Amyloid-independent atrophy patterns predict time to progression to dementia in mild cognitive impairment

BackgroundAmyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer’s disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations.MethodsWe included 276 subjects with MCI (mean age 67 ± 8, 41% female, mean Mini-Mental State Examination 26.6 ± 2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status.ResultsDementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5 ± 1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p < 0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32–1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66–1.91)).ConclusionsIn subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.

[1]  B. Boeve,et al.  Does TDP-43 type confer a distinct pattern of atrophy in frontotemporal lobar degeneration? , 2010, Neurology.

[2]  P. Grambsch,et al.  Proportional hazards tests and diagnostics based on weighted residuals , 1994 .

[3]  C. Jack,et al.  Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease , 2009, Brain : a journal of neurology.

[4]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[5]  P. Vemuri,et al.  Brain β-amyloid load approaches a plateau , 2013, Neurology.

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

[7]  Frederik Barkhof,et al.  Optimizing patient care and research: the Amsterdam Dementia Cohort. , 2014, Journal of Alzheimer's disease : JAD.

[8]  S Minoshima,et al.  Diagnosis and management of dementia with Lewy bodies , 2005, Neurology.

[9]  Charles D. Smith,et al.  Hippocampal sclerosis of aging, a prevalent and high-morbidity brain disease , 2013, Acta Neuropathologica.

[10]  John Suckling,et al.  Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

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

[12]  C. Jack,et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. , 2008, Brain : a journal of neurology.

[13]  C. Jack,et al.  Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia , 2002, Neurology.

[14]  A. Fagan,et al.  Suspected non-Alzheimer disease pathophysiology — concept and controversy , 2016, Nature Reviews Neurology.

[15]  R. Faber,et al.  Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. , 1999, Neurology.

[16]  G. C. Román,et al.  Vascular dementia , 1993, Neurology.

[17]  C. Jack,et al.  MRI and CSF biomarkers in normal, MCI, and AD subjects , 2009, Neurology.

[18]  J. Hodges,et al.  Outcome in subgroups of mild cognitive impairment (MCI) is highly predictable using a simple algorithm , 2009, Journal of Neurology.

[19]  Nick C Fox,et al.  Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. , 2011, Brain : a journal of neurology.

[20]  Clifford R. Jack,et al.  Time-to-event voxel-based techniques to assess regional atrophy associated with MCI risk of progression to AD , 2011, NeuroImage.

[21]  M. Freedman,et al.  Frontotemporal lobar degeneration , 1998, Neurology.

[22]  W. M. van der Flier,et al.  Amyloid-beta(1-42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. , 2010, Clinical chemistry.

[23]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[24]  C. Jack,et al.  MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment , 2008, Neurology.

[25]  Nick C Fox,et al.  The Diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease: Recommendations from the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease , 2011 .

[26]  Matthew L Senjem,et al.  Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings , 2015, Brain : a journal of neurology.

[27]  W. M. van der Flier,et al.  Concordance between cerebrospinal fluid biomarkers and [11C]PIB PET in a memory clinic cohort. , 2014, Journal of Alzheimer's disease : JAD.

[28]  C. Jack,et al.  Biomarker Modeling of Alzheimer’s Disease , 2013, Neuron.

[29]  William Jagust,et al.  Vulnerable Neural Systems and the Borderland of Brain Aging and Neurodegeneration , 2013, Neuron.

[30]  E. Reiman,et al.  Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementias , 2008, Journal of Nuclear Medicine.

[31]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[32]  C. Rowe,et al.  Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease , 2011, Annals of neurology.

[33]  Dan Mungas,et al.  Progression of mild cognitive impairment to dementia in clinic- vs community-based cohorts. , 2009, Archives of neurology.

[34]  Bradford C. Dickerson,et al.  Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau , 2013, Front. Aging Neurosci..

[35]  Olivier Salvado,et al.  Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiB-PET longitudinal study. , 2012, Brain : a journal of neurology.

[36]  C. Rowe,et al.  Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study , 2013, The Lancet Neurology.

[37]  Nick C Fox,et al.  Amnestic Mild Cognitive Impairment: Structural MR Imaging Findings Predictive of Conversion to Alzheimer Disease , 2008, American Journal of Neuroradiology.

[38]  Susan M Resnick,et al.  Longitudinal patterns of β-amyloid deposition in nondemented older adults. , 2011, Archives of neurology.

[39]  James T Becker,et al.  Voxel Level Survival Analysis of Grey Matter Volume and Incident Mild Cognitive Impairment or Alzheimer's Disease. , 2015, Journal of Alzheimer's disease : JAD.

[40]  Daniel Rueckert,et al.  Injury markers predict time to dementia in subjects with MCI and amyloid pathology , 2012, Neurology.

[41]  J. Trojanowski,et al.  Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers☆☆☆ , 2013, NeuroImage: Clinical.