Leveraging Neuroimaging Tools to Assess Precision and Accuracy in an Alzheimer's Disease Neuropathologic Sampling Protocol.

INTRODUCTION: The study of Alzheimer's disease investigates topographic patterns of degeneration in the context of connected networks comprised of functionally distinct domains using increasingly sophisticated molecular techniques. Therefore, obtaining high precision and accuracy of neuropathologic tissue sampling will enhance the reliability of molecular studies and contribute to the understanding of Alzheimer's disease pathology. Neuroimaging tools can help assess these aspects of current sampling protocols as well as contribute directly to their improvement. METHODS: Using a virtual sampling method on magnetic resonance images (MRIs) from 35 participants (21 women), we compared the precision and accuracy of traditional neuropathologic vs. neuroimaging-guided sampling. The impact of the resulting differences was assessed by evaluating the functional connectivity pattern of regions selected by each approach. RESULTS: Virtual sampling using the traditional neuropathologic approach had low neuroanatomical precision and accuracy for all cortical regions tested. Neuroimaging-guided strategies narrowed these gaps. Discrepancies in the location of traditional and neuroimaging-guided samples corresponded to differences in fMRI measures of functional connectivity. DISCUSSION: Integrating neuroimaging tools with the neuropathologic assessment will improve neuropathologic-neuroimaging correlations by helping to ensure specific functional domains are accurately sampled for quantitative molecular neuropathologic applications. Our neuroimaging-based simulation of current sampling practices provides a benchmark of precision and accuracy against which to measure improvements when using novel tissue sampling approaches. Our results suggest that relying on gross landmarks alone to select samples at autopsy leads to significant variability, even when sampled by the same neuropathologist. Further, this exercise highlights how sampling precision could be enhanced if neuroimaging were integrated with the standard neuropathologic assessment. More accurate targeting and improved biological homogeneity of sampled brain tissue will facilitate the interpretation of neuropathological analyses in AD and the downstream research applications of brain tissue from biorepositories.

Relationships Between Sensorimotor Inhibition and Mobility in Older Adults With and Without Parkinson's Disease.

BACKGROUND: Reduced cortical sensorimotor inhibition is associated with mobility and cognitive impairments in people with Parkinson's disease (PD) and older adults (OAs). However, there is a lack of clarity regarding the relationships among sensorimotor, cognitive, and mobility impairments. The purpose of this study was to determine how cortical sensorimotor inhibition relates to impairments in mobility and cognition in people with PD and OAs. METHOD: Cortical sensorimotor inhibition was characterized with short-latency afferent inhibition (SAI) in 81 people with PD and 69 OAs. Six inertial sensors recorded single- and dual-task gait and postural sway characteristics during a 2-minute walk and a 1-minute quiet stance. Cognition was assessed across the memory, visuospatial, executive function, attention, and language domains. RESULTS: SAI was significantly impaired in the PD compared to the OA group. The PD group preformed significantly worse across all gait and postural sway tasks. In PD, SAI significantly correlated with single-task foot strike angle and stride length variability, sway area, and jerkiness of sway in the coronal and sagittal planes. In OAs, SAI significantly related to single-task gait speed and stride length, dual-task stride length, and immediate recall (memory domain). No relationship among mobility, cognition, and SAI was observed. CONCLUSIONS: Impaired SAI related to slower gait in OA and to increased gait variability and postural sway in people with PD, all of which have been shown to be related to increased fall risk.

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages.

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second "natural" language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50-72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.

Effect of Dopaminergic Medications on Blood Oxygen Level-Dependent Variability and Functional Connectivity in Parkinson's Disease and Healthy Aging.

Both functional connectivity (FC) and blood oxygen level-dependent (BOLD) signal variability (SDBOLD) are methods that are used for examining the physiological state of the brain. Although they are derived from signal changes and are related, a few studies have explored their relationship. Here, we examined the relationship between SDBOLD and FC within the default mode network (DMN) in healthy aging participants and those with Parkinson's disease (PD) ON and OFF dopaminergic medications. Dopaminergic medications had profound effects on both DMN FC and SDBOLD measured separately in PD. Analyzing DMN FC and SDBOLD in a joint independent component analysis, we identified joint components of DMN FC and SDBOLD that were separately associated with measurements of motor and cognitive impairment in PD and qualitatively similar to those in healthy aging. Dopaminergic medications had a differential effect on these components depending on these measures of disease severity, "normalizing" the relationships. Importantly, we show that dopaminergic medication status matters in imaging PD, and it can affect both connectivity and SDBOLD. Imaging PD ON may lead to inflated estimates of SDBOLD and diminish the ability to measure changes associated with declining motor and cognitive capacities.

Spontaneous Variation in Electrocorticographic Resting-State Connectivity.

Prior studies using functional magnetic resonance imaging, electroencephalography, and magnetoencephalography have observed both structured patterns in resting-state functional connectivity and spontaneous longitudinal variation in connectivity patterns independent of a task. In this first study using electrocorticography (ECoG), we characterized spontaneous, intersession variation in resting-state functional connectivity not linked to a task. We evaluated pairwise connectivity between electrodes using three measures (phase locking value [PLV], amplitude correlation, and coherence) for six canonical frequency bands, capturing different characteristics of time-evolving signals. We grouped electrodes into 10 functional regions and used intraclass correlation (ICC) to estimate pairwise longitudinal stability. We found that stronger PLV (PLV ≥0.4) in theta through gamma bands and strong correlation in all bands (R2's ≥0.6) are linked to substantial stability (ICC ≥0.6), but that stability does not imply strong phase locking or amplitude correlation. There was no notable link between strong coherence and high ICC. All within-region PLVs are markedly stable across frequencies. In addition, we highlight interaction patterns across several regions: parahippocampal/entorhinal cortex is characterized by stable, weak functional connectivity except self-connections. Dorsolateral prefrontal cortex connectivity is weak and unstable, except self-connections. Inferior parietal lobule has little stability despite narrow connectivity bounds. We confirm prior studies linking functional connectivity strength and intersession variability, extending into higher frequencies than other modalities, with greater spatial specificity than scalp electrophysiology. We suggest further studies quantitatively compare ECoG to other modalities and/or use these findings as a baseline to capture functional connectivity and dynamics linked to perturbations with a task or disease state.

Attention Network Test fMRI data for participants with Parkinson's disease and healthy elderly.

Here, we present unprocessed and preprocessed Attention Network Test data from 25 adults with Parkinson's disease and 21 healthy adults, along with the associated defaced structural scans. The preprocessed data has been processed with a provided Analysis of Functional NeuroImages afni_proc.py script and includes structural scans that were skull-stripped before defacing. All acquired demographic and neuropsychological data are included.

Telomere Length and Magnetic Resonance Imaging Findings of Vascular Brain Injury and Central Brain Atrophy: The Strong Heart Study.

Telomeres are repeating regions of DNA that cap chromosomes. They shorten over the mammalian life span, especially in the presence of oxidative stress and inflammation. Telomeres may play a direct role in cell senescence, serving as markers of premature vascular aging. Leukocyte telomere length (LTL) may be associated with premature vascular brain injury and cerebral atrophy. However, reports have been inconsistent, especially among minority populations with a heavy burden of illness related to vascular aging. We examined associations between LTL and magnetic resonance imaging in 363 American Indians aged 64-93 years from the Strong Heart Study (1989-1991) and its ancillary study, Cerebrovascular Disease and Its Consequences in American Indians (2010-2013). Our results showed significant associations of LTL with ventricular enlargement and the presence of white matter hyperintensities. Secondary models indicated that renal function may mediate these associations, although small case numbers limited inference. Hypertension and diabetes showed little evidence of effect modification. Results were most extreme among participants who evinced the largest decline in LTL. Although this study was limited to cross-sectional comparisons, it represents (to our knowledge) the first consideration of associations between telomere length and brain aging in American Indians. Findings suggest a relationship between vascular aging by cell senescence and severity of brain disease.

Running Neuroimaging Applications on Amazon Web Services: How, When, and at What Cost?

The contribution of this paper is to identify and describe current best practices for using Amazon Web Services (AWS) to execute neuroimaging workflows "in the cloud." Neuroimaging offers a vast set of techniques by which to interrogate the structure and function of the living brain. However, many of the scientists for whom neuroimaging is an extremely important tool have limited training in parallel computation. At the same time, the field is experiencing a surge in computational demands, driven by a combination of data-sharing efforts, improvements in scanner technology that allow acquisition of images with higher image resolution, and by the desire to use statistical techniques that stress processing requirements. Most neuroimaging workflows can be executed as independent parallel jobs and are therefore excellent candidates for running on AWS, but the overhead of learning to do so and determining whether it is worth the cost can be prohibitive. In this paper we describe how to identify neuroimaging workloads that are appropriate for running on AWS, how to benchmark execution time, and how to estimate cost of running on AWS. By benchmarking common neuroimaging applications, we show that cloud computing can be a viable alternative to on-premises hardware. We present guidelines that neuroimaging labs can use to provide a cluster-on-demand type of service that should be familiar to users, and scripts to estimate cost and create such a cluster.

Characterizing cross-subject spatial interaction patterns in functional magnetic resonance imaging studies: A two-stage point-process model.

We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.

Total Brain and Hippocampal Volumes and Cognition in Older American Indians: The Strong Heart Study.

BACKGROUND: Estimates of hippocampal volume by magnetic resonance imaging have clinical and cognitive correlations and can assist in early Alzheimer disease diagnosis. However, little is known about the relationship between global or regional brain volumes and cognitive test performance in American Indians. MATERIALS AND METHODS: American Indian participants (N=698; median age, 72 y) recruited for the Cerebrovascular Disease and its Consequences in American Indians study, an ancillary study of the Strong Heart Study cohort, were enrolled. Linear regression models assessed the relationship between magnetic resonance imaging brain volumes (total brain and hippocampi) and cognitive measures of verbal learning and recall, processing speed, verbal fluency, and global cognition. RESULTS: After controlling for demographic and clinical factors, all volumetric measurements were positively associated with processing speed. Total brain volume was also positively associated with verbal learning, but not with verbal recall. Conversely, left hippocampal volume was associated with both verbal learning and recall. The relationship between hippocampal volume and recall performance was more pronounced among those with lower scores on a global cognitive measure. Controlling for APOE ε4 did not substantively affect the associations. CONCLUSIONS: These results support further investigation into the relationship between structural Alzheimer disease biomarkers, cognition, genetics, and vascular risk factors in aging American Indians.

Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults.

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals.

Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI.

Correction of echo planar imaging (EPI)-induced distortions (called "unwarping") improves anatomical fidelity for diffusion magnetic resonance imaging (MRI) and functional imaging investigations. Commonly used unwarping methods require the acquisition of supplementary images during the scanning session. Alternatively, distortions can be corrected by nonlinear registration to a non-EPI acquired structural image. In this study, we compared reliability using two methods of unwarping: (1) nonlinear registration to a structural image using symmetric normalization (SyN) implemented in Advanced Normalization Tools (ANTs); and (2) unwarping using an acquired field map. We performed this comparison in two different test-retest data sets acquired at differing sites (N = 39 and N = 32). In both data sets, nonlinear registration provided higher test-retest reliability of the output fractional anisotropy (FA) maps than field map-based unwarping, even when accounting for the effect of interpolation on the smoothness of the images. In general, field map-based unwarping was preferable if and only if the field maps were acquired optimally.

Findings of Vascular Brain Injury and Structural Loss from Cranial Magnetic Resonance Imaging in Elderly American Indians: The Strong Heart Study.

BACKGROUND: The Cerebrovascular Disease and its Consequences in American Indians study conducted cranial MRI examination of surviving participants of the Strong Heart Study, a longitudinal cohort of elderly American Indians. METHODS: Of the 1,033 recruited participants, some were unable to complete the MRI (n = 22), some scans were unusable due to participant motion or technical errors (n = 13), and one community withdrew consent after data collection (n = 209), leaving 789 interpretable MRI scan images. Six image sequences were obtained in contiguous slices on 1.5T scanners. Neuroradiologists graded white matter hyperintensities (WMH), sulci, and ventricles on a 0- to 9-point scale, and recorded the presence of infarcts and hemorrhages. Intracranial, brain, hippocampal, and WMH volumes were estimated by automated image processing. RESULTS: The median scores for graded measures were 2 (WMH) and 3 (sulci, ventricles). About one-third of participants had lacunar (20%) or other infarcts (13%); few had hemorrhages (5.7%). Findings of cortical atrophy were also prevalent. Statistical analyses indicated significant associations between older age and findings of vascular injury and atrophy; male gender was associated with findings of cortical atrophy. CONCLUSIONS: Vascular brain injury is the likely explanation in this elderly American Indian population for brain infarcts, hemorrhages, WMH grade, and WMH volume. Although vascular brain injury may play a role in other findings, independent degenerative other disease processes may underlie abnormal sulcal widening, ventricular enlargement, hippocampal volume, and total brain volume. Further examination of risk factors and outcomes with these findings may expand the understanding of neurological conditions in this understudied population.

Executive attention networks show altered relationship with default mode network in PD.

Attention dysfunction is a common but often undiagnosed cognitive impairment in Parkinson's disease that significantly reduces quality of life. We sought to increase understanding of the mechanisms underlying attention dysfunction using functional neuroimaging. Functional MRI was acquired at two repeated sessions in the resting state and during the Attention Network Test, for 25 non-demented subjects with Parkinson's disease and 21 healthy controls. Behavioral and MRI contrasts were calculated for alerting, orienting, and executive control components of attention. Brain regions showing group differences in attention processing were used as seeds in a functional connectivity analysis of a separate resting state run. Parkinson's disease subjects showed more activation during increased executive challenge in four regions of the dorsal attention and frontoparietal networks, namely right frontal eye field, left and right intraparietal sulcus, and precuneus. In three regions we saw reduced resting state connectivity to the default mode network. Further, whereas higher task activation in the right intraparietal sulcus correlated with reduced resting state connectivity between right intraparietal sulcus and the precuneus in healthy controls, this relationship was absent in Parkinson's disease subjects. Our results suggest that a weakened interaction between the default mode and task positive networks might alter the way in which the executive response is processed in PD.

Using Make for Reproducible and Parallel Neuroimaging Workflow and Quality-Assurance.

The contribution of this paper is to describe how we can program neuroimaging workflow using Make, a software development tool designed for describing how to build executables from source files. A makefile (or a file of instructions for Make) consists of a set of rules that create or update target files if they have not been modified since their dependencies were last modified. These rules are processed to create a directed acyclic dependency graph that allows multiple entry points from which to execute the workflow. We show that using Make we can achieve many of the features of more sophisticated neuroimaging pipeline systems, including reproducibility, parallelization, fault tolerance, and quality assurance reports. We suggest that Make permits a large step toward these features with only a modest increase in programming demands over shell scripts. This approach reduces the technical skill and time required to write, debug, and maintain neuroimaging workflows in a dynamic environment, where pipelines are often modified to accommodate new best practices or to study the effect of alternative preprocessing steps, and where the underlying packages change frequently. This paper has a comprehensive accompanying manual with lab practicals and examples (see Supplemental Materials) and all data, scripts, and makefiles necessary to run the practicals and examples are available in the "makepipelines" project at NITRC.

Group comparison of spatiotemporal dynamics of intrinsic networks in Parkinson's disease.

Recent advances with functional connectivity magnetic resonance imaging have demonstrated that at rest the brain exhibits coherent activity within a number of spatially independent maps, normally called 'intrinsic' or 'resting state' networks. These networks support cognition and behaviour, and are altered in neurodegenerative disease. However, there is a longstanding perspective, and ample functional magnetic resonance imaging evidence, demonstrating that intrinsic networks may be fractionated and that cortical elements may participate in multiple intrinsic networks at different times, dynamically changing alliances to adapt to cognitive demands. A method to probe the fine-grained spatiotemporal structure of networks may be more sensitive to subtle network changes that accompany heterogeneous cognitive deficits caused by a neurodegenerative disease such as Parkinson's disease. Here we tested the hypothesis that alterations to the latent (hidden) structure of intrinsic networks may reveal the impact of underlying pathophysiologic processes as assessed with cerebrospinal fluid biomarkers. Using a novel modelling approach that we call 'network kernel analysis', we compared fine-grained network ensembles (network kernels) that include overlapping cortical elements in 24 patients with Parkinson's disease (ages 45-86, 17 male) and normal cognition or mild cognitive impairment (n = 13), and 21 cognitively normal control subjects (ages 41-76, nine male). An omnibus measure of network disruption, calculated from correlations among network kernels, was correlated with cerebrospinal fluid biomarkers of pathophysiological processes in Parkinson's disease: concentrations of α-synuclein and amyloid-ß42. Correlations among network kernels more accurately classified Parkinson's disease from controls than other functional neuroimaging measures. Inspection of the spatial maps related to the default mode network and a frontoparietal task control network kernel showed that the right insula, an area implicated in network shifting and associated with cognitive impairment in Parkinson's disease, was more highly correlated with both these networks in Parkinson's disease than in controls. In Parkinson's disease, increased correlation of the insula with the default mode network was related to lower attentional accuracy. We demonstrated that in an omnibus sense, correlations among network kernels describe biological impact of pathophysiological processes (through correlation with cerebrospinal fluid biomarkers) and clinical status (by classification of patient group). At a greater level of detail, we demonstrate aberrant involvement of the insula in the default mode network and the frontal frontoparietal task control network kernel. Network kernel analysis holds promise as a sensitive method for detecting biologically and clinical relevant changes to specific networks that support cognition and are impaired in Parkinson's disease.

Cerebral perfusion and cortical thickness indicate cortical involvement in mild Parkinson's disease.

Cortical dysfunction in Parkinson's disease (PD) may be caused by disruption to ascending systems or by intrinsic cortical neuropathology. We introduce and conduct a joint analysis of metabolism and atrophy capable of identifying whether metabolic disruption occurs in mild PD without cortical atrophy, to determine the extent and spatial pattern of cortical involvement in mild PD. The design was observational, studying 23 cognitively normal participants with mild PD (mean Hoehn & Yahr stage 2) and 21 healthy controls. Cortical thickness (obtained from analysis of structural magnetic resonance imaging [MRI] with FreeSurfer) and cerebral perfusion measures (obtained from arterial spin labeling [ASL]) analyzed independently and then together in a joint multiple factorial analysis to identify spatial patterns of perfusion and cortical thickness. We identify a pattern of changes in perfusion and cortical thickness characterized by symmetric parietal cortical thinning and reduced precuneus perfusion, with relative preservation of thickness and perfusion in the anterior cingulate cortex (ACC), right prefrontal gyrus, and medial frontal gyrus. The expression of this pattern is correlated with motor system symptoms and speed of processing. A spatial pattern of joint parietal cortical thinning and disproportionate reduction in perfusion occurs in our nondemented PD sample. We found no PD-related components of reduced perfusion without cortical thinning. This suggests that PD affects the cortex itself, even when symptoms are relatively mild.

Dynamic connectivity at rest predicts attention task performance.

Consistent spatial patterns of coherent activity, representing large-scale networks, have been reliably identified in multiple populations. Most often, these studies have examined "stationary" connectivity. However, there is a growing recognition that there is a wealth of information in the time-varying dynamics of networks which has neural underpinnings, which changes with age and disease and that supports behavior. Using factor analysis of overlapping sliding windows across 25 participants with Parkinson disease (PD) and 21 controls (ages 41-86), we identify factors describing the covarying correlations of regions (dynamic connectivity) within attention networks and the default mode network, during two baseline resting-state and task runs. Cortical regions that support attention networks are affected early in PD, motivating the potential utility of dynamic connectivity as a sensitive way to characterize physiological disruption to these networks. We show that measures of dynamic connectivity are more reliable than comparable measures of stationary connectivity. Factors in the dorsal attention network (DAN) and fronto-parietal task control network, obtained at rest, are consistently related to the alerting and orienting reaction time effects in the subsequent Attention Network Task. In addition, the same relationship between the same DAN factor and the alerting effect was present during tasks. Although reliable, dynamic connectivity was not invariant, and changes between factor scores across sessions were related to changes in accuracy. In summary, patterns of time-varying correlations among nodes in an intrinsic network have a stability that has functional relevance.

Age-related differences in the dynamic architecture of intrinsic networks.

Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.

The association between higher order abilities, processing speed, and age are variably mediated by white matter integrity during typical aging.

Although aging is associated with changes in brain structure and cognition it remains unclear which specific structural changes mediate individual cognitive changes. Several studies have reported that white matter (WM) integrity, as assessed by diffusion tensor imaging (DTI), mediates, in part, age-related differences in processing speed (PS). There is less evidence for WM integrity mediating age-related differences in higher order abilities (e.g., memory and executive functions). In 165 typically aging adults (age range 54-89) we show that WM integrity in select cerebral regions is associated with higher cognitive abilities and accounts variance not accounted for by PS or age. Specifically, voxel-wise analyses using tract-based spatial statistics (TBSS) revealed that WM integrity was associated with reasoning, cognitive flexibility and PS, but not memory or word fluency, after accounting for age and gender. While cerebral fractional anisotropy (FA) was only associated with PS; mean (MD), axial (AD) and radial (RD) diffusivity were associated with reasoning and flexibility. Reasoning was selectively associated with left prefrontal AD, while cognitive flexibility was associated with MD, AD and RD throughout the cerebrum. Average WM metrics within select WM regions of interest accounted for 18% and 29% of the variance in reasoning and flexibility, respectively, similar to the amount of variance accounted for by age. WM metrics mediated ~50% of the age-related variance in reasoning and flexibility and different proportions, 11% for reasoning and 44% for flexibility, of the variance accounted for by PS. In sum, (i) WM integrity is significantly, but variably, related to specific higher cognitive abilities and can account for a similar proportion of variance as age, and (ii) while FA is selectively associated with PS; while MD, AD and RD are associated with reasoning, flexibility and PS. This illustrates both the anatomical and cognitive selectivity of structure-cognition relationships in the aging brain.