A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI.

Recent work demonstrating low test-retest reliability of neural activation during fMRI tasks raises questions about the utility of task-based fMRI for the study of individual variation in brain function. Two possible sources of the instability in task-based BOLD signal over time are noise or measurement error in the instrument, and meaningful variation across time within-individuals in the construct itself-brain activation elicited during fMRI tasks. Examining the contribution of these two sources of test-retest unreliability in task-evoked brain activity has far-reaching implications for cognitive neuroscience. If test-retest reliability largely reflects measurement error, it suggests that task-based fMRI has little utility in the study of either inter- or intra-individual differences. On the other hand, if task-evoked BOLD signal varies meaningfully over time, it would suggest that this tool may yet be well suited to studying intraindividual variation. We parse these sources of variance in BOLD signal in response to emotional cues over time and within-individuals in a longitudinal sample with 10 monthly fMRI scans. Test-retest reliability was low, reflecting a lack of stability in between-person differences across scans. In contrast, within-person, within-session internal consistency of the BOLD signal was higher, and within-person fluctuations across sessions explained almost half the variance in voxel-level neural responses. Additionally, monthly fluctuations in neural response to emotional cues were associated with intraindividual variation in mood, sleep, and exposure to stressors. Rather than reflecting trait-like differences across people, neural responses to emotional cues may be more reflective of intraindividual variation over time. These patterns suggest that task-based fMRI may be able to contribute to the study of individual variation in brain function if more attention is given to within-individual variation approaches, psychometrics-beginning with improving reliability beyond the modest estimates observed here, and the validity of task fMRI beyond the suggestive associations reported here.

Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease.

Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.

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.

Lifestyle Risk Factors and Findings on Brain Magnetic Resonance Imaging of Older Adult American Indians: The Strong Heart Study.

BACKGROUND: Clinical stroke is prevalent in American Indians, but the lifestyle risk factors for vascular brain injury have not been well-studied in this population. The purpose of this study was to correlate brain magnetic resonance imaging (MRI) findings with obesity, alcohol use, and smoking behaviors in elderly American Indians from the Strong Heart Study. METHODS: Cranial MRI scans (n = 789) were analyzed for dichotomous measures of infarcts, hemorrhages, white matter hyperintensities (WMH), and cerebral atrophy and continuous measures of total brain, WMH, and hippocampal volume. Poisson regression was used to estimate prevalence ratios, and linear regression was used to estimate measures of association for continuous outcomes. Models were adjusted for the risk factors of interest as well as age, sex, study site, income, education, hypertension, diabetes, and low-density lipoprotein cholesterol. RESULTS: Smoking was associated with increased hippocampal atrophy (p = 0.002) and increased prevalence of sulcal widening (p < 0.001). Relative to nonsmokers, smokers with more than 25 pack-years of smoking had a 27% (95% CI 7-47%) increased prevalence of high-grade sulci, p = 0.005. Body mass index was inversely associated with prevalence of nonlacunar infarcts and sulcal widening (all p = 0.004). Alcohol use was not significantly associated with any of the measured MRI findings. CONCLUSIONS: This study found similar associations between smoking and vascular brain injury among American Indians, as seen in other populations. In particular, these findings support the role of smoking as a key correlate for cerebral atrophy.

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.

Overview of the cholinergic contribution to gait, balance and falls in Parkinson's disease.

Mobility deficits, including gait disturbance, balance impairments and falls, are common features of Parkinson's disease (PD) that negatively impact quality of life. Mobility deficits respond poorly to dopaminergic medications, indicating a role for additional neurotransmitters. Due to the critical role of cortical input to gait and balance, acetylcholine-an essential neurotransmitter system for attention-has become an area of interest for mobility. This review aimed to identify the role of cholinergic function on gait, balance, and falls in PD using three techniques; pharmacological, imaging, and electrophysiological. Studies supported the role of the cholinergic system for mobility in PD, with the most promising evidence indicating a role in falls. Imaging studies demonstrated involvement of anterior cholinergic (basal forebrain) systems in gait, and posterior (brainstem) systems in balance. However, this review identified a small number of studies which used varying protocols, making comparisons difficult. Further studies are warranted, measuring comprehensive gait and balance characteristics as well as gold standard falls detection to further quantify the relationship between ACh and mobility in PD.

Vascular Risk Factors and Findings on Brain MRI of Elderly Adult American Indians: The Strong Heart Study.

BACKGROUND: Clinical stroke is prevalent in American Indians, but the risk factors for cerebrovascular pathology have not been well-studied in this population. The purpose of this study was to correlate abnormalities on brain magnetic resonance imaging (MRI) with clinical risk factors in a cohort of elderly American Indians. METHODS: Brain MRI scans from 789 participants of the Strong Heart Study were analyzed for infarcts, hemorrhage, white matter disease, and measures of cerebral atrophy including ventricular and sulcal grade and total brain volume. Clinical risk factors included measures of hypertension, diabetes, and high levels of low-density lipoprotein (LDL) cholesterol. Regression models adjusted for potential confounders were used to estimate associations between risk factors and brain MRI outcomes. RESULTS: -Hypertension was associated with the presence of infarcts (p = 0.001), ventricle enlargement (p = 0.01), and increased white matter hyperintensity volume (p = 0.01). Diabetes was associated with increased prevalence of cerebral atrophy (p < 0.001), ventricular enlargement (p = 0.001), and sulcal widening (p = 0.001). High LDL was not significantly associated with any of the measured cranial imaging outcomes. CONCLUSIONS: This study found risk factors for cerebrovascular disease in American Indians similar to those seen in other populations and provides additional evidence for the important roles of hypertension and diabetes in promoting cerebral infarcts and brain atrophy, respectively.

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.

Quantitative cerebrovascular pathology in a community-based cohort of older adults.

Cerebrovascular disease, especially small vessel pathology, is the leading comorbidity in degenerative disorders. We applied arterial spin labeling and cerebrovascular reserve (CVR) imaging to quantify small vessel disease and study its effect on cognitive symptoms in nondemented older adults from a community-based cohort. We evaluated baseline cerebral blood flow (CBF) using arterial spin labeling and percent signal change as a marker of CVR using blood-oxygen level-dependent imaging following a breath-hold stimulus. Measurements were performed in and near white matter hyperintensities, which are currently the standard to assess severity of vascular pathology. We show that similar to other studies (1) CBF and CVR are markedly reduced in the hyperintensities as well as in the tissue surrounding them, indicating susceptibility to infarction; (2) low CBF and CVR are significantly correlated with poor cognitive performance; and (3) in addition, compared to a 58.4% reduction in CBF, larger exhaustion (79.3%) of CVR was observed in the hyperintensities with a faster, nonlinear rate of decline. We conclude that CVR may be a more sensitive biomarker of small vessel disease than CBF.

Current methods and limitations for longitudinal fMRI analysis across development.

The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data.

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.