[BIC-announce] Fwd: Brownbag which might interest QLS members

[JB Poline]

FYI

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From: QLS Coordinator <coordinator.qls at mcgill.ca>
Date: Mon, Jan 20, 2020 at 9:55 AM
Subject: Brownbag which might interest QLS members
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*McGill Quantitative Psychology Brownbag Series*



*Title: Task-state functional brain networks detectable by fMRI using
constrained principal component analysis: More than just a pretty picture*



*Speaker*: Dr. Todd Woodward (Psychiatry, University of British Columbia)



*Time*: 10:00 – 11:00 am, Thursday January 23



*Place*: 2001 McGill College, Room 464



*Abstract:* Characterization of brain networks using functional magnetic
resonance imaging (fMRI) has primarily been advanced by resting-state
research; however, using task-based research, functional characterizations
can be more robustly determined by observing how the timing of
network-level evoked hemodynamic responses (HDRs) differ between task
conditions. To this end, our laboratory has developed a novel approach
which integrates the following principles: (1) as opposed to voxel-by-voxel
univariate analyses, use multivariate/multidimensional analysis methods
computing networks based on the dominant pattern of intercorrelations
between voxels; (2) as opposed to mixing task-related and task-unrelated
variance in brain activity, extract the task-related variance prior to
network extraction; (3) as opposed to selecting brain regions of interest
(ROIs), compute networks that allow every voxel to participate in every
brain network; (4) as opposed to assuming HDR shapes, use data-driven
explorations of HDR shapes, allowing separate HDR shapes for every subject,
network and task condition separately (Finite Impulse Response [FIR]
model). Principles (1) and (2) can be achieved by applying constrained
principal component analysis (CPCA) to fMRI data (fMRI-CPCA). Principles
(3) and (4) are achieved by decisions about the content of the matrices
submitted to fMRI-CPCA. This line of research has led to the identification
of a set of 10 core task-based fMRI networks, a subset of which are
retrieved from all task-based fMRI data, regardless of the specific task.
Based on the experimental conditions to which they respond, we have
assigned a preliminary cognitive function to each of these interacting
networks. Some of them are already familiar to the field from resting state
studies (e.g., default mode network, response network), but others are
novel and specific to the task state (e.g., cognitive evaluation,
volitional attention to internal representations). Extended applications of
CPCA models to fMRI, MEG, and EEG data are also discussed.





McGill Quant Psych Brownbag:
https://sites.google.com/view/hwanglab/quant-psych-seminar



==================================================
Heungsun Hwang, Ph.D.

Professor, Department of Psychology, McGill University

2001 McGill College Avenue, Room 710

Montreal, QC H3A 1G1, Canada
Tel: 514-398-8021       Fax: 514-398-4896
Lab: sites.google.com/view/hwanglab
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