Shanny FooMcGill University | McGill · Department of Neurology and Neurosurgery
Shanny Foo
Doctor of Philosophy
Linear mixed modelling for large longitudinal datasets in clinical populations (neuroimaging, biodata, behaviour).
About
8
Publications
1,142
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Citations
Introduction
Shanny Foo is currently a PhD Candidate at The Montreal Neurological Institute at McGill University. Shanny does research in Neuroimaging, Cognitive Neuroscience, Social Cognition, and Longitudinal Modelling in both healthy and patient populations.
Additional affiliations
January 2021 - present
September 2018 - December 2020
March 2018 - August 2018
Position
- Research Assistant
Description
- NSERC USRA Research Internship Cognitive Aging Lab Project I: Meta-cognitive control and monitoring of emotional memory during value-directed learning Project II: Age Differences in value-directed learning and memory
Education
September 2020 - August 2025
September 2018 - June 2020
September 2014 - May 2018
Publications
Publications (8)
BACKGROUND AND AIMS: A robust feature characterizing frontotemporal dementia from other dementias are early deficits in social cognition. The salience and default networks, comprising groups of brain regions studied using resting-state functional connectivity (RSFC), are linked to the processing of emotionally salient stimuli and inferring others’...
INTRODUCTION. The salience network (SN) is central for processing salient stimuli and plays an interactive role with neurocognitive networks relevant to goal-oriented behaviour. A robust feature characterising frontotemporal dementia (FTD) from other dementias are early deficits in social cognition and atrophy in the anterior insula (aINS). Resting...
(Awarded Best Poster at 5th Annual Healthy Brains, Healthy Lives 2023 Research Symposium).
BACKGROUND AND AIM. The salience network (SN), comprising the anterior insula, dorsal anterior cingulate and other structures, is central in the processing of emotionally salient stimuli and directly influences other neurocognitive networks relevant to goal...
Frontotemporal dementia (FTD) is a neurodegenerative disease impacting 50% of people with dementia under the age of 60. A core feature of FTD is a deficit in social cognition. The salience network, a functionally connected assembly of brain regions including the anterior insula (aINS) and anterior cingulate, is impacted by FTD. The Genetic FTD Init...
The discovery of movement-modulated 7-8 Hz theta rhythm in rodents provided one of the earliest indications of synchronicity of neuronal firing in the hippocampus. Subsequent research expanded on this finding on theta rhythm and its role in other domains such as spatial navigation and memory. Nevertheless, discrepancies among animal models posed th...
The hippocampus is a brain structure critical for learning and memory, including episodic, relational, and spatial memory. Previous research demonstrated a correlation between hippocampal grey matter and spatial memory. One of the first symptoms of Alzheimer's disease involves spatial memory impairments and the hippocampus and entorhinal cortex are...
This study examined the relationship between visuospatial memory strategies (egocentric and allocentric) and empathy styles (cognitive and affective). Positive correlations were hypothesized between egocentric strategies and cognitive empathy, as well as between allocentric strategies and affective empathy. Participants completed a virtual visuospa...
Visuospatial memory strategies and empathy, although disparate constructs, appear to share perspective-taking as a common underlying cognitive process. Previous literature suggests behavioural and brain activation differences between egocentric (i.e., self-referential) and allocentric (i.e., environmentally-cued) visuospatial strategies. Similar pa...
Questions
Questions (2)
I am looking at the cortical thickness of the anterior insula across healthy and patient populations. Freesurfer's default DK atlas includes all of insula. I have read that the Destrieux atlas output can also be generated from from Freesurfer but wanted to know whether it is more precise for this particular structure or advice on other parcellations?
I have a large longitudinal dataset comprising both healthy and clinical groups. The sample size for the clinical subsample drops off much more vs healthy subjects. There is missing data throughout the dataset, and across different measure types, and the amount of missingness varies by measure and time points. What are some of the best ways to address missing data for longitudinal modelling in neuroimaging, functional neuroimaging, and behavioural data? What kind of imputation methods are most robust?
