Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.
T G M van ErpD P HibarJ M RasmussenD C GlahnG D PearlsonO A AndreassenI AgartzL T WestlyeU K HaukvikA M DaleI MelleC B HartbergO GruberB KraemerD ZillesG DonohoeS KellyC McDonaldD W MorrisD M CannonA CorvinM W J MachielsenL KoendersL de HaanD J VeltmanT D SatterthwaiteD H WolfR C GurR E GurS G PotkinD H MathalonB A MuellerAdrian PredaF MacciardiS EhrlichE WaltonJ HassV D CalhounH J BockholtS R SponheimJ M ShoemakerN E M van HarenH E Hulshoff PolH E H PolR A OphoffR S KahnR Roiz-SantiañezB Crespo-FacorroL WangK I AlpertE G JönssonR DimitrovaC BoisH C WhalleyA M McIntoshS M LawrieRyota HashimotoP M ThompsonJ A TurnerPublished in: Molecular psychiatry (2015)
The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.
Keyphrases
- white matter
- resting state
- end stage renal disease
- bipolar disorder
- functional connectivity
- chronic kidney disease
- newly diagnosed
- systematic review
- mental illness
- ejection fraction
- cerebral ischemia
- prognostic factors
- peritoneal dialysis
- big data
- heart failure
- electronic health record
- magnetic resonance imaging
- machine learning
- randomized controlled trial
- magnetic resonance
- high resolution
- clinical trial
- mental health
- quality improvement
- contrast enhanced
- cognitive impairment
- pulmonary arterial hypertension
- pulmonary artery
- patient reported
- health information
- double blind