Search

We developed a novel quantification method named "shape feature" by combining the features of amyloid positron emission tomography (PET) and brain magnetic resonance imaging (MRI) and evaluated its significance in predicting the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. From the ADNI database, 334 patients with MCI were included. The brain amyloid smoothing score (AV45_BASS) and brain atrophy index (MR_BAI) were calculated using the surface area and volume of the region of interest in AV45 PET and MRI. During the 48-month follow-up period, 108 (32.3%) patients converted from MCI to AD. Age, Mini-Mental State Examination (MMSE), cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog), apolipoprotein E (APOE), standardized uptake value ratio (SUVR), AV45_BASS, MR_BAI, and shape feature were significantly different between converters and non-converters. Univariate analysis showed that age, MMSE, ADAS-cog, APOE, SUVR, AV45_BASS, MR_BAI, and shape feature were correlated with the conversion to AD. In multivariate analyses, high shape feature, SUVR, and ADAS-cog values were associated with an increased risk of conversion to AD. In patients with MCI in the ADNI cohort, our quantification method was the strongest prognostic factor for predicting their conversion to AD. [ABSTRACT FROM AUTHOR]

Imaging biomarkers support the diagnosis of Alzheimer's disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065). [ABSTRACT FROM AUTHOR]

Purpose: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clinically relevant issue is the selection of high-risk subjects who need active surveillance among equivocal cases. We validated the clinical feasibility of DL compared with visual rating or quantitative measurement for assessing the diagnosis and prognosis of subjects with equivocal amyloid scans. Methods: 18F-florbetaben scans of 430 cases (85 NC, 233 mild cognitive impairment, and 112 AD) were assessed through visual rating-based, quantification-based, and DL-based methods. DL was trained using 280 two-dimensional PET images (80%) and tested by randomly assigning the remaining (70 cases, 20%) cases and a clinical validation set of 54 equivocal cases. In the equivocal cases, we assessed the agreement among the visual rating, quantification, and DL and compared the clinical outcome according to each modality-based amyloid status. Results: The visual reading was positive in 175 cases, equivocal in 54 cases, and negative in 201 cases. The composite SUVR cutoff value was 1.32 (AUC 0.99). The subject-level performance of DL using the test set was 100%. Among the 54 equivocal cases, 37 cases were classified as positive (Eq(deep+)) by DL, 40 cases were classified by a second-round visual assessment, and 40 cases were classified by quantification. The DL- and quantification-based classifications showed good agreement (83%, κ = 0.59). The composite SUVRs differed between Eq(deep+) (1.47 [0.13]) and Eq(deep−) (1.29 [0.10]; P < 0.001). DL, but not the visual rating, showed a significant difference in the Mini-Mental Status Examination score change during the follow-up between Eq(deep+) (− 4.21 [0.57]) and Eq(deep−) (− 1.74 [0.76]; P = 0.023) (mean duration, 1.76 years). Conclusions: In visually equivocal scans, DL was more related to quantification than to visual assessment, and the negative cases selected by DL showed no decline in cognitive outcome. DL is useful for clinical diagnosis and prognosis assessment in subjects with visually equivocal amyloid scans. [ABSTRACT FROM AUTHOR]

Purpose: To assess the in vivo whole-brain relationship between uptake of [18F]THK-5351 on PET and cortical atrophy on structural MRI according to the presence and severity of Alzheimer's disease (AD). Materials and methods: Sixty-five participants (21 normal controls, 32 mild cognitive impairment [MCI] subjects, and 12 AD patients) were enrolled from a prospective multicenter clinical trial (NCT02656498). Structural MRI and [18F]THK-5351 PET were performed within a 2-month interval. Cortical volume and standardized uptake value ratios (SUVR) were calculated from MRI and PET images, respectively, for 35 FreeSurfer-derived cortical regions. Pearson's correlation coefficients between SUVR and cortical volume were calculated for the same regions, and correlated regions were compared according to disease severity and β-amyloid PET positivity. Results: No significantly correlated regions were found in the normal controls. Negative correlations between SUVR and cortical volume were found in the MCI and AD groups, mainly in limbic locations in MCI and isocortical locations in AD. The AD group exhibited stronger correlations (r = −0.576–0.781) than the MCI group (r = 0.368–0.571). Hippocampal atrophy did not show any correlation with SUVR in the β-amyloid PET-negative group, but negatively correlated with SUVR (r = −0.494, P =.012) in the β-amyloid PET-positive group. Conclusions: Regional THK-5351 uptake correlated more strongly with cortical atrophy in AD compared with MCI, thereby demonstrating a close relationship between the neuro-pathologic process and cortical atrophy. Hippocampal atrophy was associated with both β-amyloid and THK-5351 uptake, possibly reflecting an interaction between β-amyloid and tau deposition in the neurodegeneration process. [ABSTRACT FROM AUTHOR]

Abstract The concept of cognitive reserve (CR) originated from discrepancies between the degree of brain pathology and the severity of clinical manifestations. CR has been characterized through CR proxies, such as education and occupation complexity; however, such approaches have inherent limitations. Although several methods have been developed to overcome these limitations, they fail to reflect the entire Alzheimer's disease (AD) pathology. Meanwhile, graph theory analysis, one of most powerful and flexible approaches, have established remarkable network properties of the brain. The functional and structural brain networks are damaged in neurodegenerative diseases. Therefore, network analysis has been applied to clarify the characteristics of the disease or give insight. Here, using multimodal neuroimaging, we propose an intuitive model to estimate CR based on its original definition, and explore the neural substrates of CR from the perspective of networks and functional connectivity. A total of 87 subjects (21 AD, 32 mild cognitive impairment, and 34 normal aging) underwent tau and amyloid PET, 3D T1-weighted MR, and resting-state fMRI. We hypothesized CR as a residual of actual cognitive performance and expected performance to be related to quantitative factors, such as AD pathology, demographics, and a genetic factor. Then, we correlated this marker using education and occupation complexity as conventional CR proxies. We validated this marker by testing whether it would modulate the effect of brain pathology on memory function. To examine the neural substrates associated with CR, we performed graph analysis to investigate the association between the CR marker and network measures at different granularities in total subjects, AD spectrum and normal aging, respectively. The CR marker from our model was well associated with education and occupation complexity. More directly, the CR marker was revealed to modify the relationship between brain pathology and memory function among AD spectrum. The CR marker was correlated with the global efficiency of the entire network, nodal clustering coefficient, and local efficiency of the right middle-temporal pole. In connectivity analysis, one cluster of edges centered on right middle-temporal pole was significantly correlated with the CR marker. In subgroup analysis, the network measures of right middle-temporal pole still correlated with the CR marker among AD spectrum. However, right precentral gyrus was revealed to be associated with the CR marker in normal aging. This study demonstrates that our intuitive model using multimodal neuroimaging and network perspective adequately and comprehensively captures CR. From a network perspective, CR is associated with the capacity to process information efficiently in the brain. The right middle-temporal pole was revealed to be a pivotal neural substrate of CR in AD spectrum. These findings foster understanding of AD and will be useful to help identify individuals with vulnerability or resistance to AD pathology, and characterize patients for intervention or drug trials. Highlights • We generated an equation to quantify cognitive reserve (CR) reflecting overall AD neuropathology using multimodal neuroimaging techniques. • CR was proved to modify the relationship between brain pathology and memory function in AD spectrum. • CR was associated with global network efficiency and nodal clustering coefficient and local efficiency of the right middle-temporal pole. • Graph parameters of right middle-temporal pole was revealed to modulate the effect of brain atrophy on cognition in AD spectrum. [ABSTRACT FROM AUTHOR]