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BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.
Journal Article
Whole-brain comparison of rodent and human brains using spatial transcriptomics
The ever-increasing use of mouse models in preclinical neuroscience research calls for an improvement in the methods used to translate findings between mouse and human brains. Previously, we showed that the brains of primates can be compared in a direct quantitative manner using a common reference space built from white matter tractography data (Mars et al., 2018b). Here, we extend the common space approach to evaluate the similarity of mouse and human brain regions using openly accessible brain-wide transcriptomic data sets. We show that mouse-human homologous genes capture broad patterns of neuroanatomical organization, but the resolution of cross-species correspondences can be improved using a novel supervised machine learning approach. Using this method, we demonstrate that sensorimotor subdivisions of the neocortex exhibit greater similarity between species, compared with supramodal subdivisions, and mouse isocortical regions separate into sensorimotor and supramodal clusters based on their similarity to human cortical regions. We also find that mouse and human striatal regions are strongly conserved, with the mouse caudoputamen exhibiting an equal degree of similarity to both the human caudate and putamen.
Journal Article
A platform for brain-wide imaging and reconstruction of individual neurons
The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain. Nerve cells or neurons transmit electrical impulses to each other over long distances. These signals travel through highly branching nerve fibers called axons, which are about one hundred times thinner than a human hair, and can extend across the entire brain. Tracing the axon of a neuron from start to end can help to explain how individual neurons and brain areas communicate signals over long distances. A mouse brain contains approximately 70 million neurons, and tracing the axons of many neurons within a brain is a challenging problem. Tackling this problem requires a method for imaging entire brains in high enough detail to unambiguously resolve and follow axons from individual neurons across the brain. Economo, Clack et al. now demonstrate such a method for three-dimensional imaging of tissue samples as large as the whole mouse brain. This system is fully automated and works by first imaging a layer of tissue near the exposed surface of a sample, and then cutting off a slice of tissue that corresponds to the volume that has been imaged. These steps then repeat until the entire sample has been imaged; this takes about a week for a whole mouse brain and produces about 30 terabytes of images. Economo, Clack et al.’s advance can uncover how neurons communicate over long distances with an unprecedented level of precision. The method can now be used to generate a comprehensive database of neurons and their long distance connections. Such a database would aid efforts to model the roles of neural circuits in the brain, and inform the design of experiments to study brain activity during particular behaviors.
Journal Article
The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke
The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
Journal Article
Waxholm Space atlas of the Sprague Dawley rat brain
Three-dimensional digital brain atlases represent an important new generation of neuroinformatics tools for understanding complex brain anatomy, assigning location to experimental data, and planning of experiments. We have acquired a microscopic resolution isotropic MRI and DTI atlasing template for the Sprague Dawley rat brain with 39μm isotropic voxels for the MRI volume and 78μm isotropic voxels for the DTI. Building on this template, we have delineated 76 major anatomical structures in the brain. Delineation criteria are provided for each structure. We have applied a spatial reference system based on internal brain landmarks according to the Waxholm Space standard, previously developed for the mouse brain, and furthermore connected this spatial reference system to the widely used stereotaxic coordinate system by identifying cranial sutures and related stereotaxic landmarks in the template using contrast given by the active staining technique applied to the tissue. With the release of the present atlasing template and anatomical delineations, we provide a new tool for spatial orientationanalysis of neuroanatomical location, and planning and guidance of experimental procedures in the rat brain. The use of Waxholm Space and related infrastructures will connect the atlas to interoperable resources and services for multi-level data integration and analysis across reference spaces.
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•High-resolution MRI and DTI template for the Sprague Dawley rat brain•Atlas of major anatomical structures with detailed delineation criteria•Internal landmarks for implementing ‘Waxholm Space’ spatial reference system•Cranial landmarks for translation to stereotaxic coordinate system•The template and atlas form a new tool for spatial orientation in the rat brain.
Journal Article
Genetic architecture of subcortical brain structures in 38,851 individuals
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for
Drosophila
orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
Genome-wide analysis identifies variants associated with the volume of seven different subcortical brain regions defined by magnetic resonance imaging. Implicated genes are involved in neurodevelopmental and synaptic signaling pathways.
Journal Article
SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.
Journal Article
Modeling brain dynamics after tumor resection using The Virtual Brain
Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first “virtual neurosurgery”, mimicking patient’s actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome.
We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation.
•We build individual models of brain activity in brain tumor patients and controls.•Model parameters are stable at group level between pre- and post-operatory phase.•Global scaling parameter and efficiency of SC are negatively correlated.•Virtual neurosurgery (modelling using a resected individual SC matrix) is explored.•Variability in the results is evident; caution and more data are needed.
Journal Article