Precuneus and Cingulate Cortex Atrophy and Hypometabolism in Patients with Alzheimer's Disease and Mild Cognitive Impairment: MRI and 18F-FDG PET Quantitative Analysis Using FreeSurfer

Objective. The objective of this study was to compare glucose metabolism and atrophy, in the precuneus and cingulate cortex, in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI), using FreeSurfer. Methods. 47 individuals (17 patients with AD, 17 patients with amnestic MCI, and 13 healthy controls (HC)) were included. MRI and PET images using 18F-FDG (mean injected dose of 185 MBq) were acquired and analyzed using FreeSurfer to define regions of interest in the hippocampus, amygdala, precuneus, and anterior and posterior cingulate cortex. Regional volumes were generated. PET images were registered to the T1-weighted MRI images and regional uptake normalized by cerebellum uptake (SUVr) was measured. Results. Mean posterior cingulate volume was reduced in MCI and AD. SUVr were different between the three groups: mean precuneus SUVr was 1.02 for AD, 1.09 for MCI, and 1.26 for controls (p < 0.05); mean posterior cingulate SUVr was 0.96, 1.06, and 1.22 for AD, MCI, and controls, respectively (p < 0.05). Conclusion. We found graduated hypometabolism in the posterior cingulate cortex and the precuneus in prodromal AD (MCI) and AD, whereas atrophy was not significant. This suggests that the use of 18F-FDG in these two regions could be a neurodegenerative biomarker.

[1]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[2]  D. Benson,et al.  The fluorodeoxyglucose 18F scan in Alzheimer's disease and multi-infarct dementia. , 1983, Archives of neurology.

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

[4]  P. Scheltens,et al.  Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[5]  N. Foster,et al.  Preserved Pontine Glucose Metabolism in Alzheimer Disease: A Reference Region for Functional Brain Image (PET) Analysis , 1995, Journal of computer assisted tomography.

[6]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[7]  N. Foster,et al.  Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease , 1997, Annals of neurology.

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

[9]  A. Convit,et al.  Hippocampal formation glucose metabolism and volume losses in MCI and AD , 2001, Neurobiology of Aging.

[10]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[11]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

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

[13]  L. Mosconi,et al.  Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[14]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[15]  P. Scheltens,et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria , 2007, The Lancet Neurology.

[16]  J B Poline,et al.  Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. , 2007, Brain : a journal of neurology.

[17]  B T Hyman,et al.  Temporoparietal MR Imaging Measures of Atrophy in Subjects with Mild Cognitive Impairment That Predict Subsequent Diagnosis of Alzheimer Disease , 2009, American Journal of Neuroradiology.

[18]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[19]  Nick C Fox,et al.  Revising the definition of Alzheimer's disease: a new lexicon , 2010, The Lancet Neurology.

[20]  Sébastien Ourselin,et al.  Head size, age and gender adjustment in MRI studies: a necessary nuisance? , 2010, NeuroImage.

[21]  Arno Villringer,et al.  Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies , 2010, NeuroImage.

[22]  Susan M Resnick,et al.  Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. , 2010, Archives of general psychiatry.

[23]  Magda Tsolaki,et al.  Effect of APOE ε4 allele on cortical thicknesses and volumes: the AddNeuroMed study. , 2010, Journal of Alzheimer's disease : JAD.

[24]  A. McKinney,et al.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease , 2010 .

[25]  A. Simmons,et al.  Analysis of regional MRI volumes and thicknesses as predictors of conversion from mild cognitive impairment to Alzheimer's disease , 2010, Neurobiology of Aging.

[26]  A. Dale,et al.  Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease. , 2010, Radiology.

[27]  Alan C. Evans,et al.  APOE ε2 Allele Is Associated with Larger Regional Cortical Thicknesses and Volumes , 2010, Dementia and Geriatric Cognitive Disorders.

[28]  Jesse S. Jin,et al.  Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors , 2011, PloS one.

[29]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease: Report of the NINCDS—ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease , 2011, Neurology.

[30]  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.

[31]  Anders M. Dale,et al.  Consistent neuroanatomical age-related volume differences across multiple samples , 2011, Neurobiology of Aging.

[32]  G. Chételat La neuro-imagerie au service de la maladie d’Alzheimer - Imagerie et cognition (3) , 2011 .

[33]  A. Simmons,et al.  Combination analysis of neuropsychological tests and structural MRI measures in differentiating AD, MCI and control groups—The AddNeuroMed study , 2011, Neurobiology of Aging.

[34]  R. Coleman,et al.  Use of florbetapir-PET for imaging beta-amyloid pathology. , 2011, JAMA.

[35]  Andrew Simmons,et al.  Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease. , 2011, Journal of Alzheimer's disease : JAD.

[36]  A. Simmons,et al.  Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment , 2012, Brain Topography.

[37]  Jing Yang,et al.  Voxelwise meta-analysis of gray matter anomalies in Alzheimer's disease and mild cognitive impairment using anatomic likelihood estimation , 2012, Journal of the Neurological Sciences.

[38]  Ron Mengelers,et al.  The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements , 2012, PloS one.

[39]  Charles DeCarli,et al.  Maximal brain size remains an important predictor of cognition in old age, independent of current brain pathology , 2012, Neurobiology of Aging.

[40]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[41]  Ciprian Catana,et al.  Effect of MRI Acoustic Noise on Cerebral Fludeoxyglucose Uptake in Simultaneous MR-PET Imaging , 2013, Investigative radiology.

[42]  Jyrki Lötjönen,et al.  Implementation and Validation of an Adaptive Template Registration Method for 18F-Flutemetamol Imaging Data , 2013, The Journal of Nuclear Medicine.

[43]  Matthias L. Schroeter,et al.  Impaired cerebral glucose metabolism in prodromal Alzheimer's disease differs by regional intensity normalization , 2013, Neuroscience Letters.

[44]  Andrei G. Vlassenko,et al.  Quantitative Analysis of PiB-PET with FreeSurfer ROIs , 2013, PloS one.

[45]  Nick C Fox,et al.  Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria , 2014, The Lancet Neurology.

[46]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[47]  Christer Halldin,et al.  Evaluation of Two Automated Methods for PET Region of Interest Analysis , 2014, Neuroinformatics.

[48]  C. Jack,et al.  Comparing 3T and 1.5T MRI for Mapping Hippocampal Atrophy in the Alzheimer's Disease Neuroimaging Initiative , 2015, American Journal of Neuroradiology.