Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

Abstract

There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD.

Cortical surface alterations in cingulate and frontal regulatory areas underlie insensitive mothering

“…Besides, substantial macrostructural WM volume reductions in bilateral frontal areas in NM accompanied the frontal and cingulate cortical thinning observed in NM in this study. This convergence is congruent with studies reporting that alterations in white matter can influence CT values in the altered white matter regions (Belathur Suresh et al, 2018).…”

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study

“…In addition, with SGL, the most important cortex regions are identified. Many studies showed that late life education reduces the rate of cortical thinning (Belathur Suresh et al, 2018 ; Thow et al, 2018 ). Thus, education can be considered as one of the features for brain age prediction.…”

“…In general, the mean cortical thickness values of 148 distinct ROIs were computed from each brain MRIs and used as predicting variables. The confounding variables of gender and education of the subjects were also included to these predictors because they have cortical thinning effect in relation to normal aging (Tang et al, 2013 ; Li et al, 2014 ; Mortby et al, 2014 ; Ruigrok et al, 2014 ; Ritchie et al, 2017 ; Belathur Suresh et al, 2018 ; Thow et al, 2018 ). The gender feature is a “0–1” binary variable that indicates whether the subject is male (0) or female (1).…”

“…The demographic variables of education and gender are also confounding factors that influence cortical thickness (Tang et al, 2013 ; Li et al, 2014 ; Mortby et al, 2014 ; Ruigrok et al, 2014 ; Ritchie et al, 2017 ; Belathur Suresh et al, 2018 ; Thow et al, 2018 ). In particular, the recent work in Belathur Suresh et al ( 2018 ) and Thow et al ( 2018 ) demonstrated the impact of education on cortical thickness for the accuracy of Alzheimer's disease detection. These indicate the potentiality of gender and education as predictive variables in addition to brain anatomical features.…”

Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample

“…Various structural features obtained from simple T1 weighted MRI are impacted by typical aging as well as the degenerative processes of AD ( Jefferson et al, 2015 , Lindemer et al, 2015 , Lindemer et al, 2017a , Coutu et al, 2016 , Belathur Suresh et al, 2018 ) and such features have been used in statistical classification of individuals with a clinical diagnosis of AD ( Chetelat and Baron, 2003 , Teipel et al, 2013 , Park and Moon, 2016 , Belathur Suresh et al, 2018 ). The patterns of atrophy measured by computational models of cerebral cortical thickness can provide spatially distinct features to be utilized in classification models ( Eskildsen et al, 2013 , Raamana et al, 2015 , de Vos et al, 2016 ).…”