Neuro-Informatics seminars

Functional connectomics reveals general wiring rule in mouse visual cortex

Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Connectomic reconstruction of a cortical column

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.

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..

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

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.

Lead author Mehdi Azabou will present on his work "POYO-1: A Unified, Scalable Framework for Neural Population Decoding" (https://poyo-brain.github.io/). Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Paper link: https://arxiv.org/abs/2310.16046 Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

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.

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.

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.

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.