Mapping body size estimation accuracy using a 3D avatar: an fMRI task proof of concept
Poster No:
981
Submission Type:
Abstract Submission
Authors:
Joel Diaz-Fong1,2, Sameena Karsan2, Madison Lewis2,3, Zeina Beidas2, Jamie Feusner1,2
Institutions:
1Institute of Medical Science, University of Toronto, Toronto, Canada, 2Centre for Addiction and Mental Health, Toronto, Canada, 3Department of Psychology, University of Toronto, Toronto, Canada
First Author:
Joel Diaz-Fong, MS
Institute of Medical Science, University of Toronto|Centre for Addiction and Mental Health
Toronto, Canada|Toronto, Canada
Institute of Medical Science, University of Toronto|Centre for Addiction and Mental Health
Toronto, Canada|Toronto, Canada
Co-Author(s):
Madison Lewis
Centre for Addiction and Mental Health|Department of Psychology, University of Toronto
Toronto, Canada|Toronto, Canada
Centre for Addiction and Mental Health|Department of Psychology, University of Toronto
Toronto, Canada|Toronto, Canada
Jamie Feusner, MD
Institute of Medical Science, University of Toronto|Centre for Addiction and Mental Health
Toronto, Canada|Toronto, Canada
Institute of Medical Science, University of Toronto|Centre for Addiction and Mental Health
Toronto, Canada|Toronto, Canada
Introduction:
Body image disturbance is a key characteristic of body dysmorphic disorder (BDD), an often-severe psychiatric disorder that affects 1 in 40 individuals (Phillips, 2004). Somatomap 3D is a digital avatar tool used to quantify body image disturbance through measuring body size estimation (BSE) accuracy. Somatomap 3D has been previously used to examine abnormalities in BSE accuracy among individuals with anorexia nervosa compared to healthy controls (Ralph-Nearman, 2021). However, its use during fMRI to probe body processing regions and networks involved in accurate internal representations of one's body has yet to be tested. Previous fMRI studies have established that tasks involving viewing body images result in brain activation in areas including the extrastriate body area (EBA; Downing, 2001), the fusiform body area (Peelen, 2005), somatosensory cortex (Saxe, 2006) and the temporoparietal junction (TPJ; Hamamoto, 2023). As an initial proof of concept, the current study examined brain activation related to BSE accuracy using Somatomap 3D in individuals with and without BDD. We hypothesized that body processing regions including the EBA, the fusiform body area, and the TPJ would be activated while completing the Somatomap task.
Methods:
Participants were recruited from the Greater Toronto Area. Twenty unmedicated adults (18-38 years, 24.55±5.74; 80% female) with BDD (n=6), sub-clinical BDD (n=5), and healthy controls (n=9) were included in this preliminary analysis.
During fMRI, participants used the Somatomap 3D tool to adjust 23 different body parts on the avatar to create, as accurately as possible, their current body's size and shape. Participants completed the task using an MR-compatible trackball. While completing the task, eye movements and mouse clicks were recorded and synchronized with the fMRI scan. Using mouse click timestamps, body size estimation was defined in the time-series as the periods when the participant was rotating the avatar or adjusting the avatar body size. The baseline contrast included intervening periods between these rotations/adjustments.
As this preliminary sample was small, it did not permit subgroup analyses or comparisons; we thus modeled responses across the whole group. Single group average fMRI data analysis was carried out using FSL FEAT. Z (Gaussianized T/F) statistic image were thresholded using clusters determined by Z>3.1 and a corrected cluster significance threshold of P=0.05. Peak MNI coordinates were identified using the Harvard-Oxford atlas. To identify activation patterns that overlap with the specific regions of interest involved in body processing, Neurosynth masks using the term "body" were generated and compared to the cluster results.
During fMRI, participants used the Somatomap 3D tool to adjust 23 different body parts on the avatar to create, as accurately as possible, their current body's size and shape. Participants completed the task using an MR-compatible trackball. While completing the task, eye movements and mouse clicks were recorded and synchronized with the fMRI scan. Using mouse click timestamps, body size estimation was defined in the time-series as the periods when the participant was rotating the avatar or adjusting the avatar body size. The baseline contrast included intervening periods between these rotations/adjustments.
As this preliminary sample was small, it did not permit subgroup analyses or comparisons; we thus modeled responses across the whole group. Single group average fMRI data analysis was carried out using FSL FEAT. Z (Gaussianized T/F) statistic image were thresholded using clusters determined by Z>3.1 and a corrected cluster significance threshold of P=0.05. Peak MNI coordinates were identified using the Harvard-Oxford atlas. To identify activation patterns that overlap with the specific regions of interest involved in body processing, Neurosynth masks using the term "body" were generated and compared to the cluster results.
Results:
Multiple statistically significant clusters were evident in the whole-brain, voxelwise analysis corresponding to the body part adjustment/rotation periods vs. baseline (Table 1). Specifically, the supramarginal gyrus and postcentral gyrus (right: voxels=3088, Z=5.82; left: voxels=302, Z=5.02), the precentral gyrus (right: voxels=1899, Z=6.05), the lateral occipital cortex (right: voxels=1811, Z=5.43; left: voxels=363, Z=5.46), the supplementary motor cortex (right: voxels=1048, Z=5.29), and the inferior frontal gyrus (right: voxels=417, Z=4.96). In addition, cluster activations overlapped with the EBA, fusiform body area, and insular cortex but not the TPJ.
Conclusions:
Although this preliminary sample size is modest, this proof-of-concept study suggests that BSE using Somatomap 3D is associated with brain activation patterns in known body processing regions such as the EBA, fusiform body area, insula, somatosensory, and inferior frontal brain regions. The study suggests Somatomap 3D is a task that may be used to examine neural mechanisms underlying BSE accuracy. As such, it may be useful to probe pathophysiological mechanisms associated with body image disturbance in those with body-image related disorders such as BDD and eating disorders.
Emotion, Motivation and Social Neuroscience:
Self Processes
Higher Cognitive Functions:
Imagery 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Perception, Attention and Motor Behavior:
Perception: Multisensory and Crossmodal 2
Keywords:
FUNCTIONAL MRI
Perception
Other - Body Image
1|2Indicates the priority used for review
Provide references using author date format
Hamamoto, Y. (2023), ‘Neural mechanisms of perceptual and affective body-image disturbance during own-body and ideal-body estimation’, Behavioural Brain Research, vol. 444, pp. 114349.
Peelen, M. V. (2005), ‘Selectivity for the human body in the fusiform gyrus’, Journal of Neurophysiology, vol. 93, no. 1, pp. 603–608.
Phillips K. A. (2004), ‘Body dysmorphic disorder: recognizing and treating imagined ugliness’, World Psychiatry, vol. 3, no. 1, pp. 12–17.
Ralph-Nearman, C. (2021), ‘Visual mapping of body image disturbance in anorexia nervosa reveals objective markers of illness severity’, Scientific Reports, vol. 11, no. 1, pp. 12262.
Saxe, R. (2006), ‘My body or yours? The effect of visual perspective on cortical body representations’, Cerebral cortex, vol. 16, no. 2, pp. 178–182.