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= Halifax: June 21, 2013 = HBM 2013 satellite meeting, full-day Brainstorm workshop. [[http://www.biomag2014.org|Conference link]] |
= Halifax, Canada: August 29, 2014 = [[http://www.biomag2014.org|Biomag 2014]] satellite meeting: Brainstorm community workshop. == Download slides == '''Francois Tadel''': Brainstorm introduction<<BR>><<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_tadel.pptx');">PowerPoint</A>)>> '''Sheraz Khan''': Spectral signatures of brain network development<<BR>><<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_khan.pptx');">PowerPoint</A>)>> '''Dimitrios Pantazis''': Expanding the limits of imaging technologies<<BR>><<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_pantazis.pptx');">PowerPoint</A>)>> '''John Mosher''': Source modeling of invasive EEG data<<BR>><<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_mosher_key06.key.zip');">Keynote 06</A>)>> <<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_mosher_key09.key');">Keynote 09</A>)>> <<HTML(<A HREF='javascript:' onClick="javascript: window.open('http://neuroimage.usc.edu/bst/download.php?file=slides_biomag2014_mosher.pptx.zip');">PowerPoint</A>)>> |
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'''Where:''' TBA. | '''Where:''' <<BR>>Kenneth Rowe building, University Avenue, Dalhousie University<<BR>>[[https://www.google.ca/maps/dir/44.6485466,-63.5755305/Social+Media+Lab,+6100+University+Ave+#2010,+Halifax,+NS+B3H+4R2/@44.6437297,-63.5880143,15z/data=!4m9!4m8!1m0!1m5!1m1!1s0x4b5a2224d4dae1c1:0x690f55ecff7fab8!2m2!1d-63.58833!2d44.6371!3e3|Google maps]] |
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'''When:''' Saturday, August 23, 8am-6pm | '''When:''' <<BR>>Friday, August 29, 8am-6pm |
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'''Organizers: '''Francois Tadel (MNI, !McGill) | '''Objectives''':<<BR>>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. |
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'''Instructors:''' Francois Tadel (MNI, !McGill), Elizabeth Bock (MNI, !McGill), John Mosher (Cleveland Clinic) | '''Organizers:'''<<BR>>Francois Tadel, Elizabeth Bock & Sylvain Baillet (MNI/McGill), John Mosher (Cleveland Clinic) |
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'''Target audience: '''Neuroscientists, psychologists, clinicians, engineers, etc. using MEG or EEG as advanced research tools. Students, post-docs, staff, faculty interested in learning a new academic software tool for MEG and EEG data analysis, with an emphasis on source imaging, spectral decompositions and functional connectivity metrics. | '''Target audience:'''<<BR>>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 <<HTML(<FONT color=red>)>> '''bring a laptop''' <<HTML(</FONT>)>> (an external mouse will add to your comfort). In order to make the session as efficient as possible, we ask all the attendees to <<HTML(<FONT color=red>)>>'''download, install and test'''<<HTML(</FONT>)>> the software and sample dataset on their laptops prior to the workshop. Please read carfully the following instructions:<<BR>>[[WorkshopBiomag2014Install|How to prepare your laptop for the training]] |
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<<HTML(<FONT color=red>)>>'''Requirements'''<<HTML(</FONT>)>> | '''08:00-08:30: L''''''aptop clinic''' |
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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 carfully the following instructions:<<BR>>[[WorkshopSeattle2013Install|How to prepare your laptop for the training]]<<BR>><<BR>> | * Come early for assistance in installing the material for the training session |
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'''08:00-09:00:''' ''' Breakfast ''''''and laptop clinic''' * Come early you are not sure you were successful in installing the material for the training session '''09:00-10:00''': '''Brainstorm overview''' |
'''08:30-9:30''': '''Brainstorm overview''' |
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'''10:00-10:45: Hands-on training begins''' | '''9:30-11:00: Hands-on training begins''' |
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'''10:45-11:00: Coffee break<<BR>>''' '''11:00-12:40: Hands-on training<<BR>>''' |
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* Artifact detection and correction * Filtering and epoching * Averaging, observation of a typical somato-sensory evoked response |
* 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 |
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* 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 |
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__'''12:40-13:30: Lunch break'''__ (lunch not provided by the organizers, cafeteria nearby) | __'''13:00-14:00: Lunch break'''__ |
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'''13:30-15:30: Hands-on training resumes''' | '''14:00-15:30: User symposium: '''Presentations of recent research from Brainstorm users |
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* Definition of ROIs, tracking the processing of the sensory information millisecond by millisecond at the cortex level * Frequency and time-frequency analysis |
* '''John Mosher''' (Cleveland Clinic)<<BR>>''Source modeling of invasive EEG data'' * '''Sheraz Khan''' (MGH, Boston)<<BR>>''Spectral signatures of brain network development'' * '''Dimitrios Pantazis''' (MIT, Boston)<<BR>>''Expanding the limits of imaging technologies:<<BR>>Merging MEG and fMRI using representational similarity analysis'' * Full abstracts: [[http://neuroimage.usc.edu/brainstorm/WorkshopBiomag2014#User_symposium_abstracts|See below]] '''15:30-16:00: Coffee break''' '''16:00-18:00: Advanced Brainstorm features''' |
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* Phase-amplitude coupling * Sub-cortical atlases and advanced source modeling |
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* Other topics on participants demand | |
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'''15:30-16:00: Coffee break''' | |
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'''16:00-16:45: EEG and epilepsy '''(Lecture) | |
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* John Mosher will share his experience at the Cleveland Clinic's Epilepsy center and illustrate the use of Brainstorm in a clinical environment. * How to process epilepsy recordings in Brainstorm: MEG, EEG, and intra-cranial recordings. |
== User symposium abstracts == '''John Mosher''' (Cleveland Clinic)<<BR>>''Source modeling of invasive EEG data'' |
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'''16:45-18:00: Open discussion''' | . 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. |
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* Other topics can be discussed depending on the audience specific requests | '''Dimitrios Pantazis''' (MIT, Boston)<<BR>>''Expanding the limits of imaging technologies: Merging MEG and fMRI using representational similarity analysis'' |
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== Registration form == Registration is closed for this event. |
. 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. |
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. <<EmbedContent(http://neuroimage.usc.edu/bst/workshop.js)>> . <<HTML(<!-- div id="bst_table" style=""><FORM METHOD="post" ACTION="http://neuroimage.usc.edu/brainstorm3_register/workshop_reg.php" name="form" onSubmit="return validateForm(this);"><TABLE><TR><TD align=right>First name * </TD> <TD><INPUT type="text" name="firstname" size=40></TD></TR> <TR><TD align=right>Last name * </TD> <TD><INPUT type="text" name="lastname" size=40></TD></TR> <TR><TD align=right>Title / Occupation * </TD> <TD><INPUT type="text" name="title" size=40></TD></TR> <TR><TD align=right>Institution * </TD> <TD><INPUT type="text" name="institution" size=40></TD></TR> <TR><TD align=right>City * </TD> <TD><INPUT type="text" name="city" size=40></TD></TR> <TR><TD align=right>Email address * </TD> <TD><INPUT type="text" name="email" size=40></TD></TR> <TR><TD align=right>Phone </TD> <TD><INPUT type="text" name="phone" size=40></TD></TR></TABLE> <BR> <BR> <INPUT type="submit" value="Register now!" style="width:200px"> <A HREF="WorkshopBiomag2014Reg">View list of registered participants </A> <BR><BR><BR><INPUT type="hidden" name="eventname" value="Biomag2014"> </FORM> </div -->)>> |
'''Sheraz Khan''' (MGH, Boston)<<BR>>''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. |
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 carfully 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 dataSheraz Khan (MGH, Boston)
Spectral signatures of brain network developmentDimitrios Pantazis (MIT, Boston)
Expanding the limits of imaging technologies:
Merging MEG and fMRI using representational similarity analysisFull 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.