AUTHOR=Lebedeva Aleksandra K. , Westman Eric , Borza Tom , Beyer Mona K. , Engedal Knut , Aarsland Dag , Selbaek Geir , Haberg Asta K. 

TITLE=MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 9 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2017.00013

DOI=10.3389/fnagi.2017.00013

ISSN=1663-4365

ABSTRACT=Objective
Late-life depression (LLD) is associated with development of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain MRI in LLD patients can predict mild cognitive impairment (MCI) or dementia one year prior to the diagnosis.
Methods
LLD patients underwent brain MRI at baseline and repeated clinical assessment after one-year. Structural brain measurements were obtained using Freesurfer software (v 5.1) from the T1 brain MRI images. MRI-based Random Forest classifier was used to discriminate between LLD who developed MCI or dementia after one-year follow up and cognitively stable LLD. Additionally, a previously established Random Forest model trained on 185 patients with Alzheimer’s disease vs 225 cognitively normal elderly from the Alzheimer’s disease Neuroimaging Initiative was tested on the LLD data set (ADNI model).
Results
MCI and dementia diagnoses were predicted in LLD patients with 76%/68%/84% accuracy/ sensitivity/specificity. Adding the baseline Mini-Mental State Examination scores to the models improved accuracy/sensitivity/specificity to 81%/75%/86%. The best model predicted MCI status alone using MRI and baseline Mini-Mental State Examination scores with accuracy/sensitivity/specificity of 89%/85%/90%. The most important region for all the models was right ventral diencephalon, including hypothalamus. Its volume correlated negatively with the number of depressive episodes. ADNI model trained on AD vs Controls could predict MCI-DEM patients with 67% accuracy. 
Conclusion
LDD patients developing MCI and dementia can be discriminated from LLD patients remaining cognitively stable with good accuracy based on baseline structural MRI alone. Baseline Mini-Mental State Examination score improves prediction accuracy. Ventral diencephalon, including the hypothalamus might play an important role in preservation of cognitive functions in LLD.