Connectomics
Electroencephalography (EEG) measures the differences in the electrical potential generated by oscillating currents at the surface of the scalp, due to the non-invasive, external placement of the electrodes.
EM has been used to produce connectomes from a variety of nervous system samples, including publicly available datasets that encompass the entire brain[24] and ventral nerve cord[25][26] of adult Drosophila melanogaster, the full central nervous system (connected brain and ventral nerve cord) of larval Drosophila melanogaster,[27] and volumes from mouse[28] and human cortex.
[31] Electron microscopy is the imaging technique that provides the highest spatial resolution, which is crucial for being able to recover presynaptic and postsynaptic sites as well as fine morphological details.
[35][36] The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D electron microscopy,[37] used for neural circuit reconstruction.
[38] In addition to advanced microscopy techniques, connectomics heavily relies on software analysis tools and machine learning pipelines for reconstructing and analyzing neural networks.
[41] Neuroglancer, a web-based tool designed for visualizing and navigating large-scale neuroscience data, offers features like 3D rendering and interactive exploration of brain datasets.
[42] This comparative approach aims to uncover fundamental principles of brain organization and function by identifying conserved and divergent patterns in neural circuitry.
By analyzing similarities and differences in the wiring diagrams of various organisms, researchers can gain insights into the evolutionary processes shaping the nervous system, as well as into the neural basis of behavior and cognition.
[53] Within the last decade, largely owing to technological advancements in EM data collection and image processing, multiple synapse-scale connectome datasets have been generated for the fruit fly Drosophila melanogaster in its adult and larval forms.
The FAFB volume was imaged by a team at Janelia Research Campus using a novel high-throughput serial section transmission electron microscopy (ssTEM) pipeline.
[45] Dr. Sebastian Seung’s lab at Princeton used convolutional neural networks (CNNs) to automatically segment neurons and detect pre- and post-synaptic sites in the volume.
This automated version was then used as a starting point for a massive community effort among fly neuroscientists to proofread neuronal morphologies by correcting errors and adding information about cell type and other attributes.
It was collected using focused ion beam scanning electron microscopy (FIB-SEM) which generated an 8 nm isotropic dataset, then automatically segmented using a flood-filling network before being manually proofread by a team of experts.
The female adult nerve cord (FANC) was collected using high-throughput ssTEM by Dr. Wei-Chung Allen Lee’s lab at Harvard Medical School.
The male adult nerve cord (MANC) was collected and segmented at Janelia using FIB-SEM and flood-filling network protocols modified from the Hemibrain pipeline.
[26] In a collaboration between researchers at Janelia, Google, the University of Cambridge, and the MRC Laboratory of Molecular Biology (LMB), it is fully proofread and annotated with cell types and other properties, and searchable on neuPrint.
[63] The connectome of a complete central nervous system (connected brain and VNC) of a 1st instar D. melanogaster larva has been collected as a single volume.
[27] This dataset of 3016 neurons was segmented and annotated manually using CATMAID by a team of people mainly led by researchers at Janelia, Cambridge, and the MRC LMB.
[69] Similarly, connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.
[72] With this in mind, diseases can not only be tracked, but predicted based on behavior of previous cases, a process that would take an extensive period of time to collect and record.
[72] Another study supports the finding that there is relation between connectivity and likelihood of disease, as researchers found those diagnosed with schizophrenia have less structurally complete brain networks.
[75] The main drawback in this area of connectomics is not being able to achieve images of whole-brain networks, therefore it is hard to make complete and accurate assumptions about cause and effect of diseases' neural pathways.
Connectomics has been used to find neuromarkers associated with social anxiety disorder (SAD) at a high precision rate in improving related symptoms.
[77] This is an expanding field and there is room for greater application to mental health disorders and brain malfunction, in which current research is building on neural networks and the psychopathology involved.
By integrating these two fields, researchers can explore how genetic variations and gene expression patterns influence the wiring and organization of neural circuits.
Additionally, connectomics can benefit from genomics by leveraging genetic tools and techniques to manipulate specific genes or neuronal populations to study their impact on neural circuitry and behavior.
[88] The goal was to obtain and distribute information regarding the structural and functional connections within the human brain, improving imaging and analysis methods to enhance resolution and practicality in the realm of connectomics.
[88] By understanding the wiring patterns within and across individuals, researchers hope to unravel the electrical signals that give rise to our thoughts, emotions, and behaviors.
The potential of a "Connectome II" project has been referenced recently, which would focus on developing a scanner designed for high-throughput studies involving multiple subjects.
[89] Advancements in this area might also involve incorporating wearable mobile technology to acquire various types of behavioral data, complementing the neuroimaging information gathered by the scanner.
