Hyun Jin Yoon

Regional cerebral blood flow differences in patients with mild cognitive impairment between those who did and did not develop Alzheimer's disease

Mild cognitive impairment (MCI) is a heterogeneous condition associated with increased risk of Alzheimers disease (AD) and other dementias. This study aimed to identify areas of initial hypoperfusion in MCI conversion to AD using technetium (Tc-99m) hexamethylpropyleneamine oxime (TC-99m HMPAO) single photon emission computed tomography (SPECT) to compare baseline cerebral hypoperfusion in converted MCI and non-converted MCI patients and normal controls. Forty-nine MCI patients were recruited for brain magnetic resonance imaging (MRI), detailed neuropsychological testing, Tc-99m HMPAO SPECT, and 1- to 2-year periodic follow-up to monitor progression to dementia status. We processed SPECT images with Statistical Parametric Mapping 8 (SPM8) software and performed voxel-based statistical parametric mapping analysis. Thirty-nine of 49 MCI patients were included in our analysis. Nine patients were diagnosed with conversion to AD, on average 19.0±6.6 months after initial assessment. Compared with normal controls, converted MCI patients demonstrated perfusion deficits in both parahippocampal gyri and right precuneus, and non-converted MCI patients demonstrated hypoperfusion in the left parahippocampal gyrus. Compared with non-converted MCI patients, converted MCI patients demonstrated significant hypoperfusion in both cingulate gyri and right precuneus. Our study suggests that using brain SPECT to identify initial hypoperfusion in patients with MCI may be helpful for predicting MCI patients likely to develop AD.

Visual and Quantitative Analysis Methods of Respiratory Patterns for Respiratory Gated PET/CT.

We integrated visual and quantitative methods for analyzing the stability of respiration using four methods: phase space diagrams, Fourier spectra, Poincaré maps, and Lyapunov exponents. Respiratory patterns of 139 patients were grouped based on the combination of the regularity of amplitude, period, and baseline positions. Visual grading was done by inspecting the shape of diagram and classified into two states: regular and irregular. Quantitation was done by measuring standard deviation of x and v coordinates of Poincaré map (SDx, SDv) or the height of the fundamental peak (A 1) in Fourier spectrum or calculating the difference between maximal upward and downward drift. Each group showed characteristic pattern on visual analysis. There was difference of quantitative parameters (SDx, SDv, A 1, and MUD-MDD) among four groups (one way ANOVA, p = 0.0001 for MUD-MDD, SDx, and SDv, p = 0.0002 for A 1). In ROC analysis, the cutoff values were 0.11 for SDx (AUC: 0.982, p < 0.0001), 0.062 for SDv (AUC: 0.847, p < 0.0001), 0.117 for A 1 (AUC: 0.876, p < 0.0001), and 0.349 for MUD-MDD (AUC: 0.948, p < 0.0001). This is the first study to analyze multiple aspects of respiration using various mathematical constructs and provides quantitative indices of respiratory stability and determining quantitative cutoff value for differentiating regular and irregular respiration.

Functional volumetric analysis of striatum using F-18 FP-CIT PET in patients with idiopathic Parkinson's disease and normal subjects.

ObjectiveWe applied a simple isocontour volume-of-interest (VOI) method to analyze the whole striatum in an F-18 FP-CIT PET image and to investigate the usefulness of the method in differentiating healthy subjects from idiopathic Parkinson’s disease (IPD) patients and the correlation of the value of functional volume parameters with the motor symptoms in patients with IPD.MethodsForty-three IPD patients and 23 age-matched healthy controls underwent F-18 FP-CIT PET. Using a dedicated workstation, VOIs for the whole striatum were drawn automatically with the gradient delineation method. The SUVmax, SUVmean, functional volume (FV), striatal volume activity (SVA), striatal-specific binding (SSB), and volume-specific uptake ratio (VSUR) were compared between the IPD patients and the normal subjects. In the IPD patients, the correlation between the clinical factor and the functional parameters was assessed.ResultsThe SUVmax, SUVmean, FV, SVA, SSB, and VSUR were significantly lower in the IPD patients than in the normal subjects. In the receiver operating characteristic analysis, those parameters had significant and good-to-excellent accuracy. In the patients with IPD, a moderate negative correlation was revealed between the SUVmax and H&Y stage, the SUVmean and H&Y stage, SVA and H&Y stage, the VSUR and H&Y stage, the FV and bradykinesia, and the SVA and bradykinesia.ConclusionThe functional volumetric analysis of the striatum based on simple isocontour VOI was a useful method of analyzing the F-18 FP-CIT PET image. Not only can it be easily applied in daily clinical practice, but it can also be used as a clinical parameter to discriminate IPD and to correlate it with the disease severity.

Significant correlation between cerebral hypoperfusion and neuropsychological assessment scores of patients with mild cognitive impairment.

PurposeThe regions of significant correlation between cerebral hypoperfusion and neuropsychological assessment scores were evaluated using 99mTc-HMPAO SPECT in patients with mild cognitive impairment (MCI) on the basis of its three subtypes, namely, single-domain amnestic MCI (aMCI-s), multiple-domain amnestic MCI (aMCI-m), and nonamnestic MCI (naMCI), following which comparisons were made among the three subtypes of MCI. MethodsRegions of hypoperfusion were determined by comparing the three groups with the normal group. Neuropsychological assessment included tests to evaluate attention, language and related functions, visuospatial function, memory, frontal-executive function, and mini-mental state examination and depression scores. Regions of cerebral hypoperfusion were identified by comparing the three groups of MCI patients with the normal group (P<0.05, uncorrected). One-way analysis of variance was used to examine differences across groups, and post-hoc a-priori pairwise comparisons were used for between-group analyses. The regions of significant correlation, related to the neuropsychological assessment scores, were identified by simple regression of SPM8 within the masking image of the area of cerebral hypoperfusion (P<0.05, uncorrected). ResultsThe regions of cerebral hypoperfusion were identified by comparing members of the normal group with patients with aMCI-s, aMCI-m, and naMCI. The patients with aMCI-m showed significant correlation with all neuropsychological assessment scores, but the patients with aMCI-s correlated with four neuropsychological assessment scores of attention. The patients with naMCI revealed no significantly correlated regions (P<0.05, uncorrected). The regions that correlated with neuropsychological assessment scores in patients with aMCI-s were very small compared with those in patients with aMCI-m. The correlated regions in patients with aMCI-m were restricted to the left cerebrum and cerebellum. Brain areas showed significant correlation between neuropsychological assessment scores and hypoperfusion, which was evaluated by simple regression with the threshold being P less than 0.05, uncorrected. Rey complex figure test 20 min delayed, Korean-color word stroop test word reading, and Korean mini-mental state examination scores correlated more strongly with cerebral hypoperfusion compared with other assessment scores. ConclusionThe specific pattern of significant correlation of cerebral hypoperfusion with neuropsychological assessment scores was classified into three subtypes (aMCI-s, aMCI-m, and naMCI) according to the patients’ deficits in their cognitive domains.

Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia

Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson’s Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

Pattern of cerebral hypoperfusion according to the clinical staging in dementia with Lewy bodies

ABSTRACT This study aimed to detect different patterns of cerebral hypoperfusion in DLB according to clinical staging. Thirty-three patients with DLB were recruited by clinical dementia rating (CDR) stage. Compared with control, cerebral hypoperfusion was mainly observed in the lingual gyrus, the cuneus, the occipital gyrus in CDR 0.5 group; the fusiform gyrus, the middle temporal gyrus, and the posterior cingulate in CDR 1; and the lingual gyrus, the cuneus, the hippocampus, the fusiform gyrus, and the inferior frontal gyrus in CDR 2. Our findings suggest that cerebral hypoperfusion spreads to the frontal cortex and temporal lobes as disease progresses.

Assessment of change in glucose metabolism in white matter of amyloid-positive patients with Alzheimer disease using F-18 FDG PET

Abstract In Alzheimer disease (AD), neuroinflammation is an important process related to the deposition of beta-amyloid plaques and the activation of microglia. The inflammatory process can occur in both the gray matter and the white matter. We evaluated glucose metabolism of the white matter in AD patients and compared the value with cognitive parameters of the patients. Eighteen AD patients and 18 healthy subjects underwent F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben positron emission tomography (PET). After segmentation of the white matter in both PET images, the specific binding ratio (SBR) of the global and regional cerebral white matter was checked. We evaluated the differences in SBR of the global and regional white matter between AD patients and healthy subjects. Then, we assessed the correlation between SBR and cognitive parameters in AD patients. In F-18 FDG PET images, the global white matter SBR was significantly higher in AD patients than in healthy subjects. In the regional analysis, the white matter SBR was significantly higher for the frontal, temporal, and parietal areas in AD patients. In the correlation analysis with F-18 FDG PET, SBR was significantly correlated with the Global Deterioration Scale, Mini-Mental State Examination scores, and amyloid deposition. Glucose metabolism of the white matter was significantly higher in AD patients than in healthy subjects and it was related to the scores of cognitive parameters. We suggest that F-18 FDG PET, like 18-kDa translocator protein PET, could be used as an indicator of neuroinflammation; however, further research is needed for a direct comparison between the 2 tests.