Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Pegah Kassraian-Fard; Caroline Matthis; Joshua H Balsters; Marloes H Maathuis; Nicole Wenderoth

doi:10.3389/fpsyt.2016.00177

Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Pegah Kassraian-Fard, Caroline Matthis, Joshua H Balsters, Marloes H Maathuis, Nicole Wenderoth

Department of Psychology

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	177
Journal	Frontiers in psychiatry
Volume	7
DOIs	https://doi.org/10.3389/fpsyt.2016.00177
Publication status	Published - 1 Dec 2016

Keywords

Autism
Machine Learning
Resting state

Access to Document

10.3389/fpsyt.2016.00177Licence: CC BY

Connectivity-based parcellation in Autism Spectrum Disorder
Balsters, J.
Project: Research

Cite this

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AU - Balsters, Joshua H

AU - Maathuis, Marloes H

AU - Wenderoth, Nicole

PY - 2016/12/1

Y1 - 2016/12/1

KW - Autism

KW - Machine Learning

KW - Resting state

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JO - Frontiers in psychiatry

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Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example

Keywords

Access to Document

Projects

Connectivity-based parcellation in Autism Spectrum Disorder

Cite this