Neuro-Informatics seminars

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

In the age of connectomics, it is increasingly important to understand how the nodes and edges of a brain's anatomical network, or "connectome," gives rise to neural signaling and neural function. I will present the first comprehensive brain-wide cell-resolved causal measurements of how neurons signal to one another in response to stimulation in the nematode C. elegans. I will compare this signal propagation atlas to the worm's known connectome to address fundamental questions of structure and function in the brain.

A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.

This webinar will focus on spike sorting analysis with SpikeInterface, an open-source framework for the analysis of extracellular electrophysiology data. After a brief introduction of the project (~30 mins) highlighting the basics of the SpikeInterface software and advanced features (e.g., data compression, quality metrics, drift correction, cloud visualization), we will have an extensive hands-on tutorial (~90 mins) showing how to use SpikeInterface in a real-world scenario. After attending the webinar, you will: (1) have a global overview of the different steps involved in a processing pipeline; (2) know how to write a complete analysis pipeline with SpikeInterface.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

This set of short webinars will provide neuroscience researchers working in a neuroimaging setting with practical tips on strengthening credibility at different stages of the research project. Each webinar will be hosted by Cassandra Gould Van Praag from the Wellcome Centre for Integrative Neuroimaging.

It is now widely accepted that openness and transparency are keys to improving the reproducibility of scientific research, but many challenges remain to adoption of these practices. I will discuss the growth of an ecosystem for open science within the field of neuroimaging, focusing on platforms for open data sharing and open source tools for reproducible data analysis. I will also discuss the role of the Brain Imaging Data Structure (BIDS), a community standard for data organization, in enabling this open science ecosystem, and will outline the scientific impacts of these resources.

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.

Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.

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, 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.

Many of the collaborations we encounter in our scientific careers are centered on a common idea that can be associated with certain resources, such as a dataset, an algorithm, or a model. All partners in a collaboration need to develop a common understanding of these resources, and need to be able to access them in a simple and unambiguous manner in order to avoid incorrect conclusions especially in highly cross-disciplinary contexts. While digital computers have entered to assist scientific workflows in experiment and simulation for many decades, the high degree of heterogeneity in the field had led to a scattered landscape of highly customized, lab-internal solutions to organizing and managing the resources on a project-by-project basis. Only with the availability of modern technologies such as the semantic web, platforms for collaborative coding or the development of data standards overarching different disciplines, we have tools at our disposal to make resources increasingly more accessible, understandable, and usable. However, without overarching standardization efforts and adaptation of such technologies to the workflows and needs of individual researchers, their adoption by the neuroscience community will be impeded. From the perspective of computational neuroscience, which is inherently dependent on leveraging data and methods across the field of neuroscience for inspiration and validation, I will outline my view on past and present developments towards a more rigorous use of digital resources and how they improved collaboration, and introduce emerging initiatives to support this process in the future (e.g., EBRAINS http://ebrains.eu, NFDI-Neuro http://www.nfdi-neuro.de).

Open-source tools are gaining an increasing foothold in neuroscience. The rising complexity of experiments in systems neuroscience has led to a need for multiple parts of experiments to work together seamlessly. This means that open-source tools that freely interact with each other and can be understood and modified more easily allow scientists to conduct better experiments with less effort than closed tools. Open Ephys is an organization with team members distributed all around the world. Our mission is to advance our understanding of the brain by promoting community ownership of the tools we use to study it. We are making and distributing cutting edge tools that exploit modern technology to bring down the price and complexity of neuroscience experiments. A large component of this is to take tools that were developed in academic labs and helping with documentation, support, and distribution. More recently, we have been working on bringing high-quality manufacturing, distribution, warranty, and support to open source tools by partnering with OEPS in Portugal. We are now also establishing standards that make it possible to combine methods, such as miniaturized microscopes, electrode drive implants, and silicon probes seamlessly in one system. In the longer term, our development of new tools, interfaces and our standardization efforts have the goal of making it possible for scientists to easily run complex experiments that span from complex behaviors and tasks, multiple recording modalities, to easy access to data processing pipelines.

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this presentation, I will provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Kilosort is a spike sorting pipeline for large-scale electrophysiology. Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Kilosort is a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available.

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.

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.