Halifax, Canada: August 29, 2014

Biomag 2014 satellite meeting: Brainstorm community workshop.

Download slides

Francois Tadel: Brainstorm introduction
PowerPoint

Sheraz Khan: Spectral signatures of brain network development
PowerPoint

Dimitrios Pantazis: Expanding the limits of imaging technologies
PowerPoint

John Mosher: Source modeling of invasive EEG data
Keynote 06 Keynote 09 PowerPoint

General information

Where:
Kenneth Rowe building, University Avenue, Dalhousie University
Google maps

When:
Friday, August 29, 8am-6pm

Objectives:
This satellite workshop will feature both oral communications from Brainstorm users and a hands-on training course. It is a unique opportunity for the Biomag community to provide feedback on the software, to help its developers improve and provide new features, and to learn about current and future developments.

Organizers:
Francois Tadel, Elizabeth Bock & Sylvain Baillet (MNI/McGill), John Mosher (Cleveland Clinic)

Target audience:
MEG and EEG users interested in presenting or discussing their results using Brainstorm and/or learning about the software basic and more advanced elements.

Participation fees: 45 CAD

Estimated attendance: 40

Requirements

The participants are required to bring a laptop (an external mouse will add to your comfort). In order to make the session as efficient as possible, we ask all the attendees to download, install and test the software and sample dataset on their laptops prior to the workshop.

Please read carefully the following instructions:
How to prepare your laptop for the training

Workshop program

08:00-08:30: Laptop clinic

• Come early for assistance in installing the material for the training session

08:30-9:30: Brainstorm overview

• Software architecture
• Typical data workflow

9:30-11:00: Hands-on training begins

• Database explorer
• Importing MRI volumes, surfaces and atlases
• Introduction to anatomical atlases
• Co-registration MEG/MRI
• Reviewing continuous recordings
• Correcting stimulation delays
• Artifact detection and correction (60Hz, blinks, heartbeats)
• Identification of bad channels and bad segments

11:00-11:15: Coffee break

11:15-13:00: Hands-on training

• Epoching and averaging
• Observation of a typical auditory evoked response
• Head modeling, cortical source reconstruction
• Use of MEG empty room measurements for noise normalization
• Definition of ROIs, tracking the processing of the sensory information at the cortex level
• Dipole scanning
• Time-frequency analysis

13:00-14:00: Lunch break

14:00-15:30: User symposium: Presentations of recent research from Brainstorm users

• John Mosher (Cleveland Clinic)
  Source modeling of invasive EEG data

• Sheraz Khan (MGH, Boston)
  Spectral signatures of brain network development

• Dimitrios Pantazis (MIT, Boston)
  Expanding the limits of imaging technologies:
  Merging MEG and fMRI using representational similarity analysis

• Full abstracts: See below

15:30-16:00: Coffee break

16:00-18:00: Advanced Brainstorm features

• Functional connectivity
• Phase-amplitude coupling
• Sub-cortical atlases and advanced source modeling
• Group analysis and statistics
• Advanced scripting interface
• Other topics on participants demand

User symposium abstracts

John Mosher (Cleveland Clinic)
Source modeling of invasive EEG data

• Patients with epilepsy who are surgical candidates may have dozens of EEG contacts inserted into their brain, in order to record abnormal activity and assist in the determination of the surgical plan. Brainstorm has the unusual ability to model the lead fields of these deep electrodes, allowing source modeling techniques to be applied to these data. We review the setup, import, modeling, and display of sources from an exemplar recording.

Dimitrios Pantazis (MIT, Boston)
Expanding the limits of imaging technologies: Merging MEG and fMRI using representational similarity analysis

• A comprehensive understanding of the neural basis of brain function requires resolving both the spatial and temporal dynamics of cortical processing. The most common type of brain scan, fMRI, identifies the anatomical substrate of neuronal activation, but is too slow to capture brain dynamics. MEG measures neuronal activation with a millisecond accuracy, but has a highly non-isotropic spatial resolution and does not reveal the precise location of these signals. I will describe a fundamentally novel way to combine MEG and fMRI based on a computational method called representational similarity analysis (RSA). The key idea relies on the fact that if two different stimuli (such as face images) evoke similar signals in MEG, they will also evoke similar signals in fMRI. We introduced the technique in our recent work [Cichy, Pantazis, Oliva, Nature Neuroscience, 2014], proving that this approach is not only possible, but could pave the way to rich experimental designs and many novel findings.

Sheraz Khan (MGH, Boston)
Spectral signatures of brain network development

• Here we present results, how the topology of the brain networks changes over the Human life cycle as infer from graph theory measures.

• Introduction: Understanding the intricate connectivity changes in brain networks that occur over the lifespan will not only help to define what constitutes normal brain development and aging, but also provide benchmarks against which to assess what goes awry in developmental and psychiatric disorders. Existing knowledge on the development of brain networks is based on fMRI studies that demonstrate that brain networks show a higher level of local processing in children. With maturity, the processing shifts into a globally distributed network. However, since fMRI is an indirect correlate of neural activity, here we have investigated whether physiologically relevant oscillations measured directly with MEG, contribute to brain network development.

• Materials & Methods: We used network analysis in resting state MEG data from 71 healthy subjects divided in to three groups (8-12 years, n=23; 13-17 years, n=19; 18-47 years, n=29) to investigate how oscillations in different frequency bands contribute to developmental changes in brain networks. Connectivity was measured between 450 cortical regions by measuring temporal correlation in amplitude of MEG signals and analyzed with network theory and Graph Analysis.

• Results: At the group mean level, the spatial pattern of intrinsic connections were markedly different for different frequency bands. Developmentally, our grouping showed an age related decrease in efficiency of local networks connected by gamma (F=8.44, p=0.001), alpha (F=3.9, p=0.026), delta (F=9.6, p<0.0001) and theta bands (F=6.7, p<0.0001). In contrast, efficiency of globally distributed brain networks increased for gamma (F=4.25,p=0.02) and delta (F=5.7,p=0.05) bands (Effect: F=7.1, p<0.0001;network density=5%).

• Discussion: This is the first evidence to show that specific neuronal frequencies mediate developmental changes in brain networks from local to a global topology. These findings have significant implications for characterizing the normative range for brain network maturation. These benchmarks will have utility in early detection of neurodevelopmental disorders like Autism and ADHD.