Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis

The Alzheimer's Disease Neuroimaging Initiative,Lee, Y.B.,Lee, J.,Tak, S.,Lee, K.,Na, D.L.,Seo, S.W.,Jeong, Y.,Ye, J.C. Academic Press 2016 NeuroImage Vol.125 No.-

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease.

Gene Interactions and Structural Brain Change in Early-Onset Alzheimer’s Disease Subjects Using the Pipeline Environment

문석우,Ivo D. Dinov,Alen Zamanyan,Ran Shi,Alex Genco,Sam Hobel,Paul M. Thompson,Arthur W. Toga,Alzheimer’s Disease Neuroimaging Initiative 대한신경정신의학회 2015 PSYCHIATRY INVESTIGATION Vol.12 No.1

ObjectiveaaThis article investigates subjects aged 55 to 65 from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers. MethodsaaNine of the subjects had EO-AD (Alzheimer’s disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers. ResultsaaWe identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area). ConclusionaaWe observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

Improved Diagnostic Accuracy of Alzheimer’s Disease by Combining Regional Cortical Thickness and Default Mode Network Functional Connectivity: Validated in the Alzheimer’s Disease Neuroimaging Initiative Set

박지은,박범우,김상준,김호성,최충곤,정승채,오주영,이재홍,노지훈,심우현,Alzheimer’s Disease Neuroimaging Initiative (ADNI) 대한영상의학회 2017 Korean Journal of Radiology Vol.18 No.6

Objective: To identify potential imaging biomarkers of Alzheimer’s disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model’s diagnostic accuracy in a validation set. Materials and Methods: Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer’s Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Results: Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Conclusion: Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer’s disease.

Cerebrospinal Biomarker Cut-off Methods Defined Only by Alzheimer’s Disease Predict More Precisely Conversions of Mild Cognitive Impairment

김종헌,임현선,이지언,조정희,김규식,최성혜,이준홍,Alzheimer’s Disease Neuroimaging Initiative 대한치매학회 2017 Dementia and Neurocognitive Disorders Vol.16 No.4

Background and Purpose The cerebrospinal fluid (CSF) biomarkers play an important supportive role as diagnostic and predictive indicators of Alzheimer’s disease (AD). About 30% of controls in old age show abnormal values of CSF biomarkers and display a higher risk for AD compared with those showing normal values. The cut-off values are determined by their diagnostic accuracy. However, the current cut-off values may be less accurate, because controls include high-risk groups of AD. We sought to develop models of patients with AD, who are homogenous for CSF biomarkers. Methods We included participants who had CSF biomarker data in the Alzheimer’s Disease Neuroimaging Initiative database. We investigated the factors related to CSF biomarkers in patients with AD using linear mixed models. Using the factors, we developed models corresponding to CSF biomarkers to classify patients with mild cognitive impairment (MCI) into high risk and low risk and analyzed the conversion from MCI to AD using the Cox proportional hazards model. Results APOE ε4 status and age were significantly related to CSF Aβ1-42. CSF t-tau, APOE ε2 status and sex were significant factors. The CSF p-tau181 was associated with age and frequency of diagnosis. Accordingly, we modeled the three CSF biomarkers of AD. In MCI without APOE ε4, our models were better predictors of conversion. Conclusions We can interpret CSF biomarkers based on the models derived from the data obtained from patients with AD.

Independent and Interactive Influences of the APOE Genotype and Beta-Amyloid Burden on Cognitive Function in Mild Cognitive Impairment

서은현,김상훈,박상학,강성호,추일한,Alzheimer’s Disease Neuroimaging Initiative 대한의학회 2016 Journal of Korean medical science Vol.31 No.2

This study aimed to investigate the independent and interactive influences of apolipoprotein E (APOE) ε4 and beta-amyloid (Aβ) on multiple cognitive domains in a large group of cognitively normal (CN) individuals and patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Participants were included if clinical and cognitive assessments, amyloid imaging, and APOE genotype were all available from the Alzheimer’s Disease Neuroimaging Initiative database (CN = 324, MCI = 502, AD = 182). Individuals with one or two copies of ε4 were designated as APOE ε4 carriers (ε4+); individuals with no ε4 were designated as APOE ε4 non-carriers (ε4−). Based on mean florbetapir standard uptake value ratios, participants were classified as Aβ burden-positive (Aβ+) or Aβ burdennegative (Aβ−). In MCI, APOE ε4 effects were predominantly observed on frontal executive function, with ε4+ participants exhibiting poorer performances; Aβ positivity had no influence on this effect. Aβ effects were observed on global cognition, memory, and visuospatial ability, with Aβ+ participants exhibiting poorer performances. Measures of frontal executive function were not influenced by Aβ. Interactive effects of APOE ε4+ and Aβ were observed on global cognition and verbal recognition memory. Aβ, not APOE ε4+, influenced clinical severity and functional status. The influences of APOE ε4+ and Aβ on cognitive function were minimal in CN and AD. In conclusion, we provide further evidence of both independent and interactive influences of APOE ε4+ and Aβ on cognitive function in MCI, with APOE ε4+ and Aβ showing dissociable effects on executive and non-executive functions, respectively.

Comprehensive Visual Rating Scale on Magnetic Resonance Imaging: Application to Prodromal Alzheimer Disease

Jae Kyung Chung,장재원,the Alzheimer’s Disease Neuroimaging Initiative 대한노인병학회 2021 Annals of geriatric medicine and research Vol.25 No.1

Background: A comprehensive visual rating scale (CVRS) using brain magnetic resonance imaging (MRI) was previously developed to evaluate structural changes in the brains of older patients. This study investigated the usefulness of the CVRS in predicting dementia with Alzheimer disease (AD) in patients with prodromal AD. Methods: We included 189 patients with prodromal AD with available data from the Alzheimer’s Disease Neuroimaging Initiative study. We evaluated all patients using CVRS and assessed their progression to AD dementia over 3 years of longitudinal follow-up. Survival analysis was performed using the Cox proportional hazards model to analyze the hazard ratios of the CVRS for progression to AD dementia. Results: Among 189 patients with prodromal AD, 61 (32.3%) progressed to dementia. The mean baseline CVRS scores differed significantly between the stable and progressive groups (9.9±5.1 vs. 12.4±4.9; p=0.002). An initial high CVRS score was an independent risk factor for the progression to AD dementia (hazard ratio=1.110; 95% confidence interval, 1.043–1.182). Conclusion: The baseline CVRS score predicted the progression to dementia in patients with prodromal AD, indicating its independent association with longitudinal cognitive decline.

Decreased Basal Ganglia Volume in Cerebral Amyloid Angiopathy

Panagiotis Fotiadis,Marco Pasi,Andreas Charidimou,Andrew D. Warren,Kristin M. Schwab,Alzheimer’s Disease Neuroimaging Initiative,Jonathan Rosand,Jeroen van der Grond,Mark A. van Buchem,Anand Viswanath 대한뇌졸중학회 2021 Journal of stroke Vol.23 No.2

Background and Purpose Cerebral amyloid angiopathy (CAA) is a common pathology of the leptomeningeal and cortical small vessels associated with hemorrhagic and non-hemorrhagic brain injury. Given previous evidence for CAA-related loss of cortical thickness and white matter volume, we hypothesized that CAA might also cause tissue loss in the basal ganglia. Methods We compared basal ganglia volumes expressed as a percentage of total intracranial volume (pBGV) of non-demented patients with sporadic and hereditary CAA to age-matched healthy control (HC) and Alzheimer’s disease (AD) cohorts. Results Patients with sporadic CAA had lower pBGV (n=80, 1.16%±0.14%) compared to HC (n=80, 1.30%±0.13%, P<0.0001) and AD patients (n=80, 1.23%±0.11%, P=0.001). Similarly, patients with hereditary CAA demonstrated lower pBGV (n=25, 1.26%±0.17%) compared to their matched HC (n=25, 1.36%±0.15%, P=0.036). Using a measurement of normalized basal ganglia width developed for analysis of clinical-grade magnetic resonance images, we found smaller basal ganglia width in patients with CAA-related lobar intracerebral hemorrhage (ICH; n=93, 12.35±1.47) compared to age-matched patients with hypertension-related deep ICH (n=93, 13.46±1.51, P<0.0001) or HC (n=93, 15.45±1.22, P<0.0001). Within the sporadic CAA research cohort, decreased basal ganglia volume was independently correlated with greater cortical gray matter atrophy (r=0.45, P<0.0001), increased basal ganglia fractional anisotropy (r=–0.36, P=0.001), and worse performance on language processing (r=0.35, P=0.003), but not with cognitive tests of executive function or processing speed. Conclusions These findings suggest an independent effect of CAA on basal ganglia tissue loss, indicating a novel mechanism for CAA-related brain injury and neurologic dysfunction