Topic: Human Connectome Project

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 dyna

Hemispheric asymmetry in functional connectivity of the orbitofrontal cortex (OFC) has been recently demonstrated in reward-related cognitive tasks like gambling. In this work, which was part of our Neuromatch Academy 2022 project, we used resting state and task-dependent fMRI data from 339 human subjects from the Human Connectome Project (HCP 2020) to quantify the asymmetry of functional connectivity between left and right OFC, and posterior OFC (pOFC) regions during rest and task blocks. We then aimed to relate this asymmetry index (AI) to reward-driven impulsivity traits, quantified by subj

Generalized linear models (GLMs) are a gold-standard in computational neuroscience, allowing for the statistical investigation of neural activity when a population of neurons is exposed to stimuli (Gerwinn, 2010 et al.). Logistic regression models are one of such GLMs that allow for the prediction of two possible outcomes (i.e. binary dependent variables) based on predictor variables (i.e. the neural activity). One of such neural activity of interest is the one produced during language-related tasks in a study done by the Human Connectome Project (Binder et. al, 2020). In this study, the subje

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 dir

Long-range temporal dependencies (LRTD) are an important signature of computation in the brain. They were shown to be decreased by Alzheimer’s and increased by epilepsy. An established way of capturing the LRTDs is to use Hurst exponents, for which estimation various methodologies were proposed in different fields of study. In neuroscience, LRTDs have been measured in broad-band electro- and magnetoencephalography (E/MEG) [2] (Fig. 1h), amplitude envelopes of oscillations [1] (Fig. 1g), and functional Magnetic Resonance Imaging (fMRI) [3] (Fig. 1f) using several existing methods. Theoretical w