Neuro-Informatics seminars

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

Brains must integrate complex sensory information and compare to past events to generate appropriate behavioral responses. The neural circuit basis of these computations is unclear and the underlying structure unknown. Here, we mapped the comprehensive synaptic wiring diagram of the fruit fly larva brain, which contains 3,013 neurons and 544K synaptic sites. It is the most complete insect connectome to date: 1) Both brain hemispheres are reconstructed, allowing investigation of neural pathways that include contralateral axons, which we found in 37% of brain neurons. 2) All sensory neurons and descending neurons are reconstructed, allowing one to follow signals in an uninterrupted chain—from the sensory periphery, through the brain, to motor neurons in the nerve cord. We developed novel computational tools, allowing us to cluster the brain and investigate how information flows through it. We discovered that feedforward pathways from sensory to descending neurons are multilayered and highly multimodal. Robust feedback was observed at almost all levels of the brain, including descending neurons. We investigated how the brain hemispheres communicate with each other and the nerve cord, leading to identification of novel circuit motifs. This work provides the complete blueprint of a brain and a strong foundation to study the structure-function relationship of neural circuits.

In modern neuroscience, we often want to extract information from recordings of many neurons in the brain. Unfortunately, the activity of individual neurons is very noisy, making it difficult to relate to cognition and behavior. Thankfully, we can use the correlations across time and neurons to denoise the data we record. In particular, using recent advances in machine learning, we can build models which harness this structure in the data to extract more interpretable signals. In this talk, we present two such methods as well as examples of how they can help us gain further insights into the neural underpinnings of behavior.

Behavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child’s profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample, and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome. (https://www.medrxiv.org/content/10.1101/2021.09.15.21262637v1)

Given the intrinsically large and complex data sets collected in neuroimaging research, coupled with the extensive array of shared data and tools amassed in the research community, ReproNim seeks to lower the barriers for efficient: use of data; description of data and process; use of standards and best practices; sharing; and subsequent reuse of the collective ‘big’ data. Aggregation of data and reuse of analytic methods have become critical in addressing concerns about the replicability and power of many of today’s neuroimaging studies.

Large-scale neural recordings contain high-dimensional structure that cannot be easily captured by existing data visualization methods. We therefore developed an embedding algorithm called Rastermap, which captures highly nonlinear relationships between neurons, and provides useful visualizations by assigning each neuron to a location in the embedding space. Compared to standard algorithms such as t-SNE and UMAP, Rastermap finds finer and higher dimensional patterns of neural variability, as measured by quantitative benchmarks. We applied Rastermap to a variety of datasets, including spontaneous neural activity, neural activity during a virtual reality task, widefield neural imaging data during a 2AFC task, artificial neural activity from an agent playing atari games, and neural responses to visual textures. We found within these datasets unique subpopulations of neurons encoding abstract properties of the environment.

The standard method for staining structures in the brain is to slice the brain into 2D sections. Each slice is treated using a technique such as in-situ hybridization to examine the spatial expression of a particular molecule at a given developmental timepoint. Depending on the brain structures being studied, slices can be made coronally, sagitally, or at any angle that is thought to be optimal for analysis. However, assimilating the information presented in the 2D slice images to gain quantitiative and informative 3D expression patterns is challenging. Even if expression levels are presented as voxels, to give 3D expression clouds, it can be difficult to compare expression across individuals and analysing such data requires significant expertise and imagination. In this talk, I will describe a new approach to examining histology slices, in which the user defines the brain structure of interest by drawing curves around it on each slice in a set and the depth of tissue from which to sample expression. The sampled 'curves' are then assembled into a 3D surface, which can then be transformed onto a common reference frame for comparative analysis. I will show how other neuroscientists can obtain and use the tool, which is called Stalefish, to analyse their own image data with no (or minimal) changes to their slice preparation workflow.

The brain is complex over multiple length-scales, from many protein molecules forming intricate nano-machines in a synapse to many neurons forming interconnected networks across the brain. Unraveling this multi-scale complexity is fundamental to our understanding of brain function and disease. In this lecture, I will introduce advances in visualizing the complex, multi-scale structures in the brain. Emphasis will be on new imaging techniques, including cryo electron tomography and correlative light-electron microscopy that enabled revealing in situ organization of synaptic molecules, and ultra-high speed volumetric imaging method VISoR developed to map brain-wide circuits at subcellular resolution. I will also discuss challenges and opportunities for interdisciplinary research collaboration to analyze and understand the enormous data generated by these cutting-edge technologies.

The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence are often inefficient and the results hard to interpret. Here we introduce Suite2p: a fast, accurate, parameter-free and complete pipeline that registers raw movies, detects active and/or inactive cells (using Cellpose), extracts their calcium traces and infers their spike times. Suite2p runs faster than real time on standard workstations and outperforms state-of-the-art methods on newly developed ground-truth benchmarks for motion correction and cell detection.

The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture I will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. I will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. I will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, I will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect I will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools

Neuroscientists routinely perform experiments aimed at recording or manipulating neural activity, uncovering physiological processes underlying brain function or elucidating aspects of brain anatomy. Understanding how the brain generates behaviour ultimately depends on merging the results of these experiments into a unified picture of brain anatomy and function. We present BrainGlobe, a new initiative aimed at developing common Python tools for computational neuroanatomy. These include cellfinder for fast, accurate cell detection in whole-brain microscopy images, brainreg for aligning images to a reference atlas, and brainrender for visualisation of anatomically registered data. These software packages are developed around the BrainGlobe Atlas API. This API provides a common Python interface to download and interact with reference brain atlases from multiple species (including human, mouse and larval zebrafish). This allows software to be developed agnostic to the atlas and species, increasing adoption and interoperability of software tools in neuroscience.

In this webinar, Dr Ye Tian and A/Prof Andrew Zalesky will present new research on mapping the functional architecture of the human subcortex. They used 3T and 7T functional MRI from more than 1000 people to map one of the most detailed functional atlases of the human subcortex to date. Comprising four hierarchical scales, the new atlas reveals the complex topographic organisation of the subcortex, which dynamically adapts to changing cognitive demands. The atlas enables whole-brain mapping of connectomes and has been used to optimise targeting of deep brain stimulation. This joint work with Professors Michael Breakspear and Daniel Margulies was recently published in Nature Neuroscience. In the second part of the webinar, Dr Ye Tian will present her current research on the biological ageing of different body systems, including the human brain, in health and degenerative conditions. Conducted in more than 30,000 individuals, this research reveals associations between the biological ageing of different body systems. She will show the impact of lifestyle factors on ageing and how advanced ageing can predict the risk of mortality. Associate Professor Andrew Zalesky is a Principal Researcher with a joint appointment between the Faculties of Engineering and Medicine at The University of Melbourne. He currently holds a NHMRC Senior Research Fellowship and serves as Associate Editor for Brain Topography, Neuroimage Clinical and Network Neuroscience. Dr Zalesky is recognised for the novel tools that he has developed to analyse brain networks and their application to the study of neuropsychiatric disorders. Dr Ye Tian is a postdoctoral researcher at the Department of Psychiatry, University of Melbourne. She received her PhD from the University of Melbourne in 2020, during which she established the Melbourne Subcortex Atlas. Dr Tian is interested in understanding brain organisation and using brain imaging techniques to unveil neuropathology underpinning neuropsychiatric disorders.

The Group Iterative Multiple Model Estimation (GIMME) framework has emerged as a promising method for characterizing connections between brain regions in functional neuroimaging data. Two of the most appealing features of this framework are its ability to estimate the directionality of connections between network nodes and its ability to determine whether those connections apply to everyone in a sample (group-level) or just to one person (individual-level). However, there are outstanding questions about the validity and stability of these estimates, including: 1) how recovery of connection directionality is affected by features of data sets such as scan length and autoregressive effects, which may be strong in some imaging modalities (resting state fMRI, fNIRS) but weaker in others (task fMRI); and 2) whether inferences about directionality at the group and individual levels are stable across time. This talk will provide an overview of the GIMME framework and describe relevant results from a large-scale simulation study that assesses directionality recovery under various conditions and a separate project that investigates the temporal stability of GIMME’s inferences in the Human Connectome Project data set. Analyses from these projects demonstrate that estimates of directionality are most precise when autoregressive and cross-lagged relations in the data are relatively strong, and that inferences about the directionality of group-level connections, specifically, appear to be stable across time. Implications of these findings for the interpretation of directional connectivity estimates in different types of neuroimaging data will be discussed.

Behavior arises from the coordinated activity of numerous distinct brain regions. Modern experimental tools allow access to neural populations brain-wide, yet understanding such large-scale datasets necessitates scalable computational models to extract meaningful features of inter-region communication. In this talk, I will introduce Current-Based Decomposition (CURBD), an approach for inferring multi-region interactions using data-constrained recurrent neural network models. I will first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. I will then apply CURBD to understand the brain-wide flow of information leading to behavioral state transitions in larval zebrafish. These examples will establish CURBD as a flexible, scalable framework to infer brain-wide interactions that are inaccessible from experimental measurements alone.

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this powerful framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.

To understand the function of the brain and how its dysfunction leads to brain diseases, it is essential to have a deep understanding of the cell type composition of the brain, how the cell types are connected with each other and what their roles are in circuit function. At the Allen Institute, we have built multiple platforms, including single-cell transcriptomics, single and multi-patching electrophysiology, 3D reconstruction of neuronal morphology, high throughput brain-wide connectivity mapping, and large-scale neuronal activity imaging, to characterize the transcriptomic, physiological, morphological, and connectional properties of different types of neurons in a standardized way, towards a taxonomy of cell types and a description of their wiring diagram for the mouse brain, with a focus on the visual cortico-thalamic system. Building such knowledge base lays the foundation towards the understanding of the computational mechanisms of brain circuit function.

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we can ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.

The "mind of the worm", the first electron microscopy-based connectome of C. elegans, was an early sign of where connectomics is headed, followed by a long time of little progress in a field held back by the immense manual effort required for data acquisition and analysis. This changed over the last few years with several technological breakthroughs, which allowed increases in data set sizes by several orders of magnitude. Brain tissue can now be imaged in 3D up to a millimeter in size at nanometer resolution, revealing tissue features from synapses to the mitochondria of all contained cells. These breakthroughs in acquisition technology were paralleled by a revolution in deep-learning segmentation techniques, that equally reduced manual analysis times by several orders of magnitude, to the point where fully automated reconstructions are becoming useful. Taken together, this gives neuroscientists now access to the first wiring diagrams of thousands of automatically reconstructed neurons connected by millions of synapses, just one line of program code away. In this talk, I will cover these developments by describing the past few years' technological breakthroughs and discuss remaining challenges. Finally, I will show the potential of automated connectomics for neuroscience by demonstrating how hypotheses in reinforcement learning can now be tackled through virtual experiments in synaptic wiring diagrams of the songbird basal ganglia.

Many networks in the brain are sparsely connected, and the brain eliminates synapses during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a synapse between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of information flow between them. Noise is ubiquitous in neural systems, and often considered an irritant to be overcome. In the first part of this talk, I will suggest that noise could play a functional role in synaptic pruning, allowing the brain to probe network structure and determine which synapses are redundant. I will introduce a simple, local, unsupervised plasticity rule that either strengthens or prunes synapses using only synaptic weight and the noise-driven covariance of the neighboring neurons. For a subset of linear and rectified-linear networks, this rule provably preserves the spectrum of the original matrix and hence preserves network dynamics even when the fraction of pruned synapses asymptotically approaches 1. The plasticity rule is biologically-plausible and may suggest a new role for noise in neural computation. Time permitting, I will then turn to the problem of extracting structure from neural population data sets using dimensionality reduction methods. I will argue that nonlinear structures naturally arise in neural data and show how these nonlinearities cause linear methods of dimensionality reduction, such as Principal Components Analysis, to fail dramatically in identifying low-dimensional structure.