Quantitative MRI (qMRI) techniques allow the assessment of changes in brain tissue composition. However, the development and pattern of disease-related cortical changes have not yet been demonstrated in PD with qMRI methods. The aim of this study was to investigate longitudinal cortical microstructural changes in PD with quantitative T1 relaxometry.
Methods: 13 patients with mild to moderate PD and 20 matched healthy subjects underwent high resolution T1 mapping at two time points with an interval of 6.4 years (healthy subjects: 6.5 years). Data from two healthy subjects had to be excluded due to MRI artifacts. Surface-based analysis of cortical T1 values was performed with the FreeSurfer toolbox.
Results: In PD patients, a widespread decrease of cortical T1 was detected during follow-up which affected large parts of the temporo-parietal and occipital cortices and also frontal areas. In contrast, age-related T1 decrease in the healthy control group was much less pronounced and only found in lateral frontal, parietal and temporal areas. Average cortical T1 values did not differ between the groups at baseline (p = 0.17), but were reduced in patients at follow-up (p = 0.0004). Annualized relative changes of cortical T1 were higher in patients vs. healthy subjects (patients: − 0.72 ± 0.64%/year; healthy subjects: − 0.17 ± 0.41%/year, p = 0.007).
Conclusions: In patients with PD, the development of widespread changes in cortical microstructure was observed as reflected by a reduction of cortical T1. The pattern of T1 decrease in PD patients exceeded the normal T1 decrease as found in physiological aging and showed considerable overlap with the pattern of cortical thinning demonstrated in previous PD studies. Therefore, cortical T1 might be a promising additional imaging marker for future longitudinal PD studies. The biological mechanisms underlying cortical T1 reductions remain to be further elucidated.
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.