Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression.

BACKGROUND Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. OBJECTIVE To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. METHODS We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. RESULTS The HI was significantly reduced in the AD group (p <  10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p <  10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. CONCLUSION In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.

[1]  E M Wijsman,et al.  Gender difference in apolipoprotein E-associated risk for familial Alzheimer disease: a possible clue to the higher incidence of Alzheimer disease in women. , 1996, American journal of human genetics.

[2]  D. Louis Collins,et al.  Appearance-based modeling for segmentation of hippocampus and amygdala using multi-contrast MR imaging , 2011, NeuroImage.

[3]  Li Shen,et al.  Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort , 2009, Current Alzheimer research.

[4]  Juan Zhou,et al.  Segmentation of subcortical brain structures using fuzzy templates , 2005, NeuroImage.

[5]  A. Dale,et al.  Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. , 2011, Radiology.

[6]  P. Murali Doraiswamy,et al.  Marked gender differences in progression of mild cognitive impairment over 8 years , 2015, Alzheimer's & dementia.

[7]  C. Jack,et al.  Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease , 1998, Neurology.

[8]  Pradeep Reddy Raamana,et al.  Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls , 2014, Front. Neurol..

[9]  A. Convit,et al.  Hippocampal volume losses in minimally impaired elderly , 1995, The Lancet.

[10]  Arthur W. Toga,et al.  Defining the human hippocampus in cerebral magnetic resonance images—An overview of current segmentation protocols , 2009, NeuroImage.

[11]  Márcio Sarroglia Pinho,et al.  Automated Methods for Hippocampus Segmentation: the Evolution and a Review of the State of the Art , 2014, Neuroinformatics.

[12]  L. Squire,et al.  The medial temporal lobe memory system , 1991, Science.

[13]  R. Brookmeyer,et al.  National estimates of the prevalence of Alzheimer’s disease in the United States , 2011, Alzheimer's & Dementia.

[14]  Sébastien Ourselin,et al.  MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset , 2013, NeuroImage.

[15]  J. Haines,et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. , 1993, Science.

[16]  H. Soininen,et al.  Major decrease in the volume of the entorhinal cortex in patients with Alzheimer’s disease carrying the apolipoprotein E ε4 allele , 1998, Journal of neurology, neurosurgery, and psychiatry.

[17]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[19]  Bruce Fischl,et al.  Within-subject template estimation for unbiased longitudinal image analysis , 2012, NeuroImage.

[20]  J. Haines,et al.  Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. , 1997, JAMA.

[21]  A. Convit,et al.  Specific Hippocampal Volume Reductions in Individuals at Risk for Alzheimer’s Disease , 1997, Neurobiology of Aging.

[22]  Andre Altmann,et al.  Sex modifies the APOE‐related risk of developing Alzheimer disease , 2014, Annals of neurology.

[23]  D. Louis Collins,et al.  Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.

[24]  Charles DeCarli,et al.  Sex, Apolipoprotein E ε4 Status, and Hippocampal Volume in Mild Cognitive Impairment , 2005 .

[25]  Wiro J. Niessen,et al.  Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts , 2008, NeuroImage.

[26]  Michael Weiner,et al.  and the Alzheimer’s Disease Neuroimaging Initiative* , 2007 .

[27]  K. Langa,et al.  Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study , 2007, Neuroepidemiology.

[28]  Sandra E. Black,et al.  A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease , 2013, NeuroImage.

[29]  Sébastien Ourselin,et al.  STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation , 2013, Medical Image Anal..

[30]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[31]  W. Thies,et al.  2013 Alzheimer's disease facts and figures , 2013, Alzheimer's & Dementia.

[32]  Babak A. Ardekani,et al.  Model-based automatic detection of the anterior and posterior commissures on MRI scans , 2009, NeuroImage.

[33]  J. Haines,et al.  Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease: A Meta-analysis , 1997 .

[34]  Dinggang Shen,et al.  Sex differences in grey matter atrophy patterns among AD and aMCI patients: Results from ADNI , 2011, NeuroImage.

[35]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[36]  L. McEvoy,et al.  Predicting MCI outcome with clinically available MRI and CSF biomarkers , 2011, Neurology.

[37]  Iwao Kanno,et al.  Automatic detection of the mid-sagittal plane in 3-D brain images , 1997, IEEE Transactions on Medical Imaging.

[38]  D. Louis Collins,et al.  Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation , 2010, MICCAI.

[39]  Jennifer L. Whitwell,et al.  Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance , 2015, NeuroImage.

[40]  Henry Rusinek,et al.  Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. , 2003, Radiology.

[41]  J. Trojanowski,et al.  Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification , 2011, Neurobiology of Aging.

[42]  T. Iwatsubo [Alzheimer's disease Neuroimaging Initiative (ADNI)]. , 2011, Nihon rinsho. Japanese journal of clinical medicine.

[43]  Frederik Barkhof,et al.  Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST , 2014, NeuroImage.

[44]  I. Lombardo,et al.  The efficacy of RVT-101, a 5-ht6 receptor antagonist, as an adjunct to donepezil in adults with mild-to-moderate Alzheimer’s disease: Completer analysis of a phase 2b study , 2015, Alzheimer's & Dementia.

[45]  A. Dale,et al.  Higher Rates of Decline for Women and Apolipoprotein E ε4 Carriers , 2013, American Journal of Neuroradiology.

[46]  Klaus P. Ebmeier,et al.  A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease , 2011, Neurobiology of Aging.

[47]  Harald Hampel,et al.  Fully automated atlas-based hippocampal volumetry for detection of Alzheimer's disease in a memory clinic setting. , 2015, Journal of Alzheimer's disease : JAD.

[48]  D. Louis Collins,et al.  Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion , 2010, NeuroImage.

[49]  Michael W. Weiner,et al.  Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans , 2010, Neurobiology of Aging.

[50]  Reto Meuli,et al.  Robust parameter estimation of intensity distributions for brain magnetic resonance images , 1998, IEEE Transactions on Medical Imaging.

[51]  C. Jack,et al.  Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment , 1999, Neurology.

[52]  Alexander Hammers,et al.  Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation , 2009, NeuroImage.

[53]  Paul M. Thompson,et al.  Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment , 2005, NeuroImage.