Similar and Differing Distributions Between 18F-FDG-PET and Arterial Spin Labeling Imaging in Temporal Lobe Epilepsy

Background: Despite the increasing use of arterial spin labeling (ASL) in patients with epilepsy, little is known about its brain regional distribution pattern, including diaschisis, and its correspondence with FDG-PET. Here, we investigated the regional match and mismatch between FDG-PET and ASL in temporal lobe epilepsy (TLE).Methods: We recruited 27 patients with unilateral TLE, who underwent inter-ictal ASL and FDG-PET scans. These images were spatially normalized using Statistical Parametric Mapping 12, and the regional values in both ASL and FDG-PET were calculated using PMOD software within 20 volumes of interest (VOIs), including the temporal lobe, adjacent cortices, subcortical structures, and cerebellum. ASL images of 37 healthy controls were also analyzed and compared.Results: Whereas, ASL showed significant side differences, mainly in the temporal and frontal lobes, the significant abnormalities in FDG-PET were more widespread and included the insula and supramarginal gyrus. Ipsilateral thalamic reduction was found in FDG-PET only. The detectability of the focus side compared with the contralateral side was generally higher in FDG-PET. The discriminative values in ASL compared with healthy controls were higher in temporal neocortex and amygdala VOIs.Conclusions: There are similar and differing regional distributions between FDG-PET and ASL in TLE, possibly reflecting regional match and mismatch of cerebral blood flow and metabolism. At this stage, it seems that ASL couldn't present comparable clinical usefulness with FDG-PET. These findings deepen our knowledge of ASL imaging and are potentially useful for its further application

Abnormal neurite density and orientation dispersion in unilateral temporal lobe epilepsy detected by advanced diffusion imaging

BackgroundDespite recent advances in diffusion MRI (dMRI), there is still limited information on neurite orientation dispersion and density imaging (NODDI) in temporal lobe epilepsy (TLE). This study aimed to demonstrate neurite density and dispersion in TLE with and without hippocampal sclerosis (HS) using whole-brain voxel-wise analyses.Material and methodsWe recruited 33 patients with unilateral TLE (16 left, 17 right), including 14 patients with HS (TLE-HS) and 19 MRI-negative 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)-positive patients (MRI-/PET+ TLE). The NODDI toolbox calculated the intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Conventional dMRI metrics, that is, fractional anisotropy (FA) and mean diffusivity (MD), were also estimated. After spatial normalization, all dMRI parameters (ICVF, ODI, FA, and MD) of the patients were compared with those of age- and sex-matched healthy controls using Statistical Parametric Mapping 12 (SPM12). As a complementary analysis, we added an atlas-based region of interest (ROI) analysis of relevant white matter tracts using tract-based spatial statistics.ResultsWe found decreased neurite density mainly in the ipsilateral temporal areas of both right and left TLE, with the right TLE showing more severe and widespread abnormalities. In addition, etiology-specific analyses revealed a localized reduction in ICVF (i.e., neurite density) in the ipsilateral temporal pole in MRI-/PET+ TLE, whereas TLE-HS presented greater abnormalities, including FA and MD, in addition to a localized hippocampal reduction in ODI. The results of the atlas-based ROI analysis were consistent with the results of the SPM12 analysis.ConclusionNODDI may provide clinically relevant information as well as novel insights into the field of TLE. Particularly, in MRI-/PET+ TLE, neurite density imaging may have higher sensitivity than other dMRI parameters. The results may also contribute to better understanding of the pathophysiology of TLE with HS

Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

BackgroundsAlthough neurofibrillary tangles (NFTs) mainly accumulate in the medial temporal lobe with human aging, only a few imaging studies have investigated correlations between NFT accumulation and gray matter (GM) volume in cognitively normal older adults. Here, we investigated the correlations between 18F-THK5351 accumulation and GM volume at the voxel level.Material and methodsWe recruited 47 amyloid-negative, cognitively normal, older adults (65.0 ± 7.9 years, 26 women), who underwent structural magnetic resonance imaging, 11C-Pittsburgh compound-B and 18F-THK5351 PET scans, and neuropsychological assessment. The magnetic resonance and 18F-THK5351 PET images were spatially normalized using Statistical Parametric Mapping 12. Voxel-wise correlations between 18F-THK5351 accumulation and GM volume were evaluated using the Biological Parametric Mapping toolbox.ResultsA significant negative correlation (p < 0.001) between 18F-THK5351 accumulation and GM volume was detected in the bilateral medial temporal lobes.ConclusionsVoxel-wise correlation analysis revealed a significant negative correlation between 18F-THK5351 accumulation and GM volume in the medial temporal lobe in individuals without amyloid-β deposits. These results may contribute to a better understanding of the pathophysiology of primary age-related tauopathy in human aging

Dissociation of Tau Deposits and Brain Atrophy in Early Alzheimer’s Disease: A Combined Positron Emission Tomography/Magnetic Resonance Imaging Study

The recent advent of tau-specific positron emission tomography (PET) has enabled in vivo assessment of tau pathology in Alzheimer’s disease (AD). However, because PET scanners have limited spatial resolution, the measured signals of small brain structures or atrophied areas are underestimated by partial volume effects (PVEs). The aim of this study was to determine whether partial volume correction (PVC) improves the precision of measures of tau deposits in early AD. We investigated tau deposits in 18 patients with amyloid-positive early AD and in 36 amyloid-negative healthy controls using 18F-THK5351 PET. For PVC, we applied the SPM toolbox PETPVE12. The PET images were then spatially normalized and subjected to voxel-based group analysis using SPM12 for comparison between the early AD patients and healthy controls. We also compared these two groups in terms of brain atrophy using voxel-based morphometry of MRI. We found widespread neocortical tracer retention predominantly in the posterior cingulate and precuneus areas, but also in the inferior temporal lobes, inferior parietal lobes, frontal lobes, and occipital lobes in the AD patients compared with the controls. The pattern of tracer retention was similar between before and after PVC, suggesting that PVC had little effect on the precision of tau load measures. Gray matter atrophy was detected in the medial/lateral temporal lobes and basal frontal lobes in the AD patients. Interestingly, only a few associations were found between atrophy and tau deposits, even after PVC. In conclusion, PVC did not significantly affect 18F-THK5351 PET measures of tau deposits. This discrepancy between tau deposits and atrophy suggests that tau load precedes atrophy

Japanese multicenter database of healthy controls for [¹²³I]FP-CIT SPECT

Purpose: The aim of this multicenter trial was to generate a [¹²³I]FP-CIT SPECT database of healthy controls from the common SPECT systems available in Japan. Methods: This study included 510 sets of SPECT data from 256 healthy controls (116 men and 140 women; age range, 30–83 years) acquired from eight different centers. Images were reconstructed without attenuation or scatter correction (NOACNOSC), with only attenuation correction using the Chang method (ChangACNOSC) or X-ray CT (CTACNOSC), and with both scatter and attenuation correction using the Chang method (ChangACSC) or X-ray CT (CTACSC). These SPECT images were analyzed using the Southampton method. The outcome measure was the specific binding ratio (SBR) in the striatum. These striatal SBRs were calibrated from prior experiments using a striatal phantom. Results: The original SBRs gradually decreased in the order of ChangACSC, CTACSC, ChangACNOSC, CTACNOSC, and NOACNOSC. The SBRs for NOACNOSC were 46% lower than those for ChangACSC. In contrast, the calibrated SBRs were almost equal under no scatter correction (NOSC) conditions. A significant effect of age was found, with an SBR decline rate of 6.3% per decade. In the 30–39 age group, SBRs were 12.2% higher in women than in men, but this increase declined with age and was absent in the 70–79 age group. Conclusions: This study provided a large-scale quantitative database of [¹²³I]FP-CIT SPECT scans from different scanners in healthy controls across a wide age range and with balanced sex representation. The phantom calibration effectively harmonizes SPECT data from different SPECT systems under NOSC conditions. The data collected in this study may serve as a reference database

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.</p

Harmonized Z-Scores Calculated from a Large-Scale Normal MRI Database to Evaluate Brain Atrophy in Neurodegenerative Disorders

Alzheimer’s disease (AD), the most common type of dementia in elderly individuals, slowly and progressively diminishes the cognitive function. Mild cognitive impairment (MCI) is also a significant risk factor for the onset of AD. Magnetic resonance imaging (MRI) is widely used for the detection and understanding of the natural progression of AD and other neurodegenerative disorders. For proper assessment of these diseases, a reliable database of images from cognitively healthy participants is important. However, differences in magnetic field strength or the sex and age of participants between a normal database and an evaluation data set can affect the accuracy of the detection and evaluation of neurodegenerative disorders. We developed a brain segmentation procedure, based on 30 Japanese brain atlases, and suggest a harmonized Z-score to correct the differences in field strength and sex and age from a large data set (1235 cognitively healthy participants), including 1.5 T and 3 T T1-weighted brain images. We evaluated our harmonized Z-score for AD discriminative power and classification accuracy between stable MCI and progressive MCI. Our procedure can perform brain segmentation in approximately 30 min. The harmonized Z-score of the hippocampus achieved high accuracy (AUC = 0.96) for AD detection and moderate accuracy (AUC = 0.70) to classify stable or progressive MCI. These results show that our method can detect AD with high accuracy and high generalization capability. Moreover, it may discriminate between stable and progressive MCI. Our study has some limitations: the age groups in the 1.5 T data set and 3 T data set are significantly different. In this study, we focused on AD, which is primarily a disease of elderly patients. For other diseases in different age groups, the harmonized Z-score needs to be recalculated using different data sets

Histogram-Based Feature Extraction from Individual Gray Matter Similarity-Matrix for Alzheimer's Disease Classification

Automatic computer-aided diagnosis (CAD) systems have been widely used in classification of patients who suffer from Alzheimer's disease (AD). This paper presents an automatic CAD system based on histogram feature extraction from single-subject gray matter similarity-matrix for classifying the AD patients from healthy controls (HC) using structural magnetic resonance imaging (MRI) data. The proposed CAD system is composed of five stages. In the first stage, segmentation is employed to perform pre-processing on the MRI images, and segment into gray matter, white matter, and cerebrospinal fluid using the voxel-based morphometric toolbox procedure. In the second stage, gray matter MRI scans are used to construct similarity-matrices. In the third stage, a novel statistical feature-generation process is proposed, utilizing the histogram of the individual similarity-matrix to represent statistical patterns of the respective similarity-matrices of different size and order into fixed-size feature-vectors. In the fourth stage, we propose to combine MRI measures with a neuropsychological test, the Functional Assessment Questionnaire (FAQ), to improve the classification accuracy. Finally, the classification is performed using a support vector machine and evaluated with the 10-fold cross-validation strategy. We evaluated the proposed method on 99 AD and 102 HC subjects from the J-ADNI. The proposed CAD system yields an 84.07% classification accuracy using MRI measures and 97.01% for combining MRI measures with FAQ scores, respectively. The experimental results indicate that the performance of the proposed system is competitive with respect to state-of-the-art techniques reported in the literature

Gray Matter and White Matter Abnormalities in Temporal Lobe Epilepsy Patients with and without Hippocampal Sclerosis

The presentation and distribution of gray matter (GM) and white matter (WM) abnormalities in temporal lobe epilepsy (TLE) have been widely studied. Here, we investigated the GM and WM abnormalities in TLE patients with and without hippocampal sclerosis (HS) in five groups of participants: healthy controls (HCs) (n = 28), right TLE patients with HS (n = 26), right TLE patients without HS (n = 30), left TLE patients with HS (n = 25), and left TLE patients without HS (n = 27). We performed a flexible factorial statistical test in a whole-brain voxel-based morphometry analysis to identify significant GM and WM abnormalities and analysis of variance of hippocampal and amygdala regions among the five groups using the FreeSurfer procedure. Furthermore, we conducted multiple regression analysis to assess regional GM and WM changes with disease duration. We observed significant ipsilateral mesiotemporal GM and WM volume reductions in TLE patients with HS compared with HCs. We also observed a slight GM amygdala swelling in right TLE patients without HS. The regression analysis revealed significant negative GM and WM changes with disease duration specifically in left TLE patients with HS. The observed GM and WM abnormalities may contribute to our understanding of the root of epilepsy mechanisms