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Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure

Frick, Andreas (author)
Uppsala universitet,Institutionen för psykologi
Gingnell, Malin (author)
Uppsala universitet,Institutionen för psykologi,Obstetrik & gynekologi
Marquand, Andre F. (author)
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Howner, Katarina (author)
Karolinska Institutet
Fischer, Håkan (author)
Stockholms universitet,Psykologiska institutionen
Kristiansson, Marianne (author)
Karolinska Institutet
Williams, Steven C. R. (author)
Fredrikson, Mats (author)
Uppsala universitet,Institutionen för psykologi
Furmark, Tomas (author)
Uppsala universitet,Institutionen för psykologi
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 (creator_code:org_t)
Elsevier BV, 2014
2014
English.
In: Behavioural Brain Research. - : Elsevier BV. - 0166-4328 .- 1872-7549. ; 75:9, s. 358S-358S
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Functional neuroimaging of social anxiety disorder (SAD) support altered neural activation to threat-provoking stimuli focally in the fear network, while structural differences are distributed over the temporal and frontal cortices as well as limbic structures. Previous neuroimaging studies have investigated the brain at the voxel level using mass-univariate methods which do not enable detection of more complex patterns of activity and structural alterations that may separate SAD from healthy individuals. Support vector machine (SVM) is a supervised machine learning method that capitalizes on brain activation and structural patterns to classify individuals. The aim of this study was to investigate if it is possible to discriminate SAD patients (n = 14) from healthy controls (n = 12) using SVM based on (1) functional magnetic resonance imaging during fearful face processing and (2) regional gray matter volume. Whole brain and region of interest (fear network) SVM analyses were performed for both modalities. For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume. These results support that SAD is characterized by aberrant neural activation to affective stimuli in the fear network, while disorder-related alterations in regional gray matter volume are more diffusely distributed over the whole brain. SVM may thus be useful for identifying imaging biomarkers of SAD.

Subject headings

SAMHÄLLSVETENSKAP  -- Psykologi (hsv//swe)
SOCIAL SCIENCES  -- Psychology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Neurosciences (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Psykiatri (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Psychiatry (hsv//eng)

Keyword

Support vector machine
Classification
Social anxiety disorder
Multivoxel pattern analysis
Biomarker

Publication and Content Type

ref (subject category)
art (subject category)

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