Neuro-Informatics seminars

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

We will discuss a recent paper by Taylor et al. (2023): https://www.sciencedirect.com/science/article/pii/S1053811923002896. They discuss the merits of highlighting results instead of hiding them; that is, clearly marking which voxels and clusters pass a given significance threshold, but still highlighting sub-threshold results, with opacity proportional to the strength of the effect. They use this to illustrate how there in fact may be more agreement between researchers than previously thought, using the NARPS dataset as an example. By adopting a continuous, "highlighted" approach, it becomes clear that the majority of effects are in the same location and that the effect size is in the same direction, compared to an approach that only permits rejecting or not rejecting the null hypothesis. We will also talk about the implications of this approach for creating figures, detecting artifacts, and aiding reproducibility.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.

Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.

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.

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.

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.

Large-scale, single neuron resolution recordings are inherently high-dimensional, with as many dimensions as neurons. To make sense of them, for many the answer is: reduce the number of dimensions. In this talk I argue we can distinguish weak and strong principles of neural dimension reduction. The weak principle is that dimension reduction is a convenient tool for making sense of complex neural data. The strong principle is that dimension reduction moves us closer to how neural circuits actually operate and compute. Elucidating these principles is crucial, for which we subscribe to provides radically different interpretations of the same dimension reduction techniques applied to the same data. I outline experimental evidence for each principle, but illustrate how we could make either the weak or strong principles appear to be true based on innocuous looking analysis decisions. These insights suggest arguments over low and high-dimensional neural activity need better constraints from both experiment and theory.

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

Over the past years, genetic studies have uncovered hundreds of genetic variants to be associated with complex brain disorders. While this really represents a big step forward in understanding the genetic etiology of brain disorders, the functional interpretation of these variants remains challenging. We aim to help with the functional characterization of variants through transcriptomic data analysis. For instance, we rely on brain transcriptome atlases, such as Allen Brain Atlases, to infer functional relations between genes. One example of this is the identification of signaling mechanisms of steroid receptors. Further, by integrating brain transcriptome atlases with neuropathology and neuroimaging data, we identify key genes and pathways associated with brain disorders (e.g. Parkinson's disease). With technological advances, we can now profile gene expression in single-cells at large scale. These developments have presented significant computational developments. Our lab focuses on developing scalable methods to identify cells in single-cell data through interactive visualization, scalable clustering, classification, and interpretable trajectory modelling. We also work on methods to integrate single-cell data across studies and technologies.

I will introduce a web portal for the retinal neuroscience community to explore the catalog of mouse retinal ganglion cell (RGC) types, including data on light responses, correspondences with morphological types in EyeWire, and gene expression data from single-cell transcriptomics. Our current classification includes 43 types, accounting for 90% of the cells in EyeWire. Many of these cell types have new stories to tell, and I will cover two of them that represent opposite ends of the spectrum of levels of analysis in my lab. First, I will introduce the “Bursty Suppressed-by-Contrast” RGC and show how its intrinsic properties rather than its synaptic inputs differentiate its function from that of a different well-known RGC type. Second, I will present the histogram of cell types that project to the Olivary Pretectal Nucleus, focusing on the recently discovered M6 ipRGC.