Methods Lunch

Methods Lunch is a biweekly lunch meeting for interested PIs, Postdocs, PhD and MSc students. The meeting provides a platform for:

  1. Neuroscientists who would like to discuss and receive input for the design and analysis of their behavioural, neuroimaging or neurophysiological experiment.

  2. Computational scientists present new methods to see if they can be useful to the neuroscience community.

  3. Discussing new method papers in Computational Neuroscience / Computational brain imaging.

Meetings are being held every other Monday from 12:30-2:00 pm in WIRB 4190 (lunchroom)Bring your own lunch!

Upcoming Lunches:




03/30/2020 Lien Peters, Eric Wilkey, Suzanne Witt, Ali Khan

Barriers to Open Science: a roundtable discussion

Past Lunches:




02/10/2020 Joe Gati, Kyle Gilbert

Annual CFMM 7T users roundtable

12/16/2019 Marieke Mur

Analyzing multivariate responses patterns with factorial models

12/02/2019 Steve Van Hedger, Paul Minda

An introduction to running web-based experiments using PsychoPy, jsPsych, and Pavlovia

Supplementary material: 

Using PsychoPy and Pavlovia

Web-based research studies

11/04/2019 HU Yang, Jörn Diedrichsen

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

Supplementary material: 

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

10/28/2019 Igor Solovey, Alan Kuurstra

Introduction to GIT

Supplementary material: 

GIT: version-control system

10/07/2019 Suzanne Witt

An introduction to fmriPrep

Supplementary material: 

fMRIPrep: a robust preprocessing pipeline for functional MRI


09/30/2019 Spencer Arbuckle, Jörn Diedrichsen

How to implement a (valid) shuffling or randomization test for your experiment.

09/23/2019 Suzanne Witt

BIDS (Brain Imaging Data Structure): what, why, and how

Supplementary material:

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography

Brain Image Data Structure (BIDS): what, why, and

09/09/2019 Suzanne Witt

Welcome back: soliciting suggestions for future Methods Lunches

06/24/2019 J. Bruce Morton, Daamoon Ghahari

Singular value decomposition (SVD) analysis

Supplemental material:

PowerPoint Slides   


06/17/2019 Marieke Mur, Spencer Arbuckle

Analysis of neuronal population dynamics II

Supplemental material: 

Neural population dynamics during reaching

Demixed principal component analysis of neural population data

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis

05/27/2019 Jorn Diedrichsen, Suzanne Witt

Surface-based analysis toolbox


06/03/2019 Marieke Mur, Spencer Arbuckle

Analysis of neuronal population dynamics I
Supplemental material


03/18/2019 Marieke Mur

Representational component modeling

03/04/2019 Round table discussion

CFMM 7T MRI users round table

02/25/2019 Marieke Mur

Deep neural networks

02/11/2019 Suzanne Witt

NiPy - Neuroimaging Python Package
Supplementary material

02/04/2019 Suzanne Witt

Part I - Welcome back / Part II - How to register your study for automatic BIDS conversion
Supplementary material  

01/28/2019 Ladan Shahshahani, Jörn Diedrichsen

Time Delay Analysis
Supplementary material

12/17/2018 Nicolette Noonan

A general introduction and demo for fNIRS
Presentation Slides

12/10/2018 Jody Culham and Ethan Jackson

Overview of fMRI course tutorials

11/26/2018 Olivia Stanley and Jordan DeKraker

Introduction to fMRI prep pipeline
Presentation Slides

11/19/2018 Jörn Diedrichsen, Daniel Ansari, and Erin Heerey

Bayesian ANOVA II

11/12/2018 Jörn Diedrichsen, Daniel Ansari, and Erin Heerey

Bayesian ANOVA I
Supplementary material              

11/05/2018 Eva Berlot, Jörn Diedrichsen

Multivariate Pattern Connectivity 
Main paper                  

10/11/2018 Patrick Park

An introduction to BIDS

10/01/2018 Jörn Diedrichsen and Björn Herrmann

Mixed models

Main paper

Jörn Diedrichsen

Encoding models, pattern component modeling, and a little bit of RSA

Main paper

4/03/2017 Etay Hay

Multiregional integration of brain fMRI activity

Main paper

Add to calendar

3/27/2017 Andrea Soddu

A method for independent component graph analysis of resting-state fMRI

Main paper

Add to calendar

01/30/2017 Marc Joanisse

Task-free fmri predicts individual differences in brain activity during task performance

01/23/2017 Emma Holmes

Neural representations of familiar voices

11/07/2016 Culham Lab Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
Main Paper
10/24/2016 Alex Billig / Johnsrude Decoding neural representations of ambiguous speech
10/17/2016 Cusak Lab Parametric statistics for inter-subject correlations 
10/03/2016 Diedrichsen Lab Could a Neuroscientist understand a microprocessor? 
Main Paper
6/06/16 Naveed Ejaz (Diedrichsen lab) Neural correlates of single-vessel haemodynamic responses in vivo
Main Paper
4/11/16 Ali Khan & Jon Lau Regional homogeneity as a functional marker in epilepsy
4/04/16 Cusack & Diedrichsen How to best model temporal autocorrelations for fast functional acquisitions
3/28/16 Katheryn Manning Longitudinal repeated-measures study designs: how to analyze efficiently with missing data
3/14/16 Diedrichsen Randomisation testing
3/07/16 Avital Sternin Music Perception & Deep Learning Classification
2/08/16 Jessica Grahn Discussion of the paper: Norman-Haignere S, Kanwisher NG, McDermott JH (2015) Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition. Neuron 88:1281-1296.
1/18/16 Bruce Morton A pipeline for conducting whole-brain dynamic functional connectivity fMRI analyses: when it works and when it breaks down
12/7/15 Diedrichsen Lab Representational Spaces
11/23/15 Martinez Lab Multivariate Analysis of Spiking Data