Filter by Year: 2016 | 2017 | 2018 | 2019 | All Years


Add a validate_sequences() module to pybids that creates user-friendly summary data frames to check whether a BIDS data set: 1. Contains any files with duplicate content. 2. Is missing files that are expected to exist. 3. Has more files than are expected for a given subject.


AWSome stuff, but not really, it’s more like rAWSignal or nAWSombody else should figure out AWS Lambda Team: Daniel Moyer

Brain age

How old is your brain? The current project implements Support Vector Regression (SVR) to predict #brain-age from structural MRI and resting-state fMRI separately in neurotypical and clinical populations.


Train a simple convolutional neural network to classify brain CT images has having an intracranial hemorrhage or not. Determine and select usable scans and organize them in the BIDS format

Data Driven Ontology

This is a text mining project that uses automatic web scraping of relevant literature given terms of interest from the Cognitive Atlas (Poldrack et al., 2011). It includes a few tools from natural language processing to analyse and visualize the resulting corpus of literature. Contributors: Tom Donoghue, Ayala Allon, Basak Kilic, Eric Reavis, Mengya Xhiang, […]

fMRI Avalanches

A Python package for doing point process analyses on fMRI data. Built at Neurohackweek 2017 by: Asier Erramuzpe Jessica Dafflon Dan Lurie Brian Roach


A meta-project for examining Neurohackademy by the (wildly oversimplified and non-representative) numbers

KIDS: Building an ABIDE Classifier

Flexible script(s) development for easy model building Give us: CSV file with Subject IDs and Labels We’ll give you: Trained model(s) Performance metrics Predictions NIfTI weight maps Scikit learn implementations of whole-brain classifiers Template system that can be used by others to incorporate additional models Easy to “drop into” cloud-computing analysis frameworks (or use locally)

Loops are Evil!?

Loops are Evil!? Avoid Loops!? We listen or read this affirmation everywhere. The goal is to learn Numpy advanced features and see the benefit/drawback. We decide to implement a denoising algorithm (non local means – 6 loops) without any loop.


NeuroImage is a next-gen web viewer for visualization of neuroimaging data. Or at least it will be, one day. Maybe.

Neuroimaging Widgets

This provides easy and general wrappers to display interactive widgets that visualize standard-format neuroimaging data, using new functions and standard functions from other libraries.


We dockerized PALS with a GUI, refactored PALS with nipype (in progress), incorporated ni-learn for QC images


Open-access articles were scraped from the PubMed Central API. Their full-text was searched for keywords (and phrases) indicative of code and data sharing. The prevalence of code and data sharing (and preregistration) was computed as a measure of the “openness” of the research published in that journal (it’s “O-Factor”).

Parcellation Fragmenter

Fragments a surface mesh into N-equally sized annotation parcels. Besides looking beautiful, such surface parcellation could be used as feature extract for machine learning or functional connectivity approaches.


Interactive brain viewer and scatterplot visualization of univariate and multivariate neuroimage analyses.


Interactive brain viewer and scatterplot visualization of univariate and multivariate neuroimage analyses.


Broadly, examine how neural representations of characters and emotions are shared between observers, change with experience, and track with other aspects of physiology.