Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

Abstract

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI

“…The results of this study, as well as results of related studies (Davatzikos, Fan et al 2006, epub;Fan, Batmanghelich et al 2008;Vemuri, Gunter et al 2008;Kloppel, Stonnington et al 2008, epub), bolster our confidence that the methodology adopted herein can considerably assist in the diagnosis of and differentiation between dementias. Representative cross-sections of effect sizes calculated for CN minus FTD (top), CN minus AD (bottom).…”

“…However differential diagnosis between FTD and AD based on structural, rather than functional, scans is a greater challenge. The development of sophisticated high-dimensional image analysis and classification methods in the field of computational neuroanatomy during the past decade can potentially help overcome this challenge (Golland 2002;Lao, Shen et al 2004;Csernansky, Wang et al 2005;Davatzikos, Ruparel et al 2005;Davatzikos, Fan et al 2006, epub;Fan, Batmanghelich et al 2008;Lerch, Pruessner et al 2008;Vemuri, Gunter et al 2008). …”

Alzheimer Disease: Quantitative Structural Neuroimaging for Detection and Prediction of Clinical and Structural Changes in Mild Cognitive Impairment

“…Sensitivity was on par with clinical diagnostic accuracy: Accuracy of AD that is based on histopathologic verification ranges from 85% to 90% (50-52). Although investigators in some prior studies have reported classification accuracy values ranging from 90% to 100% on the basis of MR imaging measures, they used smaller sample sizes (16,31,(53)(54)(55), included AD groups with more severe impairment (53-55), or did not report fully crossvalidated results (16,31,53,54). Failure to cross-validate results produces an optimis-…”

Multivariate Pattern Recognition for Diagnosis and Prognosis in Clinical Neuroimaging: State of the Art, Current Challenges and Future Trends

“…Second, predictive modeling at the level of the single subject is key to ultimately provide new neuroimaging markers with diagnostic value. Initially, whole-brain morphometry from structural MRI has been used to train models that can discriminate between healthy controls and patients, such as Alzheimer's disease and frontotemporal dementia (Kloppel et al 2008;Fan et al, 2008a, c;Davatzikos et al 2008), fragile-X syndrome (Hoeft et al 2008), psychosis (Davatzikos et al 2005;Fan et al 2008b), depression (Costafreda et al 2009), psychosis (Sun et al 2009), multiple sclerosis (Weygandt et al 2011) and so on. Advances in functional MRI, and more recently resting-state fMRI, have made it possible to study alterations in functional networks (Fox and Greicius 2010) without behavioral confounds (Bullmore 2012).…”