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.

Transfer learning for cognitive reserve quantification.

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

Distinct cortical thickness patterns link disparate cerebral cortex regions to select mobility domains.

The cortical control of gait and mobility involves multiple brain regions. Therefore, one could speculate that the association between specific spatial patterns of cortical thickness may be differentially associated with different mobility domains. To test this possibility, 115 healthy participants aged 27-82 (mean 60.5 ± 13.8) underwent a mobility assessment (usual-walk, dual-task walk, Timed Up and Go) and MRI scan. Ten mobility domains of relatively simple (e.g., usual-walking) and complex tasks (i.e., dual task walking, turns, transitions) and cortical thickness of 68 ROIs were extracted. All associations between mobility and cortical thickness were controlled for age and gender. Scaled Subprofile Modelling (SSM), a PCA-regression, identified thickness patterns that were correlated with the individual mobility domains, controlling for multiple comparisons. We found that lower mean global cortical thickness was correlated with worse general mobility (r = - 0.296, p = 0.003), as measured by the time to complete the Timed Up and Go test. Three distinct patterns of cortical thickness were associated with three different gait domains during simple, usual-walking: pace, rhythm, and symmetry. In contrast, cortical thickness patterns were not related to the more complex mobility domains. These findings demonstrate that robust and topographically distinct cortical thickness patterns are linked to select mobility domains during relatively simple walking, but not to more complex aspects of mobility. Functional connectivity may play a larger role in the more complex aspects of mobility.

Task-based functional connectivity in aging: How task and connectivity methodology affect discovery of age effects.

INTRODUCTION: Past studies have found that healthy aging has a significant effect on the organization and function of networks in the human brain. Many of these studies have examined how functional connectivity during one task or at rest is affected by aging; however, few studies have systematically examined how the effect of age on functional connectivity may vary as a function of choice of in-scanner task. METHODS: The present study included healthy adults between the ages of 20 and 80 and examined a variety of metrics of functional connectivity during performance of 11 in-scanner tasks, falling into 4 cognitive domains: vocabulary, processing speed, fluid reasoning, and episodic memory. Functional connectivity was assessed at three levels: average correlations within and between 10 networks, system segregation (sensorimotor vs. association networks), and whole-brain graph theory metrics (global efficiency and modularity). RESULTS: Results showed that the effect of age on these metrics differed as a function of task-for example, age had a more consistent effect on functional connectivity metrics computed during fluid reasoning tasks; however, there was less of an effect of age on functional connectivity metrics computed during tasks of episodic memory. Further, some of these measures showed relationships with behavioral performance on the in-scanner task, with different networks playing a role in the different cognitive domains. CONCLUSION: These findings suggest that while aging may be generally associated with reductions in within- and between-network connectivity, system segregation, global efficiency, and modularity, the magnitude and presence of these effects varies by in-scanner task.

The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain.

Recent studies have found a deleterious effect of age on a wide variety of measures of functional connectivity, and some hints at a relationship between connectivity at rest and cognitive functioning. However, few studies have combined multiple functional connectivity methods, or examined them over a wide range of adult ages, to try to uncover which metrics and networks seem to be particularly sensitive to age-related decline across the adult lifespan. The present study utilized multiple resting state functional connectivity methods in a sample of adults from 20-80 years old to gain a more complete understanding of the effect of aging on network function and integrity. Whole-brain results showed that aging results in weakening average within-network connectivity, lower system segregation and local efficiency, and higher participation coefficient. Network-level results suggested that nearly every primary sensory and cognitive network faces some degree of age-related decline, including reduced within-network connectivity, higher network-based participation coefficient, and reduced network-level local efficiency. Further, some of these connectivity metrics showed relationships with cognitive performance. Thus, these results suggest that a multi-method analysis of functional connectivity data may be critical to capture the full effect of aging on the health of brain networks.

Between-network Functional Connectivity Is Modified by Age and Cognitive Task Domain.

Research on the cognitive neuroscience of aging has identified myriad neurocognitive processes that are affected by the aging process, with a focus on identifying neural correlates of cognitive function in aging. This study aimed to test whether internetwork connectivity among six cognitive networks is sensitive to age-related changes in neural efficiency and cognitive functioning. A factor analytic connectivity approach was used to model network interactions during 11 cognitive tasks grouped into four primary cognitive domains: vocabulary, perceptual speed, fluid reasoning, and episodic memory. Results showed that both age and task domain were related to internetwork connectivity and that some of the connections among the networks were associated with performance on the in-scanner tasks. These findings demonstrate that internetwork connectivity among several cognitive networks is not only affected by aging and task demands but also shows a relationship with task performance. As such, future studies examining internetwork connectivity in aging should consider multiple networks and multiple task conditions to better measure dynamic patterns of network flexibility over the course of cognitive aging.

Brain reserve against physical disability progression over 5 years in multiple sclerosis.

OBJECTIVE: The brain reserve hypothesis links larger maximal lifetime brain growth (MLBG, estimated with intracranial volume [ICV]) with lower risk for cognitive decline/dementia. We examined whether larger MLBG is also linked to less physical disability progression over 5 years in a prospective sample of treatment-naive patients with multiple sclerosis (MS). METHODS: Physical disability was measured with the Expanded Disability Status Scale (EDSS) at baseline and 5-year follow-up in 52 treatment-naive Serbian patients with MS. MRI measured disease burden (cerebral atrophy, T2 lesion volume) and MLBG: a genetically determined, premorbid (established during adolescence, stable thereafter) patient characteristic estimated with ICV (adjusted for sex). Logistic regression tested whether MLBG (smaller vs larger) predicts disability progression (stable vs worsened) independently of disease burden. RESULTS: Disability progression was observed in 29 (55.8%) patients. Larger MLBG predicted lower risk for progression (odds ratio 0.13, 95% confidence interval 0.02-0.78), independently of disease burden. We also calculated absolute change in EDSS scores, and observed that patients with smaller MLBG showed worse EDSS change (0.91 ± 0.71) than patients with larger MLBG (0.42 ± 0.87). CONCLUSIONS: Larger MLBG was linked to lower risk for disability progression in patients with MS over 5 years, which is the first extension of the brain reserve hypothesis to physical disability. MLBG (ICV) represents a clinically available metric that may help gauge risk for future disability in patients with MS, which may advance the science and practice of early intervention. Potential avenues for future research are discussed.

White matter integrity as a mediator in the relationship between dietary nutrients and cognition in the elderly.

OBJECTIVE: We examined the association of nutrient intake with microstructural white matter integrity, and the role of white matter integrity in the association between nutrient consumption and cognition. METHODS: This cross-sectional analysis included 239 elderly (age ≥ 65 years) participants of a multiethnic cohort. White matter integrity was measured with fractional anisotropy (FA) from diffusion tensor magnetic resonance imaging. Nutrient patterns were derived from principal component analysis based on energy-adjusted intake of 24 selected nutrients. Generalized linear models were used to assess the association between nutrient patterns and mean FA of 26 white matter tracts. Mediation analysis was used to determine whether FA mediates the nutrient-cognition relationship. All models were adjusted for age at time of scan, gender, ethnicity, education, caloric intake, and apolipoprotein genotype. RESULTS: Among the identified 6 nutrient patterns, 1 (nutrient pattern 6, characterized by high intakes of Ω-3 and Ω-6 polyunsaturated fatty acids and vitamin E) was positively associated with FA. Those with the highest tertile of nutrient pattern 6 score had a mean of 0.01 (p = 0.01) higher FA value than those with the lowest tertile, similar to the effect of a 10-year decrease in age (b for age = -0.001, p = 0.01). FA mediated the relationship between nutrient pattern 6 and memory, language, visuospatial and speed/executive function, and mean cognitive scores. INTERPRETATION: Our study suggests that older adults consuming more polyunsaturated fatty acids and vitamin E rich foods had better white matter integrity, and that maintaining white matter microstructural integrity might be a mechanism for the beneficial role of diet on cognition. Ann Neurol 2016;79:1014-1025.

Insight from uncertainty: bootstrap-derived diffusion metrics differentially predict memory function among older adults.

Diffusion tensor imaging suffers from an intrinsic low signal-to-noise ratio. Bootstrap algorithms have been introduced to provide a non-parametric method to estimate the uncertainty of the measured diffusion parameters. To quantify the variability of the principal diffusion direction, bootstrap-derived metrics such as the cone of uncertainty have been proposed. However, bootstrap-derived metrics are not independent of the underlying diffusion profile. A higher mean diffusivity causes a smaller signal-to-noise ratio and, thus, increases the measurement uncertainty. Moreover, the goodness of the tensor model, which relies strongly on the complexity of the underlying diffusion profile, influences bootstrap-derived metrics as well. The presented simulations clearly depict the cone of uncertainty as a function of the underlying diffusion profile. Since the relationship of the cone of uncertainty and common diffusion parameters, such as the mean diffusivity and the fractional anisotropy, is not linear, the cone of uncertainty has a different sensitivity. In vivo analysis of the fornix reveals the cone of uncertainty to be a predictor of memory function among older adults. No significant correlation occurs with the common diffusion parameters. The present work not only demonstrates the cone of uncertainty as a function of the actual diffusion profile, but also discloses the cone of uncertainty as a sensitive predictor of memory function. Future studies should incorporate bootstrap-derived metrics to provide more comprehensive analysis.

Mediterranean diet and brain structure in a multiethnic elderly cohort.

OBJECTIVE: To determine whether higher adherence to a Mediterranean-type diet (MeDi) is related with larger MRI-measured brain volume or cortical thickness. METHODS: In this cross-sectional study, high-resolution structural MRI was collected on 674 elderly (mean age 80.1 years) adults without dementia who participated in a community-based, multiethnic cohort. Dietary information was collected via a food frequency questionnaire. Total brain volume (TBV), total gray matter volume (TGMV), total white matter volume (TWMV), mean cortical thickness (mCT), and regional volume or CT were derived from MRI scans using FreeSurfer program. We examined the association of MeDi (scored as 0-9) and individual food groups with brain volume and thickness using regression models adjusted for age, sex, ethnicity, education, body mass index, diabetes, and cognition. RESULTS: Compared to lower MeDi adherence (0-4), higher adherence (5-9) was associated with 13.11 (p = 0.007), 5.00 (p = 0.05), and 6.41 (p = 0.05) milliliter larger TBV, TGMV, and TWMV, respectively. Higher fish (b = 7.06, p = 0.006) and lower meat (b = 8.42, p = 0.002) intakes were associated with larger TGMV. Lower meat intake was also associated with larger TBV (b = 12.20, p = 0.02). Higher fish intake was associated with 0.019 mm (p = 0.03) larger mCT. Volumes of cingulate cortex, parietal lobe, temporal lobe, and hippocampus and CT of the superior-frontal region were associated with the dietary factors. CONCLUSIONS: Among older adults, MeDi adherence was associated with less brain atrophy, with an effect similar to 5 years of aging. Higher fish and lower meat intake might be the 2 key food elements that contribute to the benefits of MeDi on brain structure.

Shared space, separate processes: Neural activation patterns for auditory description and visual object naming in healthy adults.

Historically, both clinicians and cognitive scientists have used visual object naming measures to study naming, and lesion-type studies have implicated the left posterior, temporo-parietal region as a critical component of naming circuitry. However, recent results from behavioral and cortical stimulation studies using auditory description naming as well as visual object naming in left temporal lobe epilepsy patients suggest that discrete sites in anterior temporal cortex are critical for description naming, whereas posterior temporal regions mediate both visual object naming and description naming. To determine whether this task specificity reflects normal cerebral organization and processing, 13 healthy adults performed description naming and visual naming during functional neuroimaging. In addition to standard univariate analysis, multivariate, ordinal trend analysis examined the network character of the regions involved in task-specific naming. Univariate analysis indicated posterior temporal activation for both visual naming and description naming, whereas multivariate analysis revealed broader networks for both tasks, with both overlapping and task-specific regions, as well as task-related differences in the way the tasks utilized common regions. Additionally, multivariate analysis revealed unique, task-specific, regionally covarying activation patterns that were strikingly consistent in all 13 subjects for visual naming and 12/13 subjects for description naming. Results suggest a common neural substrate, yet differentiable neural processes underlying visual naming and description naming in neurologically intact individuals. These findings support the use of both types of tasks for clinical assessment and may have application in the treatment of neurologically based naming deficits. Inc.

Cerebral blood flow and gray matter volume covariance patterns of cognition in aging.

Advancing age results in altered cognitive and neuroimaging-derived markers of neural integrity. Whether cognitive changes are the result of variations in brain measures remains unclear and relating the two across the lifespan poses a unique set of problems. It must be determined whether statistical associations between cognitive and brain measures truly exist and are not epiphenomenal due solely to their shared relationships with age. The purpose of this study was to determine whether cerebral blood flow (CBF) and gray matter volume (GMV) measures make unique and better predictions of cognition than age alone. Multivariate analyses identified brain-wide covariance patterns from 35 healthy young and 23 healthy older adults using MRI-derived measures of CBF and GMV related to three cognitive composite scores (i.e., memory, fluid ability, and speed/attention). These brain-cognitive relationships were consistent across the age range, and not the result of epiphenomenal associations with age and each imaging modality provided its own unique information. The CBF and GMV patterns each accounted for unique aspects of cognition and accounted for nearly all the age-related variance in the cognitive composite scores. The findings suggest that measures derived from multiple imaging modalities explain larger amounts of variance in cognition providing a more complete understanding of the aging brain.

Age-related changes in task related functional network connectivity.

Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.

Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data.

Research has indicated that there may be age-related and Alzheimer's disease (AD) -related reductions in regional cerebral blood flow (rCBF) in the brain. This study explored differences in age- and AD-related rCBF patterns in the context of cognitive aging using a multivariate approach to the analysis of H215O PET data. First, an rCBF covariance pattern that distinguishes between a group of younger and older adults was identified. Individual subject's expression of the identified age-related pattern was significantly correlated with their performance on tests of memory, even after controlling for the effect of age. This finding suggests that subject expression of the covariance pattern explained additional variation in performance on the memory tasks. The age-related covariance pattern was then compared to an AD-related covariance pattern. There was little evidence that the two covariance patterns were similar, and the age-related pattern did a poor job of differentiating between cognitively-healthy older adults and those with probable AD. The findings from this study are consistent with the multifactorial nature of cognitive aging.

PET network abnormalities and cognitive decline in patients with mild cognitive impairment.

Temporoparietal and posterior cingulate metabolism deficits characterize patients with Alzheimer's disease (AD). A H(2)(15)O resting PET scan covariance pattern, derived by using multivariate techniques, was previously shown to discriminate 17 mild AD patients from 16 healthy controls. This AD covariance pattern revealed hypoperfusion in bilateral inferior parietal lobule and cingulate; and left middle frontal, inferior frontal, precentral, and supramarginal gyri. The AD pattern also revealed hyperperfusion in bilateral insula, lingual gyri, and cuneus; left fusiform and superior occipital gyri; and right parahippocampal gyrus and pulvinar. In an independent sample of 23 outpatients with mild cognitive impairment (MCI) followed at 6-month intervals, the AD pattern score was evaluated as a predictor of cognitive decline. In this MCI sample, an H2(15)O resting PET scan was carried out at baseline. Mean duration of follow-up was 48.8 (SD 15.5) months, during which time six of 23 MCI patients converted to AD. In generalized estimating equations (GEE) analyses, controlling for age, sex, education, and baseline neuropsychological scores, increased AD pattern score was associated with greater decline in each neuropsychological test score over time (Mini Mental State Exam, Selective Reminding Test delayed recall, Animal Naming, WAIS-R digit symbol; Ps<0.01-0.001). In summary, a resting PET covariance pattern previously reported to discriminate AD patients from control subjects was applied prospectively to an independent sample of MCI patients and found to predict cognitive decline. Independent replication in larger samples is needed before clinical application can be considered.

Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H(2) (15)O-, and FDG-PET.

In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.

Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance.

Although multivariate analytic techniques might identify diagnostic patterns that are not captured by univariate methods, they have rarely been used to study the neural correlates of Alzheimer's disease (AD) or cognitive impairment. Nonquantitative H2(15)O PET scans were acquired during rest in 17 probable AD subjects selected for mild severity [mean-modified Mini Mental Status Examination (mMMS) 46/57; SD 5.1], 16 control subjects (mMMS 54; SD 2.5) and 23 subjects with minimal to mild cognitive impairment but no dementia (mMMS 53; SD 2.8). Expert clinical reading had low success in discriminating AD and controls. There were no significant mean flow differences among groups in traditional univariate SPM Noxel-wise analyses or region of interest (ROI) analyses. A covariance pattern was identified whose mean expression was significantly higher in the AD as compared to controls (P = 0.03; sensitivity 76-94%; specificity 63-81%). Sites of increased concomitant flow included insula, cuneus, pulvinar, lingual, fusiform, superior occipital and parahippocampal gyri, whereas decreased concomitant flow was found in cingulate, inferior parietal lobule, middle and inferior frontal, supramarginal and precentral gyri. The covariance analysis-derived pattern was then prospectively applied to the cognitively impaired subjects: as compared to subjects with Clinical Dementia Rating (CDR) = 0, subjects with CDR = 0.5 had significantly higher mean covariance pattern expression (P = 0.009). Expression of this pattern correlated inversely with Selective Reminding Test total recall (r = -0.401, P = 0.002), delayed recall (r = -0.351, P = 0.008) and mMMS scores (r = -0.401, P = 0.002) in all three groups combined. We conclude that patients with AD may differentially express resting cerebral blood flow covariance patterns even at very early disease stages. Significant alterations in expression of resting flow covariance patterns occur even for subjects with cognitive impairment. Expression of covariance patterns correlates with cognitive and functional performance measures, holding promise for meaningful associations with underlying biopathological processes.

Association of life activities with cerebral blood flow in Alzheimer disease: implications for the cognitive reserve hypothesis.

BACKGROUND: Regional cerebral blood flow (CBF), a good indirect index of cerebral pathologic changes in Alzheimer disease (AD), is more severely reduced in patients with higher educational attainment and IQ when controlling for clinical severity. This has been interpreted as suggesting that cognitive reserve allows these patients to cope better with the pathologic changes in AD. OBJECTIVE: To evaluate whether premorbid engagement in various activities may also provide cognitive reserve. DESIGN: We evaluated intellectual, social, and physical activities in 9 patients with early AD and 16 healthy elderly controls who underwent brain H(2)(15)O positron emission tomography. In voxelwise multiple regression analyses that controlled for age and clinical severity, we investigated the association between education, estimated premorbid IQ, and activities, and CBF. RESULTS: In accordance with previous findings, we replicated an inverse association between education and CBF and IQ and CBF in patients with AD. In addition, there was a negative correlation between previous reported activity score and CBF in patients with AD. When both education and IQ were added as covariates in the same model, a higher activity score was still associated with more prominent CBF deficits. No significant associations were detected in the controls. CONCLUSIONS: At any given level of clinical disease severity, there is a greater degree of brain pathologic involvement in patients with AD who have more engagement in activities, even when education and IQ are taken into account. This may suggest that interindividual differences in lifestyle may affect cognitive reserve by partially mediating the relationship between brain damage and the clinical manifestation of AD.