首页|Jena University Hospital Reports Findings in Schizophrenia (BrainAGE: Revisited and reframed machine learning workflow)
Jena University Hospital Reports Findings in Schizophrenia (BrainAGE: Revisited and reframed machine learning workflow)
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NETL
NSTL
Wiley
New research on Mental Health Diseases and Conditions - Schizophrenia is the subject of a report. According to news reporting out of Jena, Germany, by NewsRx editors, research stated, "Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health." Financial supporters for this research include Bundesministerium fur Bildung, Wissenschaft und Forschung, Carl-Zeiss-Stiftung, H2020 Marie Sklodowska-Curie Actions. Our news journalists obtained a quote from the research from Jena University Hospital, "We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing diseasespecific patterns for different levels of impairment."
JenaGermanyEuropeCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMental Health Diseases and ConditionsPsychiatrySchizophrenia
NETL
NSTL
Wiley
