Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns
Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continu...
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| Published in | Cerebral cortex (New York, N.Y. 1991) Vol. 22; no. 1; pp. 158 - 165 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Oxford University Press
01.01.2012
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples. |
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| AbstractList | Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples. Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples. |
| Author | Greicius, M. D. Ryali, S. Shirer, W. R. Rykhlevskaia, E. Menon, V. |
| AuthorAffiliation | 1 Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences 2 Stanford Cognitive and Systems Neuroscience Lab (SCSNL), Department of Psychiatry and Behavioral Sciences 3 Program in Neuroscience, Stanford School of Medicine, Stanford, CA 94305, USA |
| AuthorAffiliation_xml | – name: 3 Program in Neuroscience, Stanford School of Medicine, Stanford, CA 94305, USA – name: 2 Stanford Cognitive and Systems Neuroscience Lab (SCSNL), Department of Psychiatry and Behavioral Sciences – name: 1 Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences |
| Author_xml | – sequence: 1 givenname: W. R. surname: Shirer fullname: Shirer, W. R. organization: 1Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences – sequence: 2 givenname: S. surname: Ryali fullname: Ryali, S. organization: 2Stanford Cognitive and Systems Neuroscience Lab (SCSNL), Department of Psychiatry and Behavioral Sciences – sequence: 3 givenname: E. surname: Rykhlevskaia fullname: Rykhlevskaia, E. organization: 2Stanford Cognitive and Systems Neuroscience Lab (SCSNL), Department of Psychiatry and Behavioral Sciences – sequence: 4 givenname: V. surname: Menon fullname: Menon, V. organization: 2Stanford Cognitive and Systems Neuroscience Lab (SCSNL), Department of Psychiatry and Behavioral Sciences – sequence: 5 givenname: M. D. surname: Greicius fullname: Greicius, M. D. email: greicius@stanford.edu organization: 1Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21616982$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adolescent Adult Brain - blood supply Brain - physiology Brain Mapping Cognition - classification Cognition - physiology Cohort Studies Female Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Mental Recall - physiology Neural Pathways - blood supply Neural Pathways - physiology Neuropsychological Tests Oxygen - blood Young Adult |
| Title | Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns |
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