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Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Sato JR, Moll J, Green S, Deakin JF, Thomaz CE, Zahn R - Psychiatry Res (2015)

Bottom Line: Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD).Here, we use machine learning for the first time to address this question.Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Bangu, Santo André 09020-040, Brazil; Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil.

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(a) Receiver Operator Characteristic (ROC) curve for the MLDA classifier's ability to distinguish MD from control images, based on the projected values (decision values). (b) Axial slices ventral to the corpus callosum display MLDA weight vector maps highlighting the voxels which were among the 1% most discriminative for MD patients vs. controls including the subgenual cingulate cortex, both hippocampi, the right thalamus and the anterior insulae.
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f0005: (a) Receiver Operator Characteristic (ROC) curve for the MLDA classifier's ability to distinguish MD from control images, based on the projected values (decision values). (b) Axial slices ventral to the corpus callosum display MLDA weight vector maps highlighting the voxels which were among the 1% most discriminative for MD patients vs. controls including the subgenual cingulate cortex, both hippocampi, the right thalamus and the anterior insulae.


Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Sato JR, Moll J, Green S, Deakin JF, Thomaz CE, Zahn R - Psychiatry Res (2015)

(a) Receiver Operator Characteristic (ROC) curve for the MLDA classifier's ability to distinguish MD from control images, based on the projected values (decision values). (b) Axial slices ventral to the corpus callosum display MLDA weight vector maps highlighting the voxels which were among the 1% most discriminative for MD patients vs. controls including the subgenual cingulate cortex, both hippocampi, the right thalamus and the anterior insulae.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834459&req=5

f0005: (a) Receiver Operator Characteristic (ROC) curve for the MLDA classifier's ability to distinguish MD from control images, based on the projected values (decision values). (b) Axial slices ventral to the corpus callosum display MLDA weight vector maps highlighting the voxels which were among the 1% most discriminative for MD patients vs. controls including the subgenual cingulate cortex, both hippocampi, the right thalamus and the anterior insulae.
Bottom Line: Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD).Here, we use machine learning for the first time to address this question.Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Center for Mathematics, Computation, and Cognition, Universidade Federal do ABC, Bangu, Santo André 09020-040, Brazil; Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil.

Show MeSH
Related in: MedlinePlus