Article

Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps

Laboratory of Neuro Imaging, Department of Neurology, The David Geffen School of Medicine at UCLA, University of California-Los Angeles, Los Angeles, CA, USA.
JAMA Neurology (Impact Factor: 7.01). 06/2006; 63(5):693-9. DOI: 10.1001/archneur.63.5.693
Source: PubMed

ABSTRACT While most patients with mild cognitive impairment (MCI) transition to Alzheimer disease (AD), others develop non-AD dementia, remain in the MCI state, or improve.
To test the following hypotheses: smaller hippocampal volumes predict conversion of MCI to AD, whereas larger hippocampal volumes predict cognitive stability and/or improvement; and patients with MCI who convert to AD have greater atrophy in the CA1 hippocampal subfield and subiculum.
Prospective longitudinal cohort study.
University of California-Los Angeles Alzheimer's Disease Research Center.
We followed up 20 MCI subjects clinically and neuropsychologically for 3 years.
Baseline regional hippocampal atrophy was analyzed with region-of-interest and 3-dimensional hippocampal mapping techniques.
During the 3-year study, 6 patients developed AD (MCI-c), 7 remained stable (MCI-nc), and 7 improved (MCI-i). Patients with MCI-c had 9% smaller left and 13% smaller right mean hippocampal volumes compared with MCI-nc patients. Radial atrophy maps showed greater atrophy of the CA1 subregion in MCI-c. Patients with MCI-c had significantly smaller hippocampi than MCI-i patients (left, 24%; right, 27%). Volumetric analyses showed a trend for greater hippocampal atrophy in MCI-nc relative to MCI-i patients (eg, 16% volume loss). After permutation tests corrected for multiple comparison, the atrophy maps showed a significant difference on the right. Subicular differences were seen between MCI-c and MCI-i patients, and MCI-nc and MCI-i patients. Multiple linear regression analysis confirmed the group effect to be highly significant and independent of age, hemisphere, and Mini-Mental State Examination scores at baseline.
Smaller hippocampi and specifically CA1 and subicular involvement are associated with increased risk for conversion from MCI to AD. Patients with MCI-i tend to have larger hippocampal volumes and relative preservation of both the subiculum and CA1.

1 Follower
 · 
93 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: OBJECTIVE: The pathologic validation of European Alzheimer's Disease Consortium Alzheimer's Disease Neuroimaging Center Harmonized Hippocampal Segmentation Protocol (HarP). METHODS: Temporal lobes of nine Alzheimer's disease (AD) and seven cognitively normal subjects were scanned post-mortem at 7 Tesla. Hippocampal volumes were obtained with HarP. Six-micrometer-thick hippocampal slices were stained for amyloid beta (Aβ), tau, and cresyl violet. Hippocampal subfields were manually traced. Neuronal counts, Aβ, and tau burden for each hippocampal subfield were obtained. RESULTS: We found significant correlations between hippocampal volume and Braak and Braak staging (ρ = -0.75, P = .001), tau (ρ = -0.53, P = .034), Aβ burden (ρ = -0.61, P = .012), and neuronal count (ρ = 0.77, P < .001). Exploratory subfield-wise significant associations were found for Aβ in CA1 (ρ = -0.58, P = .019) and subiculum (ρ = -0.75, P = .001), tau in CA2 (ρ = -0.59, P = .016), and CA3 (ρ = -0.5, P = .047), and neuronal count in CA1 (ρ = 0.55, P = .028), CA3 (ρ = 0.65, P = .006), and CA4 (ρ = 0.76, P = .001). CONCLUSIONS: The observed associations provide the pathological confirmation of hippocampal morphometry as a valid biomarker for AD and the pathologic validation of HarP.
    Alzheimer's and Dementia 01/2015; DOI:10.1016/j.jalz.2015.01.001 · 17.47 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Aim: To assess Surface Based Morphometry (SBM) and Voxel Based Morphometry (VBM), the automated computation methods to demonstrate volume and thickness changes in brain among early Traumatic Brain Injury (TBI) and their correlation with cognitive test scores. Methods: 22 mild to moderate TBI patients and 20 age, gender matched healthy individuals were recruited (mean ± SD, age range: 27.7 ± 6.5 years). MRI scans were acquired in the Siemens 3T Magnetom Skyra Scanner. TheT1-weighted magnetization preparation rapid acquisition gradient echo (MP-RAGE) sequence used for morphometric analysis provided excellent gray-white matter contrast. The structural data was processed using SBM and VBM methods with statistical significance of P<0.05 corrected for multiple comparisons. Results: both methods did not show any significant changes in brain measures after correcting for false discovery rate. However, on correlating neuropsychological score with structural changes, SBM demonstrated significant voxels survived in animal naming and Token Test after correcting for multiple comparisons. No significant change was found while using VBM. Conclusion: The study emphasizes the similarities in the results obtained after using different automatedmethods. Our findings suggest that SBM is more sensitive as compared to VBM in detecting structural changes correlated with Neuropsychological scores during early phase of TBI.
    02/2014; 2. DOI:10.11131/2014/101069
  • [Show abstract] [Hide abstract]
    ABSTRACT: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF β-amyloid 1-42 (Aβ42) ≤192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio ≥1.5. We trained our classifier in the subcohort with CSF Aβ42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Aβ42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] = 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier self-tuning was allowed, AUC = 0.74. The 36-month disease-progression classifier achieved AUC = 0.75 and accuracy = 71%. Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. This study provides Class II evidence that a classification algorithm based on cognitive, imaging, and peripheral blood protein measures identifies patients with brain amyloid on PiB-PET with moderate accuracy (sensitivity 68%, specificity 78%). © 2015 American Academy of Neurology.
    Neurology 01/2015; 84(7). DOI:10.1212/WNL.0000000000001231 · 8.30 Impact Factor

Preview

Download
2 Downloads
Available from