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Task-free MRI predicts individual differences in brain activity during task performance.

We all differ in how we perceive, think, and act. What drives individual differences in evoked brain activity? Tavor et al. applied computational models to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. Brain activity in the "resting" state when subjects were no... Full description

Journal Title: Science (New York N.Y.), April 8, 2016, Vol.352(6282), pp.216-220
Main Author: Tavor, I
Other Authors: Parker Jones, O , Mars, R B , Smith, S M , Behrens, T E , Jbabdi, S
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1095-9203 ; DOI: 1095-9203 ; DOI: 10.1126/science.aad8127
Link: http://search.proquest.com/docview/1785736310/?pq-origsite=primo
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title: Task-free MRI predicts individual differences in brain activity during task performance.
format: Article
creator:
  • Tavor, I
  • Parker Jones, O
  • Mars, R B
  • Smith, S M
  • Behrens, T E
  • Jbabdi, S
subjects:
  • Brain–Physiology
  • Brain Mapping–Methods
  • Humans–Methods
  • Individuality–Methods
  • Language–Methods
  • Magnetic Resonance Imaging–Methods
  • Task Performance and Analysis–Methods
ispartof: Science (New York, N.Y.), April 8, 2016, Vol.352(6282), pp.216-220
description: We all differ in how we perceive, think, and act. What drives individual differences in evoked brain activity? Tavor et al. applied computational models to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. Brain activity in the "resting" state when subjects were not performing any explicit task predicted differences in fMRI activation across a range of cognitive paradigms. This suggests that individual differences in many cognitive tasks are a stable trait marker. Resting-state functional connectivity thus already contains the repertoire that is then expressed during task-based fMRI. Science, this issue p. 216 When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.
language: eng
source:
identifier: E-ISSN: 1095-9203 ; DOI: 1095-9203 ; DOI: 10.1126/science.aad8127
fulltext: fulltext
issn:
  • 10959203
  • 1095-9203
url: Link


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titleTask-free MRI predicts individual differences in brain activity during task performance.
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descriptionWe all differ in how we perceive, think, and act. What drives individual differences in evoked brain activity? Tavor et al. applied computational models to functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. Brain activity in the "resting" state when subjects were not performing any explicit task predicted differences in fMRI activation across a range of cognitive paradigms. This suggests that individual differences in many cognitive tasks are a stable trait marker. Resting-state functional connectivity thus already contains the repertoire that is then expressed during task-based fMRI. Science, this issue p. 216 When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.
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