Separation of cognitive domains to improve prediction of progression from mild cognitive impairment to Alzheimer's disease

Addressing causes of heterogeneity in cognitive outcomes is becoming more critical as Alzheimer's disease (AD) research focuses on earlier disease. One of the causes of this heterogeneity may be that individuals with deficiencies in different cognitive domains may perform similarly on a neuropsychological (NP) test for very different reasons. Tatsuoka and colleagues have applied a Bayesian model in order to integrate knowledge about cognitive domains relevant to each NP test with the observed outcomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) mild cognitive impairment data. This approach resulted in better prediction of AD diagnosis than more traditional approaches.