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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., Competing Interests: AL was employed by the company Etosha Business and Research Consulting. TM was employed by the company Amazon Web Services. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wapstra, Ketola, Thompson, Lee, Madhyastha, Grabowski and Stocco.)

Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Eschenburg, Grabowski and Haynor.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Webster, Grabowski, Madhyastha, Gibbons, Keene and Latimer.)

Purpose: Recently, the cerebellum's role in Parkinson's disease (PD) has been highlighted. Therefore, this study sought to test the hypothesis that functional connectivity (FC) between cerebellar and cortical nodes of the resting-state networks differentiates PD patients from controls by scanning participants at rest using functional magnetic resonance imaging (fMRI) and investigating connectivity of the cerebellar nodes of the resting-state networks. Materials and Methods: Sixty-two PD participants off medication for at least 12 h and 33 normal controls (NCs) were scanned at rest using blood oxygenation level-dependent fMRI scans. Motor and cognitive functions were assessed with the Movement Disorder Society's Revision of the Unified Parkinson's Disease Rating Scale III and Montreal Cognitive Assessment, respectively. Connectivity was investigated with cerebellar seeds defined by Buckner's 7-network atlas. Results: PD participants had significant differences in FC when compared to NC participants. Most notably, PD patients had higher FC between cerebellar nodes of the somatomotor network (SMN) and the corresponding cortical nodes. Cognitive functioning was differentially associated with connectivity of the cerebellar SMN and dorsal attention network. Further, cerebellar connectivity of frontoparietal and default mode networks correlated with the severity of motor function. Conclusion: Our study demonstrates altered cerebello-cortical FC in PD, as well as an association of this FC with PD-related motor and cognitive disruptions, thus providing additional evidence for the cerebellum's role in PD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Palmer, Cholerton, Zabetian, Montine, Grabowski and Rane.)

Background: Mobility and sensorimotor inhibition impairments are heterogeneous in Parkinson's disease (PD). Genetics may contribute to this heterogeneity since the apolipoprotein ( APOE ) ε4 allele and glucocerebrosidase ( GBA ) gene variants have been related to mobility impairments in otherwise healthy older adult (OA) and PD cohorts. The purpose of this study is to determine if APOE or GBA genetic status affects sensorimotor inhibition and whether the relationship between sensorimotor inhibition and mobility differs in genetic sub-groups of PD. Methods: Ninety-three participants with idiopathic PD (53 non-carriers; 23 ε4 carriers; 17 GBA variants) and 72 OA (45 non-carriers; 27 ε4 carriers) had sensorimotor inhibition characterized by short-latency afferent inhibition. Mobility was assessed in four gait domains (pace/turning, rhythm, trunk, variability) and two postural sway domains (area/jerkiness and velocity) using inertial sensors. Results: Sensorimotor inhibition was worse in the PD than OA group, with no effect of genetic status. Gait pace/turning was slower and variability was higher ( p < 0.01) in PD compared to OA. Postural sway area/jerkiness ( p < 0.01) and velocity ( p < 0.01) were also worse in the PD than OA group. Genetic status was not significantly related to any gait or postural sway domain. Sensorimotor inhibition was significantly correlated with gait variability ( r = 0.27; p = 0.02) and trunk movement ( r = 0.23; p = 0.045) in the PD group. In PD non-carriers, sensorimotor inhibition related to variability ( r = 0.35; p = 0.010) and trunk movement ( r = 0.31; p = 0.025). In the PD ε4 group, sensorimotor inhibition only related to rhythm ( r = 0.47; p = 0.024), while sensorimotor inhibition related to pace/turning ( r = -0.49; p = 0.046) and rhythm ( r = 0.59; p = 0.013) in the PD GBA group. Sensorimotor inhibition was significantly correlated with gait pace/turning ( r = -0.27; p = 0.04) in the OA group. There was no relationship between sensorimotor inhibition and postural sway. Conclusion: ε4 and GBA genetic status did not affect sensorimotor inhibition or mobility impairments in this PD cohort. However, worse sensorimotor inhibition was associated with gait variability in PD non-carriers, but with gait rhythm in PD ε4 carriers and with gait rhythm and pace in PD with GBA variants. Impaired sensorimotor inhibition had a larger effect on mobility in people with PD than OA and affected different domains of mobility depending on genetic status., (Copyright © 2020 Martini, Morris, Kelly, Hiller, Chung, Hu, Zabetian, Oakley, Poston, Mata, Edwards, Lapidus, Grabowski, Montine, Quinn and Horak.)

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.

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.

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.

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.

High gamma oscillations (70-150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with blood oxygen level dependence (BOLD) functional magnetic resonance imaging (fMRI) data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes.

To investigate the impact of sensory-motor systems on the neural organization for language, we conducted an H2 (15)O-PET study of sign and spoken word production (picture-naming) and an fMRI study of sign and audio-visual spoken language comprehension (detection of a semantically anomalous sentence) with hearing bilinguals who are native users of American Sign Language (ASL) and English. Directly contrasting speech and sign production revealed greater activation in bilateral parietal cortex for signing, while speaking resulted in greater activation in bilateral superior temporal cortex (STC) and right frontal cortex, likely reflecting auditory feedback control. Surprisingly, the language production contrast revealed a relative increase in activation in bilateral occipital cortex for speaking. We speculate that greater activation in visual cortex for speaking may actually reflect cortical attenuation when signing, which functions to distinguish self-produced from externally generated visual input. Directly contrasting speech and sign comprehension revealed greater activation in bilateral STC for speech and greater activation in bilateral occipital-temporal cortex for sign. Sign comprehension, like sign production, engaged bilateral parietal cortex to a greater extent than spoken language. We hypothesize that posterior parietal activation in part reflects processing related to spatial classifier constructions in ASL and that anterior parietal activation may reflect covert imitation that functions as a predictive model during sign comprehension. The conjunction analysis for comprehension revealed that both speech and sign bilaterally engaged the inferior frontal gyrus (with more extensive activation on the left) and the superior temporal sulcus, suggesting an invariant bilateral perisylvian language system. We conclude that surface level differences between sign and spoken languages should not be dismissed and are critical for understanding the neurobiology of language.