Neural Prediction of Social Support Hubs in Emerging ... · Neural Prediction of Social Support...
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1 2 3 indegree 1 2 3 prediction PCs Low indegree Medium indegree High indegree Betas Low indegree Medium indegree High indegree Neural Prediction of Social Support Hubs in Emerging Social Networks Yuan Chang Leong, Sylvia Morelli, Ryan Carlson, Monica Kullar & Jamil Zaki [email protected] [email protected] [email protected] [email protected] [email protected] Introduction Neural Prediction Procedure Social Support Hubs Conclusions Whole-brain prediction Passive Viewing Task Measures of Social Support Nomination Question Factor Loading 1. Who are your closest friends? .91 2. Whom do you spend the most time with? .91 3. Whom have you asked for advice? .83 4. Who do you turn to when something bad happens? .84 5. Whom do you share good news with? .94 6. Who makes you feel supported and cared for? .89 7. Who is the most empathetic? .72 8. Who usually makes you feel positive? .81 indegree = number of nominations received by an individual = number of edges directed to a node • 99 freshman from 2 freshman-only dorms • Social support nominations collected in week 2 • fMRI scanning between weeks 3 to 10 dormmate 1s ITI 1-8s dormmate 1s ITI 1s Oddball 1s + + Step 1 Tercile split all members of the dorm based on social support indegree Step 2 For each participant, generate brain maps associated with viewing each set of faces indegree Frequency 0 5 10 15 0 5 10 15 20 25 Step 3 Dimensionality reduction with PCA Step 4 Least squares regression with L1 regularization on n-1 participants Step 5 Predict indegree on held-out subject, and compute within-subject rank correlation indegree = … … Hi Mi Lo Hi Mi w 1 w 2 w 3 PCs weights indegree = ? ? ? w 1 w 2 w 3 PCs weights MPFC PMC RTPJ LTPJ RTP LTP VS V1 Whole Brain -0.2 0 0.2 0.4 0.6 Correlation Whole Brain Prediction Weight Map Mentalizing Network ROI Prediction MPFC PMC Temporal Pole TPJ Repeated analysis with 6 ROIs from the mentalizing network, ventral striatum and primary visual cortex r = 0.33 MPFC PMC Temporal Poles • Within a social network, some individuals are more likely to be providers of social support than others • Are members of the same social network aware of these social support “hubs”? • Here, we test the hypothesis that people automatically track the social supportiveness of members of their network • Furthermore, we assess our ability to predict social support hubs from neural activity during the passive viewing of faces • Participants neurally track the social supportiveness of an individual during the passive viewing of a face • The prediction is associated with activity in the mentalizing network and reward regions • Our results add to the emerging literature demonstrating the prediction of sociometric properties from the brain activity of a single individual • Future work will examine if the neural signature would generalize to a different network (i.e. out-of-sample prediction)
Transcript of Neural Prediction of Social Support Hubs in Emerging ... · Neural Prediction of Social Support...
1 2 3indegree
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Neural Prediction of Social Support Hubs in Emerging Social NetworksYuan Chang Leong, Sylvia Morelli, Ryan Carlson, Monica Kullar & Jamil Zaki
[email protected] [email protected] [email protected] [email protected] [email protected]
Introduction
Neural Prediction ProcedureSocial Support Hubs
Conclusions
Whole-brain predictionPassive Viewing Task
Measures of Social Support
Nomination QuestionFactor Loading
1. Who are your closest friends? .912. Whom do you spend the most time with? .913. Whom have you asked for advice? .834. Who do you turn to when something bad happens? .845. Whom do you share good news with? .946. Who makes you feel supported and cared for? .897. Who is the most empathetic? .728. Who usually makes you feel positive? .81
indegree= number of nominations received by an individual= number of edges directed to a node
• 99 freshman from 2 freshman-only dorms• Social support nominations collected in week 2 • fMRI scanning between weeks 3 to 10
dormmate1s ITI
1-8s dormmate1s ITI
1s Oddball1s
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Step 1Tercile split all members of the dorm based on social support indegree
Step 2For each participant, generate brain maps associated with viewing each set of faces
Histogram of indegree
indegree
Frequency
0 5 10 15
05
1015
2025
HighPopularityLow Medium
Step 3Dimensionality reduction with PCA
Step 4Least squares regression with L1 regularization on n-1 participants
Step 5Predict indegree on held-out subject, and compute within-subject rank correlation
indegree
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Hi
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MPFC PMC RTPJ LTPJ RTP LTP VS V1 Whole Brain-0.2
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Whole Brain Prediction Weight Map
Mentalizing Network
ROI Prediction
MPFCPMC
Temporal Pole
TPJ Repeated analysis with 6 ROIs from the mentalizing network, ventral striatum and primary visual cortex
r = 0.33
MPFCPMC
Temporal Poles
• Within a social network, some individuals are more likely to be providers of social support than others
• Are members of the same social network aware of these social support “hubs”?
• Here, we test the hypothesis that people automatically track the social supportiveness of members of their network
• Furthermore, we assess our ability to predict social support hubs from neural activity during the passive viewing of faces
• Participants neurally track the social supportiveness of an individual during the passive viewing of a face
• The prediction is associated with activity in the mentalizing network and reward regions
• Our results add to the emerging literature demonstrating the prediction of sociometric properties from the brain activity of a single individual
• Future work will examine if the neural signature would generalize to a different network (i.e. out-of-sample prediction)
