Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder.
Emanuel SchwarzNhat Trung DoanGiulio PergolaLars Tjelta WestlyeTobias KaufmannThomas WolfersRalph BrecheisenTiziana QuartoAlex J IngPasquale Di CarloTiril Pedersen GurholtRobbert L HarmsQuentin NoirhommeTorgeir MobergetIngrid AgartzOle A AndreassenMarcella BellaniAlessandro BertolinoGiuseppe BlasiPaolo BrambillaJan K BuitelaarSimon CervenkaLena FlycktSophia FrangouBarbara FrankeJeremy HallDirk J HeslenfeldMartina KirschAndrew M McIntoshMarkus M NöthenAndreas PapassotiropoulosDominique J-F de QuervainMarcella D C RietschelGunter SchumannHeike TostStephanie-H WittMathias ZinkAndreas Meyer-Lindenbergnull nullPublished in: Translational psychiatry (2019)
Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
Keyphrases
- bipolar disorder
- white matter
- major depressive disorder
- machine learning
- attention deficit hyperactivity disorder
- magnetic resonance imaging
- resting state
- end stage renal disease
- cerebral ischemia
- chronic kidney disease
- multiple sclerosis
- autism spectrum disorder
- big data
- deep learning
- ejection fraction
- computed tomography
- artificial intelligence
- contrast enhanced
- electronic health record
- magnetic resonance
- data analysis
- working memory
- brain injury
- patient reported
- structural basis