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Predicting Brain Functional Connectivity Using Mobile Sensing

on YouTube 07m
Talkat UbiComp / ISWC 2020 · 2020-09-12..09-17· [machine-learning]

speakers

name:Mikio Obuchi
links:none on file
name:Jeremy F. Huckins
links:none on file
name:Weichen Wang
links:none on file
name:Alex Dasilva
links:none on file
name:Courtney Rogers
links:none on file
name:Eilis Murphy
links:none on file
name:Elin Hedlund
links:none on file
name:Paul Holtzheimer
links:none on file
name:Shayan Nmirjafari
links:none on file
name:Andrew T. Campbell
links:none on file

resources

video:https://youtu.be/7djtKulbHWs
slides:none on file

summary

Mobile phone sensors (microphone, unlocks, sleep, GPS) predict resting-state vmPFC–amygdala RSFC in 92 first-year students scanned once and passively sensed over a term. Conversation duration correlates with connectivity (conversation duration (r=0.365)), sleep onset and unlocks also show smaller correlations, and an SVM with RBF kernel classifies high vs low connectivity with F1 0.793 and ~0.79 accuracy. Results are exploratory, small-N, and limited to undergrads.

AI-written summary · about

chapters (6)

  1. 0:00Intro
  2. 0:22Mobile sensing
  3. 1:06Background
  4. 3:50Sensing features
  5. 5:48Results
  6. 6:22Limitation

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