Regional covariance of white matter hyperintensity volume patterns associated with hippocampal volume in healthy aging.

Hippocampal volume is particularly sensitive to the accumulation of total brain white matter hyperintensity volume (WMH) in aging, but how the regional distribution of WMH volume differentially impacts the hippocampus has been less studied. In a cohort of 194 healthy older adults ages 50-89, we used a multivariate statistical method, the Scaled Subprofile Model (SSM), to (1) identify patterns of regional WMH differences related to left and right hippocampal volumes, (2) examine associations between the multimodal neuroimaging covariance patterns and demographic characteristics, and (3) investigate the relation of the patterns to subjective and objective memory in healthy aging. We established network covariance patterns of regional WMH volume differences associated with greater left and right hippocampal volumes, which were characterized by reductions in left temporal and right parietal WMH volumes and relative increases in bilateral occipital WMH volumes. Additionally, we observed lower expression of these hippocampal-related regional WMH patterns were significantly associated with increasing age and greater subjective memory complaints, but not objective memory performance in this healthy older adult cohort. Our findings indicate that, in cognitively healthy older adults, left and right hippocampal volume reductions were associated with differences in the regional distribution of WMH volumes, which were exacerbated by advancing age and related to greater subjective memory complaints. Multivariate network analyses, like SSM, may help elucidate important early effects of regional WMH volume on brain and cognitive aging in healthy older adults.

The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease.

BACKGROUND: Vascular dysfunction, including cerebral hypoperfusion, plays an important role in the pathogenesis and progression of Alzheimer's disease (AD), independent of amyloid and tau pathology. We established an AD-related perfusion pattern (ADRP) measured with arterial spin labeling (ASL) MRI using multivariate spatial covariance analysis. METHODS: We obtained multimodal MRI including pseudo-continuous ASL and neurocognitive testing in a total of 55 patients with a diagnosis of mild to moderate AD supported by amyloid PET and 46 normal controls (NCs). An ADRP was established from an identification cohort of 32 patients with AD and 32 NCs using a multivariate analysis method based on scaled subprofile model/principal component analysis, and pattern expression in individual subjects was quantified for both the identification cohort and a validation cohort (23 patients with AD and 14 NCs). Subject expression score of the ADRP was then used to assess diagnostic accuracy and cognitive correlations in AD patients and compared with global and regional cerebral blood flow (CBF) in specific areas identified from voxel-based univariate analysis. RESULTS: The ADRP featured negative loading in the bilateral middle and posterior cingulate and precuneus, inferior parietal lobule, and frontal areas, and positive loading in the right cerebellum and bilateral basal areas. Subject expression score of the ADRP was significantly elevated in AD patients compared with NCs (P < 0.001) and showed good diagnostic accuracy for AD with area under receiver-operator curve of 0.87 [95% CI (0.78-0.96)] in the identification cohort and 0.85 in the validation cohort. Moreover, there were negative correlations between subject expression score and global cognitive function and performance in various cognitive domains in patients with AD. The characteristics of the ADRP topography and subject expression scores were supported by analogous findings obtained with regional CBF. CONCLUSIONS: We have reported a characteristic perfusion pattern associated with AD using ASL MRI. Subject expression score of this spatial covariance pattern is a promising MRI biomarker for the identification and monitoring of AD.

Association of homocysteine-related subcortical brain atrophy with white matter lesion volume and cognition in healthy aging.

Homocysteine (Hcy) is a vascular risk factor associated with cognitive impairment and cerebrovascular disease but has also been implicated in Alzheimer's disease (AD). Using multivariate Scaled Subprofile Model (SSM) analysis, we sought to identify a network pattern in structural neuroimaging reflecting the regionally distributed association of plasma Hcy with subcortical gray matter (SGM) volumes and its relation to other health risk factors and cognition in 160 healthy older adults, ages 50-89. We identified an SSM Hcy-SGM pattern that was characterized by bilateral hippocampal and nucleus accumbens volume reductions with relative volume increases in bilateral caudate, pallidum, and putamen. Greater Hcy-SGM pattern expression was associated with greater white matter hyperintensity (WMH) volume, older age, and male sex, but not with other vascular and AD-related risk factors. Mediation analyses revealed that age predicted WMH volume, which predicted Hcy-SGM pattern expression, which, in turn, predicted cognitive processing speed performance. These findings suggest that the multivariate SSM Hcy-SGM pattern may be indicative of cognitive aging, reflecting a potential link between vascular health and cognitive dysfunction in healthy older adults.

Identification and validation of a gray matter volume network in Alzheimer's disease.

OBJECTIVE: This study aims to identify and validate a gray matter volume network in patients with Alzheimer's disease (AD). METHODS: To identify a disease-related network, a principal component analysis-based algorithm, Scaled Subprofile Model, was applied to gray matter volume data derived from structural T1-weighted magnetic resonance imaging of the training sample that consisted of nine patients with AD (women, four; dementia, seven; mild cognitive impairment, two; age, 66.7 ± 8.8 [mean ± SD] years) with positive 18F-flutemetamol amyloid positron emission tomography and eight age-matched healthy controls obtained on-site. The network expression scores were calculated by topographic profile rating in the validation sample obtained via the Open Access Series of Imaging Studies and comprised 12 patients with AD dementia (women, four; age, 70.0 ± 3.7 years) and 12 age-matched healthy controls. RESULTS: A significant network from the training sample, for which subject expression differed between the groups (permutation test, P = 0.006; sensitivity and specificity, 100%; area under the curve, 1), was identified. This network was represented by the principal components 1, 2, and 3 and showed a relative decrease in the inferior parietal lobule including angular gyrus, inferior temporal gyrus, premotor cortex, amygdala, hippocampus, and precuneus. It significantly differed between the groups with a sensitivity, specificity, and area under the curve of 83%, 91%, and 0.85, respectively, in the validation sample (P = 0.003). CONCLUSIONS: An AD-related gray matter volume network that captured relevant regions was identified in amyloid positron emission tomography-positive patients and validated in an independent sample.

Gray Matter Network Associated With Attention in Children With Attention Deficit Hyperactivity Disorder.

Background: Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood-onset neurodevelopmental disorders; however, the underlying neural mechanisms for the inattention symptom remain elusive for children with ADHD. At present, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fails to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. The scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to the univariate analysis method. This study aims to identify the GM network associated with attention in children with ADHD by applying SSM-PCA to the sMRI. Methods: The sMRI of 209 children with ADHD and 209 typically developing controls (TDCs) aged 7-14 years from the ADHD-200 dataset was used for anatomical computation, and the GM volume in each brain region was acquired. Then, SSM-PCA was applied to the GM volumes of all the subjects to capture the GM network of children with ADHD (i.e., ADHD-related pattern). The relationship between the expression of ADHD-related pattern and inattention symptom was further investigated. Finally, the influence of sample size on the analysis of this study was explored. Results: The ADHD-related pattern mainly included putamen, pallium, caudate, thalamus, right accumbens, superior/middle/inferior frontal cortex, superior occipital cortex, superior parietal cortex, and left middle occipital cortex. In addition, the expression of the ADHD-related pattern was related to inattention scores measured by the Conners' Parent Rating Scale long version (CPRS-LV; r = 0.25, p = 0.0004) and the DuPaul ADHD Rating Scale IV (ADHD-RS; r = 0.18, p = 0.03). Finally, we found that when the sample size was 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when exploring the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV. Conclusion: We captured a GM network associated with attention in children with ADHD, which is different from that in adolescents and adults with ADHD. Our findings may shed light on the diverse neural mechanisms of inattention and provide treatment targets for children with ADHD.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography.

PURPOSE: Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS: A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS: The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION: DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Age-Related Regional Network Covariance of Magnetic Resonance Imaging Gray Matter in the Rat.

Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.

The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects.

Aim: To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA). Methods: 50 healthy subjects (mean age 45.8 ± 18.5 years, range 21-85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample t-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA. Results: Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (R2 = 0.588; P < 0.001) and discriminated between the older and younger subgroups (P < 0.001). Conclusion: A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI.

Intra- and Inter-scanner Reliability of Scaled Subprofile Model of Principal Component Analysis on ALFF in Resting-State fMRI Under Eyes Open and Closed Conditions.

Scaled Subprofile Model of Principal Component Analysis (SSM-PCA) is a multivariate statistical method and has been widely used in Positron Emission Tomography (PET). Recently, SSM-PCA has been applied to discriminate patients with Parkinson's disease and healthy controls with Amplitude of Low Frequency Fluctuation (ALFF) from Resting-State Functional Magnetic Resonance Imaging (RS-fMRI). As RS-fMRI scans are more readily available than PET scans, it is important to investigate the intra- and inter-scanner reliability of SSM-PCA in RS-fMRI. A RS-fMRI dataset with Eyes Open (EO) and Eyes Closed (EC) conditions was obtained in 21 healthy subjects (21.8 ± 1.8 years old, 11 females) on 3 visits (V1, V2, and V3), with V1 and V2 (mean interval of 14 days apart) on one scanner and V3 (about 8 months from V2) on a different scanner. To simulate between-group analysis in conventional SSM-PCA studies, 21 subjects were randomly divided into two groups, i.e., EC-EO group (EC ALFF map minus EO ALFF map, n = 11) and EO-EC group (n = 10). A series of covariance patterns and their expressions were derived for each visit. Only the expression of the first pattern showed significant differences between the two groups for all the visits (p = 0.012, 0.0044, and 0.00062 for V1, V2, and V3, respectively). This pattern, referred to as EOEC-pattern, mainly involved the sensorimotor cortex, superior temporal gyrus, frontal pole, and visual cortex. EOEC-pattern's expression showed fair intra-scanner reliability (ICC = 0.49) and good inter-scanner reliability (ICC = 0.65 for V1 vs. V2 and ICC = 0.66 for V2 vs. V3). While the EOEC-pattern was similar with the pattern of conventional unpaired T-test map, the two patterns also showed method-specific regions, indicating that SSM-PCA and conventional T-test are complementary for neuroimaging studies.

Blood Pressure Control in Aging Predicts Cerebral Atrophy Related to Small-Vessel White Matter Lesions.

Cerebral small-vessel damage manifests as white matter hyperintensities and cerebral atrophy on brain MRI and is associated with aging, cognitive decline and dementia. We sought to examine the interrelationship of these imaging biomarkers and the influence of hypertension in older individuals. We used a multivariate spatial covariance neuroimaging technique to localize the effects of white matter lesion load on regional gray matter volume and assessed the role of blood pressure control, age and education on this relationship. Using a case-control design matching for age, gender, and educational attainment we selected 64 participants with normal blood pressure, controlled hypertension or uncontrolled hypertension from the Northern Manhattan Study cohort. We applied gray matter voxel-based morphometry with the scaled subprofile model to (1) identify regional covariance patterns of gray matter volume differences associated with white matter lesion load, (2) compare this relationship across blood pressure groups, and (3) relate it to cognitive performance. In this group of participants aged 60-86 years, we identified a pattern of reduced gray matter volume associated with white matter lesion load in bilateral temporal-parietal regions with relative preservation of volume in the basal forebrain, thalami and cingulate cortex. This pattern was expressed most in the uncontrolled hypertension group and least in the normotensives, but was also more evident in older and more educated individuals. Expression of this pattern was associated with worse performance in executive function and memory. In summary, white matter lesions from small-vessel disease are associated with a regional pattern of gray matter atrophy that is mitigated by blood pressure control, exacerbated by aging, and associated with cognitive performance.

Quantitative study on effect of enriched environmental stimulation on mouse brain cognitive reserve / 医疗卫生装备

Objective To explore the effect of enriched environmental stimulation on mouse brain cognitive reserve to enhance the sensitivity of brain age gap estimation (BrainAGE).Methods Twenty-one healthy adult C57BL / 6J male mice,15 months old,were divided into a group with a standard environment and two groups with enriched environments.All the groups underwent magnetic resonance microcopy.Scaled subprofile model was used to analyze the features reflecting the changes of brain cognitive reserve.Results There were significant differences between the mean BrainAGE of the two groups with enriched environments and that of the remained standard environment group,then it's proved that some assumption might be reasonable that brain cognitive reserve could be estimated based on BrainAGE.Optim ized BrainAGE model made explanations for 58.9％ differences during stimulus phase in enriched environment.Conclusion Improved BrainAGE model gains high sensitivity when used to measure the redundancy of brain cognitive reserve.

Quantitative study on effect of enriched environmental stimulation on mouse brain cognitive reserve / 医疗卫生装备

Objective To explore the effect of enriched environmental stimulation on mouse brain cognitive reserve to enhance the sensitivity of brain age gap estimation (BrainAGE).Methods Twenty-one healthy adult C57BL / 6J male mice,15 months old,were divided into a group with a standard environment and two groups with enriched environments.All the groups underwent magnetic resonance microcopy.Scaled subprofile model was used to analyze the features reflecting the changes of brain cognitive reserve.Results There were significant differences between the mean BrainAGE of the two groups with enriched environments and that of the remained standard environment group,then it's proved that some assumption might be reasonable that brain cognitive reserve could be estimated based on BrainAGE.Optim ized BrainAGE model made explanations for 58.9％ differences during stimulus phase in enriched environment.Conclusion Improved BrainAGE model gains high sensitivity when used to measure the redundancy of brain cognitive reserve.

Regional covariance patterns of gray matter alterations in Alzheimer's disease and its replicability evaluation.

PURPOSE: To identify regional network covariance patterns of gray matter associated with Alzheimer's disease (AD) and to further evaluate its replicability and stability. MATERIALS AND METHODS: This study applied a multivariate analytic approach based on scaled subprofile modeling (SSM) to structural magnetic resonance imaging (MRI) data from 19 patients with AD and 19 healthy controls (HC). We further applied the derived covariance patterns to examine the replicability and stability of AD-associated covariance patterns in an independent dataset (13 AD and 14 HC) acquired with a different scanner. RESULTS: The AD-associated covariance patterns identified from SSM combined principal components mainly involved the temporal lobe and parietal lobe. The expression of covariance patterns was significantly higher in AD patients than HC (t(36) = 5.84, P = 5.75E-7) and predicted the AD/HC group membership (84% sensitivity and 90% specificity). In replicability evaluation, the expression of the forward applied covariance patterns was still statistically significant and had acceptable discriminability (69% sensitivity and 71% specificity). CONCLUSION: AD patients showed regional gray matter alterations in a reliable covariance manner. The results suggest that SSM has utility for characterizing covariant features, and therefore can assist with further understanding covariance patterns of gray matter in AD based on the view of the network.