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PurposeWe estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.MethodsData from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer's Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2.ResultsAmyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value

Background: Little is known regarding the differential effects of the apolipoprotein E (APOE) ɛ4 on the regional topography of amyloid and tau in patients with both early-onset (EOAD) and late-onset Alzheimer's disease (LOAD). Objective: To compare the distribution and association of tau, amyloid, and cortical thickness among groups classified by the presence of APOEɛ4 allele and onset age. Methods: A total of 165 participants including 54 EOAD patients (29 ɛ4-; 25 ɛ4+), 45 LOAD patients (21 ɛ4-; 24 ɛ4+), and 66 age-matched controls underwent 3T MRI, 18F-THK5351 (THK) and 18F-flutemetamol (FLUTE) PET scans, APOE genotyping, and neuropsychological tests. Data for voxel-wise and standardized uptake values from PET scans were analyzed in the context of APOE and age at onset. Results: EOAD ɛ4- patients showed greater THK retention in the association cortices, whereas their EOAD ɛ4+ counterparts had more retention in medial temporal areas. THK topography of LOAD ɛ4+ was similar to EOAD ɛ4 +. THK correlated positively with FLUTE and conversely with mean cortical thickness, being lowest in EOAD ɛ4-, highest in LOAD ɛ4-, and modest in ɛ4+ groups. Even in the APOEɛ4+ groups, THK tended to correlate with FLUTE and mean cortical thickness in the inferior parietal region in EOAD and in the medial temporal region in LOAD. LOAD ɛ4- manifested with prevalent small vessel disease markers and the lowest correlation between THK retention and cognition. Conclusion: Our observations suggest the differential effects of the APOEɛ4 on the relationship between tau and amyloid in EOAD and LOAD. [ABSTRACT FROM AUTHOR]

Objective: We investigated the mediation effects of subcortical volume change in the relationship of amyloid beta (Aβ) and lacune with cognitive function in patients with mild cognitive impairment (MCI). Methods: We prospectively recruited 101 patients with MCI who were followed up with neuropsychological tests, MRI, or Pittsburgh compound B (PiB) PET for 3 years. The mediation effect of subcortical structure on the association of PiB or lacunes with cognitive function was analyzed using mixed effects models. Results: Volume changes in the amygdala and hippocampus partially mediated the effect of PiB changes on memory function (direct effect = −0.168/−0.175, indirect effect = −0.081/−0.077 for amygdala/hippocampus) and completely mediated the effect of PiB changes on clinical dementia rating scale sum of the box (CDR-SOB) (indirect effect = 0.082/0.116 for amygdala/hippocampus). Volume changes in the thalamus completely mediated the effect of lacune on memory, frontal executive functions, and CDR-SOB (indirect effect = −0.037, −0.056, and 0.047, respectively). Conclusions: Our findings provide a better understanding of the distinct role of subcortical structures in the mediation of the relationships of amyloid or vascular changes with a decline in specific cognitive domains. [ABSTRACT FROM AUTHOR]

Suicide is among the most important global health concerns; accordingly, an increasing number of studies have shown the risks for suicide attempt(s) in terms of brain morphometric features and their clinical correlates. However, brain studies addressing suicidal vulnerability have been more focused on demonstrating impairments in cortical structures than in the subcortical structures. Using local shape volumes (LSV) analysis, we investigated subcortical structures with their clinical correlates in depressed patients who attempted suicide. Then we compared them with depressed patients without a suicidal history and age‐ and sex‐matched healthy controls (HCs; i.e., 47 suicide attempters with depression, 47 non‐suicide attempters with depression, and 109 HCs). Significant volumetric differences were found between suicidal and nonsuicidal depressed patients in several vertices: 16 in the left amygdala; 201 in the left hippocampus; 1,057 in the left putamen; and 140 in the left pallidum; 1 in the right pallidum; and 6 in the bilateral thalamus. These findings indicated subcortical alterations in LSV in components of the limbic–cortical–striatal–pallidal–thalamic circuits. Moreover, our results demonstrated that the basal ganglia was correlated with perceived stress levels, and the thalamus was correlated with suicidal ideation. We suggest that suicidality in major depressive disorder may involve subcortical volume alterations. [ABSTRACT FROM AUTHOR]

Purpose: We estimated whether amyloid involvement in subcortical regions may predict cognitive impairment, and established an amyloid staging scheme based on degree of subcortical amyloid involvement.Methods: Data from 240 cognitively normal older individuals, 393 participants with mild cognitive impairment, and 126 participants with Alzheimer disease were acquired at Alzheimer’s Disease Neuroimaging Initiative sites. To assess subcortical involvement, we analyzed amyloid deposition in amygdala, putamen, and caudate nucleus. We staged participants into a 3-stage model based on cortical and subcortical amyloid involvement: 382 with no cortical or subcortical involvement as stage 0, 165 with cortical but no subcortical involvement as stage 1, and 203 with both cortical and subcortical involvement as stage 2.Results: Amyloid accumulation was first observed in cortical regions and spread down to the putamen, caudate nucleus, and amygdala. In longitudinal analysis, changes in MMSE, ADAS-cog 13, FDG PET SUVR, and hippocampal volumes were steepest in stage 2 followed by stage 1 then stage 0 (p value <0.001). Stage 2 showed steeper changes in MMSE score (β [SE] = −0.02 [0.004], p < 0.001), ADAS-cog 13 (0.05 [0.01], p < 0.001), FDG PET SUVR (−0.0008 [0.0003], p = 0.004), and hippocampal volumes (−4.46 [0.65], p < 0.001) compared to stage 1.Conclusions: We demonstrated a downward spreading pattern of amyloid, suggesting that amyloid accumulates first in neocortex followed by subcortical structures. Furthermore, our new finding suggested that an amyloid staging scheme based on subcortical involvement might reveal how differential regional accumulation of amyloid affects cognitive decline through functional and structural changes of the brain. [ABSTRACT FROM AUTHOR]

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45–91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging. [ABSTRACT FROM AUTHOR]

Background: β amyloid deposition, pathological tau, and neurodegeneration are regarded as critical biomarkers to biologically define Alzheimer's disease (AD) in living people. Development of tracers for positron emission tomography (PET) enables extracellular amyloid plaques and intraneuronal neurofibrillary tangles to be detected in vivo. Identifying in vivo spatiotemporal dynamics of those three biomarkers could be a key to understand the disease progression in Alzheimer's continuum. Methods: We recruited 74 healthy controls, 61 amnestic mild cognitive impairment patients, and 38 AD patients with T1‐weighted magnetic resonance image (MRI) and two PET scans ([18F]Flortaucipir for tau and [18F]Florbetaben for Aβ) with about two‐year follow‐up data. Longitudinal W‐scores were calculated from standardized uptake value ratios (SUVRs) for two PET data and cortical thicknesses for MRI data. All participants were aligned in the order of standardized baseline tau SUVRs using the frequency‐based tau spreading model. Partial correlations (PC) were then computed between every regional pair of baseline and follow‐up data for each biomarker within a time window involving a part of participants. Directed graphs were constructed based on the PCs for each biomarker by moving the time window through the temporal participant‐orders. Results: Amyloid seemed to follow the Thal stage. With the early window most connections existed within neocortex, and the connections moved to allocortex. Tau also seemed to follow the Braak stage showing the connections advanced from entorhinal regions to neocortex as the subject window moves to late order. However, temporal domain of amyloid and tau connections within the same area were distinct each other. Especially for tau, we found that the network hubs varied temporarily and diameters of the temporal graphs seemed to increase substantially as the hub moves out of the medial temporal lobe. Neurodegeneration showed similar trends to tau with later temporal domain. Conclusions: In this study, we suggested the in vivo temporal alterations of interregional affections for amyloid, tau, and neurodegeneration from asymptomatic to clinical AD. All biomarkers exhibited analogous trends to previous researches and the moments of appearances for each biomarker were different. To detect these peculiar spatiotemporal characteristics between biomarkers might be valuable to figure out AD progression. [ABSTRACT FROM AUTHOR]

Purpose: Alzheimer's disease (AD) dementia may not be a single disease entity. Early-onset AD (EOAD) and late-onset AD (LOAD) have been united under the same eponym of AD until now, but disentangling the heterogeneity according to the age of sonset has been a major tenet in the field of AD research., Materials and Methods: Ninety-nine patients with AD (EOAD, n=54; LOAD, n=45) and 66 cognitively normal controls completed both [ 18 F]THK5351 and [ 18 F]flutemetamol (FLUTE) positron emission tomography scans along with structural magnetic resonance imaging and detailed neuropsychological tests., Results: EOAD patients had higher THK retention in the precuneus, parietal, and frontal lobe, while LOAD patients had higher THK retention in the medial temporal lobe. Intravoxel correlation analyses revealed that EOAD presented narrower territory of local FLUTE-THK correlation, while LOAD presented broader territory of correlation extending to overall parieto-occipito-temporal regions. EOAD patients had broader brain areas which showed significant negative correlations between cortical thickness and THK retention, whereas in LOAD, only limited brain areas showed significant correlation with THK retention. In EOAD, most of the cognitive test results were correlated with THK retention. However, a few cognitive test results were correlated with THK retention in LOAD., Conclusion: LOAD seemed to show gradual increase in tau and amyloid, and those two pathologies have association to each other. On the other hand, in EOAD, tau and amyloid may develop more abruptly and independently. These findings suggest LOAD and EOAD may have different courses of pathomechanism., Competing Interests: The authors have no potential conflicts of interest to disclose., (© Copyright: Yonsei University College of Medicine 2024.)

Background: In neuromyelitis optica spectrum disorder (NMOSD), brain involvement is common and cognitive dysfunction is frequently found. The study investigated alterations of white matter (WM) connectivity using graph theory and correlations with cognitive dysfunction in patients with NMOSD. Methods: We prospectively enrolled patients with NMOSD ( N = 14) and age- and sex-matched healthy controls ( N = 21). Structural connections between any pair of the 90 cortical and subcortical regions were established using diffusion tensor imaging and graph theory. Network-based statistics was employed to assess differences in WM connectivity between the NMOSD and healthy control groups. We further investigated the relationship between the topological network characteristics and cognitive test performances. Results: WM network analysis showed decreased total strength of brain networks and two disrupted sub-networks in patients with NMOSD. The first featured six hub nodes in the rectus, hippocampus, calcarine, cuneus, and precuneus with the left-sided predominance. The second had six hub nodes in the orbitomiddle frontal, post-central, superior parietal, superior, and middle temporal, and caudate with the right-sided predominance. Compared to healthy controls, NMOSD patients showed poor performance on tests for attention/working memory and processing speed, visuospatial processing, and executive function, which were associated with significant decreases in nodal clustering coefficient, local efficiency, and regional efficiency in the disrupted sub-networks (all p < 0.05). Conclusions: The data show the overall WM disruption and the relationship between poor cognitive function and sub-network alterations identified by the network analysis in NMOSD patients. We suggest that cognitive dysfunction is related to dysconnectivity of WM network including default mode network in NMOSD.

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

Cortical thinning patterns in Alzheimer's disease (AD) have been widely reported through conventional regional analysis. In addition, the coordinated variance of cortical thickness in different brain regions has been investigated both at the individual and group network levels. In this study, we aim to investigate network architectural characteristics of a structural covariance network (SCN) in AD, and further to show that the structural covariance connectivity becomes disorganized across the brain regions in AD, while the normal control (NC) subjects maintain more clustered and consistent coordination in cortical atrophy variations. We generated SCNs directly from T1-weighted MR images of individual patients using surface-based cortical thickness data, with structural connectivity defined as similarity in cortical thickness within different brain regions. Individual SCNs were constructed using morphometric data from the Samsung Medical Center (SMC) dataset. The structural covariance connectivity showed higher clustering than randomly generated networks, as well as similar minimum path lengths, indicating that the SCNs are "small world." There were significant difference between NC and AD group in characteristic path lengths (z = -2.97, p < 0.01) and small-worldness values (z = 4.05, p < 0.01). Clustering coefficients in AD was smaller than that of NC but there was no significant difference (z = 1.81, not significant). We further observed that the AD patients had significantly disrupted structural connectivity. We also show that the coordinated variance of cortical thickness is distributed more randomly from one region to other regions in AD patients when compared to NC subjects. Our proposed SCN may provide surface-based measures for understanding interaction between two brain regions with co-atrophy of the cerebral cortex due to normal aging or AD. We applied our method to the AD Neuroimaging Initiative (ADNI) data to show consistency in results with the SMC dataset.