Search

Prior studies have shown that atrophy in vulnerable cortical regions is associated with an increased risk of progression to clinical dementia. In this work, we utilize the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to investigate the relationship between the temporally changing spatial topography of cortical thickness and conversion from mild cognitive impairment to Alzheimer's disease (AD). We develop a novel Bayesian latent spatial model that employs the spatial information underlying the thickness effects across the cortical surface. The proposed method facilitates the development of imaging markers by reliably quantifying and mapping the regional vulnerability to AD progression across the cortical surface. Simulation results showed substantial gains in statistical power and estimation performance by accounting for the spatial structure of the association. Using MRI data from ADNI, we examined the topographic patterns of anatomic regions where cortical thinning is associated with an increased risk of developing AD. [ABSTRACT FROM AUTHOR]

Purpose: Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression.Methods: From the department's clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics.Results: At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73-0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [- 0.0025, 0.002]).Conclusions: When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression-in particular, ordinal logistic regression that does not assume linearity in its predictors.Level Of Evidence: Prognostic level II. [ABSTRACT FROM AUTHOR]

• Cortical surface registration using hierarchical spherical deformation. • Smooth harmonization from rigid to non-rigid deformation. • Group-wise surface correspondence with no template selection bias. • High registration accuracy in cortical surface parcellation. • Reduced registration distortion in surface area and length. We present hierarchical spherical deformation for a group-wise shape correspondence to address template selection bias and to minimize registration distortion. In this work, we aim at a continuous and smooth deformation field to guide accurate cortical surface registration. In conventional spherical registration methods, a global rigid alignment and local deformation are independently performed. Motivated by the composition of precession and intrinsic rotation, we simultaneously optimize global rigid rotation and non-rigid local deformation by utilizing spherical harmonics interpolation of local composite rotations in a single framework. To this end, we indirectly encode local displacements by such local composite rotations as functions of spherical locations. Furthermore, we introduce an additional regularization term to the spherical deformation, which maximizes its rigidity while reducing registration distortion. To improve surface registration performance, we employ the second order approximation of the energy function that enables fast convergence of the optimization. In the experiments, we validate our method on healthy normal subjects with manual cortical surface parcellation in registration accuracy and distortion. We show an improved shape correspondence with high accuracy in cortical surface parcellation and significantly low registration distortion in surface area and edge length. In addition to validation, we discuss parameter tuning, optimization, and implementation design with potential acceleration. [ABSTRACT FROM AUTHOR]

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global ) Type I error rate is also small. This solution is achieved by employing the likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to “cleaner”-looking brain maps and operational superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. [ABSTRACT FROM AUTHOR]

Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging (rs-fMRI) connectivity analysis through more realistic assumptions. In simulation, the advantages of such methods are readily demonstrable. However, quantitative empirical validation remains elusive in vivo as the true connectivity patterns are unknown and noise distributions are challenging to characterize, especially in ultra-high field (e.g., 7T fMRI). Though the physiological characteristics of the fMRI signal are difficult to replicate in controlled phantom studies, it is critical that the performance of statistical techniques be evaluated. The SIMulation EXtrapolation (SIMEX) method has enabled estimation of bias with asymptotically consistent estimators on empirical finite sample data by adding simulated noise refid="ref1"/. To avoid the requirement of accurate estimation of noise structure, the proposed quantitative evaluation approach leverages the theoretical core of SIMEX to study the properties of inference methods in the face of diminishing data (in contrast to increasing noise). The performance of ordinary and robust inference methods in simulation and empirical rs-fMRI are compared using the proposed quantitative evaluation approach. This study provides a simple, but powerful method for comparing a proxy for inference accuracy using empirical data. [ABSTRACT FROM PUBLISHER]

It remains a clinical challenge to differentiate brain tumors from radiation-induced necrosis in the brain. Despite significant improvements, no single MRI method has been validated adequately in the clinical setting. Multi-parametric MRI (mpMRI) was performed to differentiate 9L gliosarcoma from radiation necrosis in animal models. Five types of MRI methods probed complementary information on different scales i.e., T 2 (relaxation), CEST based APT (probing mobile proteins/peptides) and rNOE (mobile macromolecules), qMT (macromolecules), diffusion based ADC (cell density) and SSIFT iAUC (cell size), and perfusion based DSC (blood volume and flow). For single MRI parameters, iAUC and ADC provide the best discrimination of radiation necrosis and brain tumor. For mpMRI, a combination of iAUC, ADC, and APT shows the best classification performance based on a two-step analysis with the Lasso and Ridge regressions. A general mpMRI approach is introduced to choosing candidate multiple MRI methods, identifying the most effective parameters from all the mpMRI parameters, and finding the appropriate combination of chosen parameters to maximize the classification performance to differentiate tumors from radiation necrosis. [ABSTRACT FROM AUTHOR]

The goal of this article is to model cognitive control related activation among predefined regions of interest (ROIs) of the human brain while properly adjusting for the underlying spatio-temporal correlations. Standard approaches to fMRI analysis do not simultaneously take into account both the spatial and temporal correlations that are prevalent in fMRI data. This is primarily due to the computational complexity of estimating the spatio-temporal covariance matrix. More specifically, they do not take into account multiscale spatial correlation (between-ROIs and within-ROI). To address these limitations, we propose a spatio-spectral mixed-effects model. Working in the spectral domain simplifies the temporal covariance structure because the Fourier coefficients are approximately uncorrelated across frequencies. Additionally, by incorporating voxel-specific and ROI-specific random effects, the model is able to capture the multiscale spatial covariance structure: distance-dependent local correlation (within an ROI), and distance-independent global correlation (between-ROIs). Building on existing theory on linear mixed-effects models to conduct estimation and inference, we applied our model to fMRI data to study activation in prespecified ROIs in the prefontal cortex and estimate the correlation structure in the network. Simulation studies demonstrate that ignoring the multiscale correlation leads to higher false positive error rates. [ABSTRACT FROM AUTHOR]

The endocrine and exocrine compartments of the pancreas are spatially related but functionally distinct. Multiple diseases affect both compartments, including type 1 diabetes (T1D), pancreatitis, cystic fibrosis, and pancreatic cancer. To better understand how the exocrine pancreas changes with age, obesity, and diabetes, we performed a systematic analysis of well-preserved tissue sections from the pancreatic head, body, and tail of organ donors with T1D (n = 20) or type 2 diabetes (T2D) (n = 25) and donors with no diabetes (ND; n = 74). Among ND donors, we found that the incidence of acinar-to-ductal metaplasia (ADM), angiopathy, and pancreatic adiposity increased with age, and ADM and adiposity incidence also increased with BMI. Compared with age- and sex-matched ND organs, T1D pancreata had greater rates of acinar atrophy and angiopathy, with fewer intralobular adipocytes. T2D pancreata had greater rates of ADM and angiopathy and a higher total number of T lymphocytes, but no difference in adipocyte number, compared with ND organs. Although total pancreatic fibrosis was increased in both T1D and T2D, the patterns were different, with periductal and perivascular fibrosis occurring more frequently in T1D pancreata and lobular and parenchymal fibrosis occurring more frequently in T2D. Thus, the exocrine pancreas undergoes distinct changes as individuals age or develop T1D or T2D. Article Highlights: We performed a systematic histologic analysis of 119 well-preserved pancreata from deceased organ donors: 74 without diabetes, 20 with type 1 diabetes, and 25 with type 2 diabetes. In donors without diabetes, patterns of pancreatic metaplasia, angiopathy, fibrosis, and adiposity varied across a broad range of ages and BMIs. In type 1 diabetes, the pancreata had greater rates of acinar atrophy, ductal/vascular fibrosis, and angiopathy, with less frequent adiposity, compared with age- and sex-matched control donors. In type 2 diabetes, there were greater rates of acinar-to-ductal metaplasia, lobular fibrosis, and angiopathy, with no change in pancreatic adipocyte number. The pancreata in type 2 diabetes also had a minimally higher number of exocrine T lymphocytes, with no change in exocrine endothelial cell or macrophage number. [ABSTRACT FROM AUTHOR]

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy. [ABSTRACT FROM AUTHOR]

The importance of metal biology in neurodegenerative diseases such as Huntingtin Disease is well documented with evidence of direct interactions between metals such as copper, zinc, iron and manganese and mutant Huntingtin pathobiology. To date, it is unclear whether these interactions are observed in humans, how this impacts other metals, and how mutant Huntington alters homeostatic mechanisms governing levels of copper, zinc, iron and manganese in cerebrospinal fluid and blood in HD patients. Plasma and cerebrospinal fluid from control, pre-manifest, manifest and late manifest HD participants were collected as part of HD-Clarity. Levels of cerebrospinal fluid and plasma copper, zinc, iron and manganese were measured as well as levels of mutant Huntingtin and neurofilament in a sub-set of cerebrospinal fluid samples. We find that elevations in cerebrospinal fluid copper, manganese and zinc levels are altered early in disease prior to alterations in canonical biomarkers of HD although these changes are not present in plasma. We also evidence that CSF iron is elevated in manifest patients. The relationships between plasma and cerebrospinal fluid metal are altered based on disease stage. These findings demonstrate that there are alterations in metal biology selectively in the CSF which occur prior to changes in known canonical biomarkers of disease. Our work indicates that there are pathological changes related to alterations in metal biology in individuals without elevations in neurofilament and mutant Huntingtin. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This multicenter prospective cohort study compared pancreas volume as assessed by MRI, metabolic scores derived from oral glucose tolerance testing (OGTT), and a combination of pancreas volume and metabolic scores for predicting progression to stage 3 type 1 diabetes (T1D) in individuals with multiple diabetes-related autoantibodies. RESEARCH DESIGN AND METHODS: Pancreas MRI was performed in 65 multiple autoantibody-positive participants enrolled in the Type 1 Diabetes TrialNet Pathway to Prevention study. Prediction of progression to stage 3 T1D was assessed using pancreas volume index (PVI), OGTT-derived Index60 score and Diabetes Prevention Trial–Type 1 Risk Score (DPTRS), and a combination of PVI and DPTRS. RESULTS: PVI, Index60, and DPTRS were all significantly different at study entry in 11 individuals who subsequently experienced progression to stage 3 T1D compared with 54 participants who did not experience progression (P < 0.005). PVI did not correlate with metabolic testing across individual study participants. PVI declined longitudinally in the 11 individuals diagnosed with stage 3 T1D, whereas Index60 and DPTRS increased. The area under the receiver operating characteristic curve for predicting progression to stage 3 from measurements at study entry was 0.76 for PVI, 0.79 for Index60, 0.79 for DPTRS, and 0.91 for PVI plus DPTRS. CONCLUSIONS: These findings suggest that measures of pancreas volume and metabolism reflect distinct components of risk for developing stage 3 type 1 diabetes and that a combination of these measures may provide superior prediction than either alone. [ABSTRACT FROM AUTHOR]

Limbic and motor integration is enabled by a mesial temporal to motor cortex network. Parkinson disease (PD) is characterized by a loss of dorsal striatal dopamine but relative preservation of mesolimbic dopamine early in disease, along with changes to motor action control. Here, we studied 47 patients with PD using the Simon conflict task and [ 18 F]fallypride PET imaging. Additionally, a cohort of 16 patients participated in a single-blinded dextroamphetamine (dAMPH) study. Task performance was evaluated using the diffusion model for conflict tasks, which allows for an assessment of interpretable action control processes. First, a voxel-wise examination disclosed a negative relationship, such that longer non-decision time is associated with reduced D2-like binding potential (BPND) in the bilateral putamen, left globus pallidus, and right insula. Second, an ROI analysis revealed a positive relationship, such that shorter non-decision time is associated with reduced D2-like BPND in the amygdala and ventromedial OFC. The difference in non-decision time between off-dAMPH and on-dAMPH trials was positively associated with D2-like BPND in the globus pallidus. These findings support the idea that dysfunction of the traditional striatal–motor loop underlies action control deficits but also suggest that a compensatory parallel limbic–motor loop regulates motor output. [ABSTRACT FROM AUTHOR]

The goal of this article is to model multisubject task‐induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio‐temporal covariance matrix. Other spatio‐temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double‐wavelet approach for modeling the spatio‐temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio‐temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false‐positive and false‐negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double‐wavelet approach was more consistent than the conventional approach when sample size decreased. [ABSTRACT FROM AUTHOR]

Background: Late-life depression (LLD) is characterized by differences in resting state functional connectivity within and between intrinsic functional networks. This study examined whether clinical improvement to antidepressant medications is associated with pre-randomization functional connectivity in intrinsic brain networks. Methods: Participants were 95 elders aged 60 years or older with major depressive disorder. After clinical assessments and baseline MRI, participants were randomized to escitalopram or placebo with a two-to-one allocation for 8 weeks. Non-remitting participants subsequently entered an 8-week trial of open-label bupropion. The main clinical outcome was depression severity measured by MADRS. Resting state functional connectivity was measured between a priori key seeds in the default mode (DMN), cognitive control, and limbic networks. Results: In primary analyses of blinded data, lower post-treatment MADRS score was associated with higher resting connectivity between: (a) posterior cingulate cortex (PCC) and left medial prefrontal cortex; (b) PCC and subgenual anterior cingulate cortex (ACC); (c) right medial PFC and subgenual ACC; (d) right orbitofrontal cortex and left hippocampus. Lower post-treatment MADRS was further associated with lower connectivity between: (e) the right orbitofrontal cortex and left amygdala; and (f) left dorsolateral PFC and left dorsal ACC. Secondary analyses associated mood improvement on escitalopram with anterior DMN hub connectivity. Exploratory analyses of the bupropion open-label trial associated improvement with subgenual ACC, frontal, and amygdala connectivity. Conclusions: Response to antidepressants in LLD is related to connectivity in the DMN, cognitive control and limbic networks. Future work should focus on clinical markers of network connectivity informing prognosis. Registration: ClinicalTrials.gov NCT02332291 [ABSTRACT FROM AUTHOR]

Background: Huntington's disease (HD) is an autosomal dominant neurodegenerative disease that predominantly impacts a Caucasian population, but few efforts have explored racial differences in presentation and progression. Objective: The aim was to assess the presentation and progression of HD across race groups using the Enroll‐HD longitudinal observational study. Methods: We applied propensity score matching for cytosine–adenine–guanine age product score, and age, to identify White, Hispanic, Asian, and Black participants from the Enroll‐HD database. We compared clinical presentations at baseline, and progression over time, using White participants as a control cohort. Results: Black participants were more severe at baseline across all clinical measures. No significant differences in progression were observed between race groups. Conclusions: We consider the factors driving clinical differences at baseline for Black participants. Our data emphasize the necessary improvement in underrepresented minority recruitment for studies of rare diseases. © 2023 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Cognitive impairment is the most frequent non-motor symptom in Parkinson's disease and is associated with deficits in a number of cognitive functions including working memory. However, the pathophysiology of Parkinson's disease cognitive impairment is poorly understood. Beta oscillations have previously been shown to play an important role in cognitive functions including working memory encoding. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits increases the spectral power of beta oscillations and results in Parkinson's disease motor symptoms. Analogous changes in parallel cognitive CSTC circuits involving the caudate and dorsolateral prefrontal cortex (DLPFC) may contribute to Parkinson's disease cognitive impairment. The objective of our study is to evaluate whether changes in beta oscillations in the caudate and DLPFC contribute to cognitive impairment in Parkinson's disease patients. To investigate this, we used local field potential recordings during deep brain stimulation surgery in 15 patients with Parkinson's disease. Local field potentials were recorded from DLPFC and caudate at rest and during a working memory task. We examined changes in beta oscillatory power during the working memory task as well as the relationship of beta oscillatory activity to preoperative cognitive status, as determined from neuropsychological testing results. We additionally conducted exploratory analyses on the relationship between cognitive impairment and task-based changes in spectral power in additional frequency bands. Spectral power of beta oscillations decreased in both DLPFC and caudate during working memory encoding and increased in these structures during feedback. Subjects with cognitive impairment had smaller decreases in caudate and DLPFC beta oscillatory power during encoding. In our exploratory analysis, we found that similar differences occurred in alpha frequencies in caudate and theta and alpha in DLPFC. Our findings suggest that oscillatory power changes in cognitive CSTC circuits may contribute to cognitive symptoms in patients with Parkinson's disease. These findings may inform the future development of novel neuromodulatory treatments for cognitive impairment in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter‐network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter‐network structural covariance across the lifespan. [ABSTRACT FROM AUTHOR]

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P -value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P -value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P -value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P -value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy. [ABSTRACT FROM AUTHOR]

Central insulin resistance (IR) influences striatal dopamine (DA) tone, an important determinant of behavioral self-regulation. We hypothesized that an association exists between the degree of peripheral IR and impulse control, mediated by the impact of IR on brain circuits controlling the speed of executing “go” and/or “stop” responses. We measured brain activation and associated performance on a stop signal task (SST) in obese adults with type 2 diabetes (age, 48.1 ± 6.9 yrs (mean ± SD); BMI, 36.5 ± 4.0 kg/m2; HOMA-IR, 7.2 ± 4.1; 12 male, 18 female). Increasing IR, but not BMI, was a predictor of shorter critical stop signal delay (cSSD), a measure of the time window during which a go response can be successfully countermanded (R2 = 0.12). This decline was explained by an IR-associated increase in go speed (R2 = 0.13) with little impact of IR or BMI on stop speed. Greater striatal fMRI activation contrast in stop error (SE) compared with stop success (SS) trials (CONSE>SS) was a significant predictor of faster go speeds (R2 = 0.33, p = 0.002), and was itself predicted by greater IR (CONSE>SS vs HOMA-IR: R2 = 0.10, p = 0.04). Furthermore, this impact of IR on striatal activation was a significant mediator of the faster go speeds and greater impulsivity observed with greater IR. These findings suggest a neural mechanism by which IR may increase impulsivity and degrade behavioral self-regulation. [ABSTRACT FROM AUTHOR]

Purpose To evaluate the magnitude of chemical exchange effects and R 1ρ dispersion in muscle and their relationship to tissue sodium levels with aging. Methods Seven healthy volunteers (aged 24 to 87 years, median age 47) underwent MRI to assess tissue sodium levels and water T 1ρ values at different spin-locking frequencies in calf muscles. T 1ρ values at each locking field were computed based on a three-parameter mono-exponential model to fit signals obtained at different locking times, and R 1ρ (= 1/T 1ρ ) rates were compared at different locking fields. In particular, the dispersion of R 1ρ (ΔR 1ρ = R 1ρ (0 Hz) − R 1ρ (500 Hz)) was examined as a function of subject age. Muscle sodium content was calculated by comparing signal intensities between tissues and reference standards within the same image. The variations of ΔR 1ρ with age and sodium were analyzed by linear regression. Results T 1ρ values and sodium content both increased with age. R 1ρ dispersion also increased with age and showed a strong linear correlation (correlation coefficient r = 0.98, P = 0.000578) with sodium content. Conclusion ΔR 1ρ reports on the contribution of labile protons such as hydroxyls which may be associated with macromolecule accumulation in the extracellular matrix (ECM). An increase of sodium signal suggests an enlarged ECM volume fraction and/or an increase in sodium concentration, which occurs during normal aging. The strong correlation between ΔR 1ρ and sodium is likely the consequence of increased ECM and density of total charged sites within the matrix from molecules such as collagens and proteoglycans. The results from this study show the potential use of R 1ρ dispersion and sodium imaging in the assessment of pathological changes in muscle such as fibrosis. [ABSTRACT FROM AUTHOR]

Background: Large national databases have become a common source of information on patterns of cancer care in the United States, particularly for low-incidence diseases such as sarcoma. Although aggregating information from many hospitals can achieve statistical power, this may come at a cost when complex variables must be abstracted from the medical record. There is a current lack of understanding of the frequency of use of the Surveillance, Epidemiology, and End Results (SEER) database and the National Cancer Database (NCDB) over the last two decades in musculoskeletal sarcoma research and whether their use tends to produce papers with conflicting findings. Questions/purposes: (1) Is the number of published studies using the SEER and NCDB databases in musculoskeletal sarcoma research increasing over time? (2) What are the author, journal, and content characteristics of these studies? (3) Do studies using the SEER and the NCDB databases for similar diagnoses and study questions report concordant or discordant key findings? (4) Are the administrative data reported by our institution to the SEER and the NCDB databases concordant with the data in our longitudinally maintained, physician-run orthopaedic oncology dataset? Methods: To answer our first three questions, PubMed was searched from 2001 through 2020 for all studies using the SEER or the NCDB databases to evaluate sarcoma. Studies were excluded from the review if they did not use these databases or studied anatomic locations other than the extremities, nonretroperitoneal pelvis, trunk, chest wall, or spine. To answer our first question, the number of SEER and NCDB studies were counted by year. The publication rate over the 20-year span was assessed with simple linear regression modeling. The difference in the mean number of studies between 5-year intervals (2001-2005, 2006-2010, 2011-2015, 2016-2020) was also assessed with Student t-tests. To answer our second question, we recorded and summarized descriptive data regarding author, journal, and content for these studies. To answer our third question, we grouped all studies by diagnosis, and then identified studies that shared the same diagnosis and a similar major study question with at least one other study. We then categorized study questions (and their associated studies) as having concordant findings, discordant findings, or mixed findings. Proportions of studies with concordant, discordant, or mixed findings were compared. To answer our fourth question, a coding audit was performed assessing the concordance of nationally reported administrative data from our institution with data from our longitudinally maintained, physician-run orthopaedic oncology dataset in a series of patients during the past 3 years. Our orthopaedic oncology dataset is maintained on a weekly basis by the senior author who manually records data directly from the medical record and sarcoma tumor board consensus notes; this dataset served as the gold standard for data comparison. We compared date of birth, surgery date, margin status, tumor size, clinical stage, and adjuvant treatment. Results: The number of musculoskeletal sarcoma studies using the SEER and the NCDB databases has steadily increased over time in a linear regression model (β = 2.51; p < 0.001). The mean number of studies per year more than tripled during 2016-2020 compared with 2011-2015 (39 versus 13 studies; mean difference 26 ± 11; p = 0.03). Of the 299 studies in total, 56% (168 of 299) have been published since 2018. Nineteen institutions published more than five studies, and the most studies from one institution was 13. Orthopaedic surgeons authored 35% (104 of 299) of studies, and medical oncology journals published 44% (130 of 299). Of the 94 studies (31% of total [94 of 299]) that shared a major study question with at least one other study, 35% (33 of 94) reported discordant key findings, 29% (27 of 94) reported mixed key findings, and 44% (41 of 94) reported concordant key findings. Both concordant and discordant groups included papers on prognostic factors, demographic factors, and treatment strategies. When we compared nationally reported administrative data from our institution with our orthopaedic oncology dataset, we found clinically important discrepancies in adjuvant treatment (19% [15 of 77]), tumor size (21% [16 of 77]), surgery date (23% [18 of 77]), surgical margins (38% [29 of 77]), and clinical stage (77% [59 of 77]). Conclusion: Appropriate use of databases in musculoskeletal cancer research is essential to promote clear interpretation of findings, as almost two-thirds of studies we evaluated that asked similar study questions produced discordant or mixed key findings. Readers should be mindful of the differences in what each database seeks to convey because asking the same questions of different databases may result in different answers depending on what information each database captures. Likewise, differences in how studies determine which patients to include or exclude, how they handle missing data, and what they choose to emphasize may result in different messages getting drawn from large-database studies. Still, given the rarity and heterogeneity of sarcomas, these databases remain particularly useful in musculoskeletal cancer research for nationwide incidence estimations, risk factor/prognostic factor assessment, patient demographic and hospital-level variable assessment, patterns of care over time, and hypothesis generation for future prospective studies. Level of Evidence: Level III, therapeutic study. [ABSTRACT FROM AUTHOR]

The aim of this work was to investigate possible sex differences in the patterns of sodium deposition between muscle and skin using sodium MRI. A total of 38 subjects were examined for comparisons: 20 males, aged 25–79 years with a median age of 51; 18 females, aged 38–66 years, median age 53. All subjects underwent sodium MRI scans of the calf muscles together with cross sections through four calibration standards containing known sodium contents (10 mM, 20 mM, 30 mM, and 40 mM). Tissue sodium concentrations (TSC) in muscle and skin were then calculated by comparing signal intensities between tissues and reference standards using a linear analysis. A Wilcoxon rank sum test was applied to the ΔTSC (= TSC muscle − TSC skin ) series of males and females to examine if they were significantly different. Finally, a multiple linear regression was utilized to account for the effects from two potential confounders, age and body mass index (BMI). We found that sodium content appears to be higher in skin than in muscle for men, however women tend to have higher muscle sodium than skin sodium. This sex-relevant sodium deposition is statistically significant ( P = 3.10 × 10 − 5 ) by the Wilcoxon rank sum test, and this difference in distribution seems to be more reliable with increasing age. In the multiple linear regression, gender still has a statistically significant effect ( P < 1.0 × 10 − 4 ) on the difference between sodium deposition in muscle and skin, while taking the effects of age and BMI into account. [ABSTRACT FROM AUTHOR]

• Data support EEG as an important tool to measure delirium-relevant aspects in mice. • During critical illness mice showed slow-wave EEG dominance and sleep disruption. • Hypersensitivity to auditory stimuli during sepsis can be measured in sick mice. • Cognitive disruption was observed in some mice following recovery from systemic inflammation. • Deficits post sepsis associated with altered hippocampal long term potentiation. Sepsis and systemic inflammation are often accompanied by severe encephalopathy, sleep disruption and delirium that strongly correlate with poor clinical outcomes including long-term cognitive deficits. The cardinal manifestations of delirium are fluctuating altered mental status and inattention, identified in critically ill patients by interactive bedside assessment. The lack of analogous assessments in mouse models or clear biomarkers is a challenge to preclinical studies of delirium. In this study, we utilized concurrent measures of telemetric EEG recordings and neurobehavioral tasks in mice to characterize inattention and persistent cognitive deficits following polymicrobial sepsis. During the 24-hour critical illness period for the mice, slow-wave EEG dominance, sleep disruption, and hypersensitivity to auditory stimuli in neurobehavioral tasks resembled clinical observations in delirious patients in which alterations in similar outcome measurements, although measured differently in mice and humans, are reported. Mice were tested for nest building ability 7 days after sepsis induction, when sickness behaviors and spontaneous activity had returned to baseline. Animals that showed persistent deficits determined by poor nest building at 7 days also exhibited molecular changes in hippocampal long-term potentiation compared to mice that returned to baseline cognitive performance. Together, these behavioral and electrophysiological biomarkers offer a robust mouse model with which to further probe molecular pathways underlying brain and behavioral changes during and after acute illness such as sepsis. [ABSTRACT FROM AUTHOR]

Impulsive-compulsive behaviours manifest in a substantial proportion of subjects with Parkinson's disease. Reduced ventral striatum dopamine receptor availability, and increased dopamine release is noted in patients with these symptoms. Prior studies of impulsivity suggest that midbrain D2 autoreceptors regulate striatal dopamine release in a feedback inhibitory manner, and in healthy populations, greater impulsivity is linked to poor proficiency of this inhibition. This has not been assessed in a Parkinson's disease population. Here, we applied 18F-fallypride PET studies to assess striatal and extrastriatal D2-like receptor uptake in a placebo-controlled oral dextroamphetamine sequence. We hypothesized that Parkinson's disease patients with impulsive-compulsive behaviours would have greater ventral striatal dopaminergic response to dextroamphetamine, and that an inability to attenuate ventral striatal dopamine release via midbrain D2 autoreceptors would underlie this response. Twenty patients with Parkinson's disease (mean age = 64.1 ± 5.8 years) both with (n = 10) and without (n = 10) impulsive-compulsive behaviours, participated in a single-blind dextroamphetamine challenge (oral; 0.43 mg/kg) in an OFF dopamine state. All completed PET imaging with 18F-fallypride, a high-affinity D2-like receptor ligand, in the placebo and dextroamphetamine state. Both voxelwise and region of interest analyses revealed dextroamphetamine-induced endogenous dopamine release localized to the ventral striatum, and the caudal-medial orbitofrontal cortex. The endogenous dopamine release observed in the ventral striatum correlated positively with patient-reported participation in reward-based behaviours, as quantified by the self-reported Questionnaire for Impulsivity in Parkinson's disease Rating Scale. In participants without impulsive-compulsive behaviours, baseline midbrain D2 receptor availability negatively correlated with ventral striatal dopamine release; however, this relationship was absent in those with impulsive-compulsive behaviours. These findings emphasize that reward-based behaviours in Parkinson's disease are regulated by ventral striatal dopamine release, and suggest that loss of inhibitory feedback from midbrain autoreceptors may underlie the manifestation of impulsive-compulsive behaviours. [ABSTRACT FROM AUTHOR]

Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50–97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions. [ABSTRACT FROM AUTHOR]

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM. [ABSTRACT FROM AUTHOR]

Conventional regions of interest (ROIs)—level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double‐wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting‐state fMRI data with reasonable computational burden. Another advantage of our double‐wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting‐state fMRI data to study the difference in resting‐state functional connectivity between healthy subjects and patients with major depressive disorder. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes. [ABSTRACT FROM AUTHOR]

Background: Reduced diffusion along perivascular spaces in adults with Alzheimer's-disease-related-dementias has been reported and attributed to reduced glymphatic function.Objectives: To apply quantitative measures of diffusion along, and orthogonal to, perivascular spaces in a cohort of older adults with and without clinical symptoms of alpha-synuclein related neurodegeneration.Methods: 181 adults with Parkinson disease (PD) or essential tremor (ET) additionally sub-classified by the presence of cognitive impairment underwent 3 T MRI. Diffusion-tensor-imaging (spatial resolution = 2x2x2 mm; b-value = 1000 s/mm2; directions = 33) measures of diffusion (mm2/s) parallel and orthogonal to perivascular spaces at the level of the medullary veins, and the ratio of these measures (ALPS-index), were calculated. Regions were identified by a board-certified neuroradiologist from T1-weighted and T2-weighted MRI. Evaluations of motor impairment and mild cognitive impairment (MCI) were interpreted by a board-certified neurologist and neuropsychologist, respectively. Multiple regression with false discovery rate correction was applied to understand how diffusion metrics related to (i) disease category (PD vs. ET), (ii) cognition (MCI status), and (iii) white matter disease severity from the Fazekas score.Results: The ALPS-index was reduced in PD compared to ET participants (p = 0.037). No association between the ALPS-index and MCI status, but an inverse association between the ALPS-index and Fazekas score (p = 0.002), was observed. The ALPS-index was inversely associated with age (p = 0.007).Conclusion: Diffusion aberrations near perivascular spaces are evident in patients with alpha-synuclein related neurodegenerative disorders, and are related to age and white matter disease severity. [ABSTRACT FROM AUTHOR]

Purpose: Pure autonomic failure (PAF) results from an impaired peripheral autonomic nervous system, and clinical symptoms present with orthostatic hypotension. While the impact on cardiovascular indices of orthostatic intolerance are well-characterized, more limited information is available regarding cerebral hemodynamic dysfunction in PAF. The objective of this study was to test the hypothesis that cerebral blood flow (CBF) is reduced in PAF, and to quantify the relationship between CBF and clinical indicators of disease severity, including peripheral supine arterial blood pressure. Methods: Participants with PAF (n = 17) and age- and sex-matched normotensive healthy controls (n = 17) were examined using established clinical rating scales, cardiovascular autonomic function tests, and 3T MRI measurements of CBF. CBF-weighted images were also used to determine the prevalence of venous hyperintensities from the major dural sinuses as evidence of abnormal capillary flow. Nonparametric tests and general linear models were used to evaluate differences and correlations between study variables. Results: Gray matter CBF was higher in PAF (51.1 ± 13.4 mL/100 g/min) compared to controls (42.9 ± 6.5 mL/100 g/min, p = 0.007). Venous hyperintensities were more prevalent in PAF relative to controls, and the presence and degree of venous hyperintensities was associated with higher mean CBF (p = 0.027). In PAF participants, CBF and supine systolic blood pressure were inversely related (Spearman's rho = −0.545, p = 0.024). Conclusions: Findings suggest that PAF patients may exhibit elevated CBF and provide evidence that this condition exerts a hemodynamic impact in the central nervous system. [ABSTRACT FROM AUTHOR]

Introduction: Dopamine medication withdrawal in Parkinson's disease (PD) is commonly employed in clinical practice and can be required for participation in research studies. When asked to withdraw from medications, participants often enquire as to what symptoms they should expect. Objectives: This study sought to improve the informed consent process by identifying patient-reported symptoms when dopamine treatment is withheld. We also sought to provide clinical guidance regarding the extent of these symptoms and consider participant willingness to undergo these assessments. Methods: Participants were recruited from community-based PD programs and support groups in Nashville, Tennessee, USA. A patient-based questionnaire determined the frequency and severity of motor and nonmotor symptoms. The questionnaire also assessed whether patients would be willing to abstain from medication at a future date and under what circumstances. Results: A total of 31/90 participants reported willingness to withdraw from dopaminergic medications for clinical or research purposes. Tremor, walking, and balance were the most common motor symptoms that worsened during this time. Sleep dysfunction, constipation, and tremor were noted as the most severe symptoms. Of note, 10% of participants indicated that they would not be willing to go off medications again, suggesting that a minority of patients find this to be most discomforting. When prompted for a reason why participants would be willing to come off of their medications again, "for clinical purposes" was selected the most. Conclusions: Study teams should list these symptoms in the applications to their institutional review board and in the informed consent to provide guidance for participants. [ABSTRACT FROM AUTHOR]

Background: Safer-at-home orders during the COVID-19 pandemic altered the structure of clinical care for Huntington's disease (HD) patients. This shift provided an opportunity to identify limitations in the current healthcare infrastructure and how these may impact the health and well-being of persons with HD. Objective: The study objectives were to assess the feasibility of remote healthcare delivery in HD patients, to identify socioeconomic factors which may explain differences in feasibility and to evaluate the impact of safer-at-home orders on HD patient stress levels. Methods: This observational study of a clinical HD population during the 'safer-at-home' orders asked patients or caregivers about their current access to healthcare resources and patient stress levels. A chart review allowed for an assessment of socioeconomic status and characterization of HD severity. Results: Two-hundred and twelve HD patients were contacted with 156 completing the survey. During safer-at-home orders, the majority of HD patients were able to obtain medications and see a physician; however, 25% of patients would not commit to regular telehealth visits, and less than 50% utilized an online healthcare platform. We found that 37% of participants were divorced/single, 39% had less than a high school diploma, and nearly 20% were uninsured or on low-income health insurance. Patient stress levels correlated with disease burden. Conclusion: A significant portion of HD participants were not willing to participate in telehealth services. Potential explanations for these limitations may include socioeconomic barriers and caregiving structure. These observations illustrate areas for clinical care improvement to address healthcare disparities in the HD community. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) and essential tremor (ET) are commonly encountered movement disorders. Pathophysiologic processes that localize to the cerebellum are described in both. There are limited studies investigating cerebellar structural changes in these conditions, largely because of inherent challenges in the efficiency of segmentation.Methods: We applied a novel multiatlas cerebellar segmentation method to T1-weighted images in 282 PD and 111 essential tremor patients to define 26 cerebellar lobule volumes. The severity of postural and resting tremor in both populations and gait and postural instability in PD patients were defined using subscores of the UPDRS and Washington Heights-Inwood Genetic Study motor scales. These clinical measurements were related to lobule volume size. Multiple comparisons were controlled using a false discovery rate method.Results: Group differences were identified between ET and PD patients, with reductions in deep cerebellar nucleus volume in ET versus reduced lobule VI volume in PD. In ET patients, lobule VIII was negatively correlated with the severity of postural tremor. In PD patients, lobule IV was positively correlated with resting tremor and total tremor severity. We observed differences in cerebellar structure that localized to sensorimotor lobules of the cerebellum. Lobule volumes appeared to differentially relate to clinical symptoms, suggesting important clinicopathologic distinctions between these conditions. These results emphasize the role of the cerebellum in tremor symptoms and should foster future clinical and pathologic investigations of the sensorimotor lobules of the cerebellum. © 2020 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Objective: Impulsive decision‐making is characterized by actions taken without considering consequences. Patients with Parkinson's disease (PD) who receive dopaminergic treatment, especially dopamine agonists, are at risk of developing impulsive–compulsive behaviors (ICBs). We assessed impulse‐related changes across a large heterogeneous PD population using the Barratt impulsivity scale (BIS‐11) by evaluating BIS‐11 first‐ and second‐order factors. Methods: We assessed a total of 204 subjects: 93 healthy controls (HCs), and 68 ICB– and 43 ICB + PD patients who completed the BIS‐11. Using a general linear model and a least absolute shrinkage and selection operation regression, we compared BIS‐11 scores between the HC, ICB– PD, and ICB + PD groups. Results: Patients with PD rated themselves as more impulsive than HCs in the BIS‐11 total score, second‐order attention domain, and first‐order attention and self‐control domains. ICB + patients recorded higher total scores as well as higher scores in the second‐order non‐planning domain and in self‐control and cognitive complexity than ICB– patients. Interpretation: These results indicate that the patients with PD show particular problems with attentional control, whereas ICB + patients show a distinct problem in cognitive control and complexity. Additionally, it appears that all patients with PD are more impulsive than their age‐ and sex‐matched healthy peers. Increased impulsivity may be a result of the disease course, or attributed to dopaminergic medication use, but these results emphasize the importance of the cognitive components of impulsivity in patients with PD. [ABSTRACT FROM AUTHOR]

There is both clinical and neuroanatomical variability at the single-subject level in Alzheimer's disease, complicating our understanding of brain-behaviour relationships and making it challenging to develop neuroimaging biomarkers to track disease severity, progression, and response to treatment. Prior work has shown that both group-level atrophy in clinical dementia syndromes and complex neurological symptoms in patients with focal brain lesions localize to brain networks. Here, we use a new technique termed 'atrophy network mapping' to test the hypothesis that single-subject atrophy maps in patients with a clinical diagnosis of Alzheimer's disease will also localize to syndrome-specific and symptom-specific brain networks. First, we defined single-subject atrophy maps by comparing cortical thickness in each Alzheimer's disease patient versus a group of age-matched, cognitively normal subjects across two independent datasets (total Alzheimer's disease patients = 330). No more than 42% of Alzheimer's disease patients had atrophy at any given location across these datasets. Next, we determined the network of brain regions functionally connected to each Alzheimer's disease patient's location of atrophy using seed-based functional connectivity in a large (n = 1000) normative connectome. Despite the heterogeneity of atrophied regions at the single-subject level, we found that 100% of patients with a clinical diagnosis of Alzheimer's disease had atrophy functionally connected to the same brain regions in the mesial temporal lobe, precuneus cortex, and angular gyrus. Results were specific versus control subjects and replicated across two independent datasets. Finally, we used atrophy network mapping to define symptom-specific networks for impaired memory and delusions, finding that our results matched symptom networks derived from patients with focal brain lesions. Our study supports atrophy network mapping as a method to localize clinical, cognitive, and neuropsychiatric symptoms to brain networks, providing insight into brain-behaviour relationships in patients with dementia. [ABSTRACT FROM AUTHOR]

Reduced pancreas volume, as measured by non-contrast magnetic resonance imaging (MRI), is observed in individuals with newly-diagnosed type 1 diabetes (T1D) and declines over the first year after diagnosis. In this study, we determined the repeatability and inter-reader reproducibility of pancreas volume measurements by MRI. Test-retest scans in individuals with or without T1D (n = 16) had an intraclass correlation coefficient (ICC) of 0.985 (95% CI 0.961 to 0.995) for pancreas volume. Independent pancreas outlines by two board-certified radiologists (n = 30) yielded an ICC of 0.945 (95% CI 0.889 to 0.973). The mean Dice coefficient, a measurement of the degree of overlap between pancreas regions of interest between the two readers, was 0.77. Prandial state did not influence pancreatic measurements, as stomach volume did not correlate with pancreas volume. These data demonstrate that MRI measurements of pancreas volume between two readers are repeatable and reproducible with ICCs that correspond to excellent clinical significance (ICC > 0.9), are not related to changes in stomach volume, and could be a useful tool for clinical investigation of diabetes and other pancreas pathologies. [ABSTRACT FROM AUTHOR]

Objective: Hereditary neuropathy with liability to pressure palsies (HNPP) is caused by heterozygous deletion of the peripheral myelin protein 22 (PMP22) gene. Patients with HNPP present multifocal, reversible sensory/motor deficits due to increased susceptibility to mechanical pressure. Additionally, age‐dependent axonal degeneration is reported. We hypothesize that length‐dependent axonal loss can be revealed by MRI, irrespective of the multifocal phenotype in HNPP. Methods: Nerve and muscle MRI data were acquired in the proximal and distal leg of patients with HNPP (n = 10) and matched controls (n = 7). More specifically, nerve magnetization transfer ratios (MTR) were evaluated to assay proximal‐to‐distal gradients in nerve degeneration, while intramuscular fat percentages (Fper) were evaluated to assay muscle fat replacement following denervation. Neurological disabilities were assessed via the Charcot‐Marie‐Tooth neuropathy score (CMTNS) for correlation with MRI. Results: Fper values were elevated in HNPP proximal muscle (9.8 ± 2.2%, P = 0.01) compared to controls (6.9 ± 1.0%). We observed this same elevation of HNPP distal muscles (10.5 ± 2.5%, P < 0.01) relative to controls (6.3 ± 1.1%). Additionally, the amplitude of the proximal‐to‐distal gradient in Fper was more significant in HNPP patients than controls (P < 0.01), suggesting length‐dependent axonal loss. In contrast, nerve MTR values were similar between HNPP subjects (sciatic/tibial nerves = 39.4 ± 2.0/34.2 ± 2.5%) and controls (sciatic/tibial nerves = 37.6 ± 3.8/35.5 ± 1.2%). Proximal muscle Fper values were related to CMTNS (r = 0.69, P = 0.03), while distal muscle Fper and sciatic/tibial nerve MTR values were not related to disability. Interpretation: Despite the multifocal nature of the HNPP phenotype, muscle Fper measurements relate to disability and exhibit a proximal‐to‐distal gradient consistent with length‐dependent axonal loss, suggesting that Fper may be a viable biomarker of disease progression in HNPP. [ABSTRACT FROM AUTHOR]

Nerve regeneration after injury must occur in a timely fashion to restore function. Unfortunately, current methods (e.g., electrophysiology) provide limited information following trauma, resulting in delayed management and suboptimal outcomes. Herein, we evaluated the ability of diffusion MRI to monitor nerve regeneration after injury/repair. Sprague-Dawley rats were divided into three treatment groups (sham = 21, crush = 23, cut/repair = 19) and ex vivo diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was performed 1–12 weeks post-surgery. Behavioral data showed a distinction between crush and cut/repair nerves at 4 weeks. This was consistent with DTI, which found that thresholds based on the ratio of radial and axial diffusivities (RD/AD = 0.40 ± 0.02) and fractional anisotropy (FA = 0.53 ± 0.01) differentiated crush from cut/repair injuries. By the 12th week, cut/repair nerves whose behavioral data indicated a partial recovery were below the RD/AD threshold (and above the FA threshold), while nerves that did not recover were on the opposite side of each threshold. Additional morphometric analysis indicated that DTI-derived normalized scalar indices report on axon density (RD/AD: r = −0.54, p < 1e-3; FA: r = 0.56, p < 1e-3). Interestingly, higher-order DKI analyses did not improve our ability classify recovery. These findings suggest that DTI may provide promising biomarkers for distinguishing successful/unsuccessful nerve repairs and potentially identify cases that require reoperation. [ABSTRACT FROM AUTHOR]

The locus coeruleus (LC) is the major origin of norepinephrine in the central nervous system, and is subject to age-related and neurodegenerative changes, especially in disorders such as Parkinson's disease and Alzheimer's disease. Previous studies have shown that neuromelanin (NM)-sensitive MRI can be used to visualize the LC, and it is hypothesized that magnetization transfer (MT) effects are the primary source of LC contrast. The aim of this study was to characterize the MT effects in LC imaging by applying high spatial resolution quantitative MT (qMT) imaging to create parametric maps of the macromolecular content of the LC and surrounding tissues. Healthy volunteers (n = 26; sex = 17 F/9M; age = 41.0 ± 19.1 years) underwent brain MRI on a 3.0 T scanner. qMT data were acquired using a 3D MT-prepared spoiled gradient echo sequence. A traditional NM scan consisting of a T 1 -weighted turbo spin echo sequence with MT preparation was also acquired. The pool-size ratio (PSR) was estimated for each voxel using a single-point qMT approach. The LC was semi-automatically segmented on the MT-weighted images. The MT-weighted images provided higher contrast-ratio between the LC and surrounding pontine tegmentum (PT) (0.215 ± 0.031) than the reference images without MT-preparation (−0.005 ± 0.026) and the traditional NM images (0.138 ± 0.044). The PSR maps showed significant differences between the LC (0.090 ± 0.009) and PT (0.188 ± 0.025). The largest difference between the PSR values in the LC and PT was observed in the central slices, which also correspond to those with the highest contrast-ratio. These results highlight the role of MT in generating NM-related contrast in the LC, and should serve as a foundation for future studies aiming to quantify pathological changes in the LC and surrounding structures in vivo. Image 1 • The human locus coeruleus can be visualized using neuromelanin MRI. • The locus coeruleus contrast is enhanced using magnetization transfer. • The locus coeruleus macromolecular content is lower than adjacent tissues. • T 1 -weighting combined with magnetization transfer produce neuromelanin-related contrast. [ABSTRACT FROM AUTHOR]

We evaluated the associations of subjective (self-reported everyday cognition [ECog]) and objective cognitive measures with regional amyloid-β (Aβ) and tau accumulation in 86 clinically normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative. Regression analyses were conducted to identify whether individual ECog domains (Memory, Language, Organization, Planning, Visuospatial, and Divided Attention) were equally or differentially associated with regional [18F]florbetapir and [18F]flortaucipir uptake and how these associations compared to those obtained with objective cognitive measures. A texture analysis, the weighted 2-point correlation, was used as an additional approach for estimating the whole-brain tau burden without positron emission tomography intensity normalization. Although the strongest models for ECog domains included either tau (planning and visuospatial) or Aβ (memory and organization), the strongest models for all objective measures included Aβ. In Aβ-negative participants, the strongest models for all ECog domains of executive functioning included tau. Our results indicate differential associations of individual subjective cognitive domains with Aβ and tau in clinically normal adults. Detailed characterization of ECog may render a valuable prescreening tool for pathological prediction. • The best models for predicting subjective cognitive decline (SCD) domains of memory and organization include amyloid-β. • The best models for SCD domains of planning and visuospatial skills include tau. • Detailed characterization of SCD may help predict the AD pathological designations. [ABSTRACT FROM AUTHOR]

Abstract The ability to evaluate empirical diffusion MRI acquisitions for quality and to correct the resulting imaging metrics allows for improved inference and increased replicability. Previous work has shown promise for estimation of bias and variance of generalized fractional anisotropy (GFA) but comes at the price of computational complexity. This paper aims to provide methods for estimating GFA, bias of GFA and standard deviation of GFA quickly and accurately. In order to provide a method for bias and variance estimation that can return results faster than the previously studied statistical techniques, three deep, fully-connected neural networks are developed for GFA, bias of GFA, and standard deviation of GFA. The results of these networks are compared to the observed values of the metrics as well as those fit from the statistical techniques (i.e. Simulation Extrapolation (SIMEX) for bias estimation and wild bootstrap for variance estimation). Our GFA network provides predictions that are closer to the true GFA values than a Q-ball fit of the observed data (root-mean-square error (RMSE) 0.0077 vs 0.0082, p <.001). The bias network also shows statistically significant improvement in comparison to the SIMEX-estimated error of GFA (RMSE 0.0071 vs. 0.01, p <.001). [ABSTRACT FROM AUTHOR]

Abstract Background Major depressive disorder (MDD) is one of the most prevalent and debilitating psychiatric disorders. Cognitive complaints are commonly reported in MDD and cognitive impairment is a criterion item for MDD diagnosis. As cognitive processes are increasingly understood as the consequences of distributed interactions between brain regions, a network-based approach may provide novel information about the neurobiological basis of cognitive deficits in MDD. Methods 51 Depressed (MDD, n = 23) and non-depressed (control, n = 28) adult participants completed neuropsychological testing and resting-state fMRI (rsfMRI). Cognitive domain scores (processing speed, working memory, episodic memory, and executive function) were calculated. Anatomical regions of interests were entered as seeds for functional connectivity analyses in: default mode (DMN), salience, and executive control (ECN) networks. Partial correlations controlling for age and sex were conducted for cognitive domain scores and functional connectivity in clusters with significant differences between groups. Results Significant rsfMRI differences between groups were identified in multiple clusters in the DMN and ECN. Greater positive connectivity within the ECN and between ECN and DMN regions was associated with poorer episodic memory performance in the Non-Depressed group but better performance in the MDD group. Greater connectivity within the DMN was associated with better episodic and working memory performance in the Non-Depressed group but worse performance in the MDD group. Conclusions These results provide evidence that cognitive performance in MDD may be associated with aberrant functional connectivity in cognitive networks and suggest patterns of alternate brain function that may support cognitive processes in MDD. [ABSTRACT FROM AUTHOR]

Objective: This study investigated the temporal dynamics of pancreas volume and microstructure in children and adolescents with recent-onset type 1 diabetes (T1D) and individuals without diabetes, including a subset expressing autoantibodies associated with the early stages of T1D.Research Design and Methods: MRI was performed in individuals with recent-onset stage 3 T1D (n = 51; median age 13 years) within 100 days after diagnosis (mean 67 days), 6 months, and 1 year postdiagnosis. Longitudinal MRI measurements were also made in similarly aged control participants (n = 57) and in autoantibody-positive individuals without diabetes (n = 20). The MRI protocol consisted of anatomical imaging to determine pancreas volume and quantitative MRI protocols interrogating tissue microstructure and composition.Results: Within 100 days of diabetes onset, individuals with T1D had a smaller pancreas (median volume 28.6 mL) than control participants (median volume 48.4 mL; P < 0.001), including when normalized by individual weight (P < 0.001). Longitudinal measurements of pancreas volume increased in control participants over the year, consistent with adolescent growth, but pancreas volume declined over the first year after T1D diagnosis (P < 0.001). In multiple autoantibody-positive individuals, the pancreas volume was significantly larger than that of the T1D cohort (P = 0.017) but smaller than that of the control cohort (P = 0.04). Diffusion-weighted MRI showed that individuals with recent-onset T1D had a higher apparent diffusion coefficient (P = 0.012), suggesting a loss of cellular structural integrity, with heterogeneous pancreatic distribution.Conclusions: These results indicate that pancreas volume is decreased in stages 1, 2, and 3 of T1D and decreases during the first year after diabetes onset and that this loss of pancreatic volume is accompanied by microstructural changes. [ABSTRACT FROM AUTHOR]

A subgroup of Parkinson's disease (PD) patients treated with dopaminergic therapy develop compulsive reward-driven behaviors, which can result in life-altering morbidity. The mesocorticolimbic dopamine network guides reward-motivated behavior; however, its role in this treatment-related behavioral phenotype is incompletely understood. Here, mesocorticolimbic network function in PD patients who develop impulsive and compulsive behaviors (ICB) in response to dopamine agonists was assessed using BOLD fMRI. The tested hypothesis was that network connectivity between the ventral striatum and the limbic cortex is elevated in patients with ICB and that reward-learning proficiency reflects the extent of mesocorticolimbic network connectivity. To evaluate this hypothesis, 3.0T BOLD-fMRI was applied to measure baseline functional connectivity on and off dopamine agonist therapy in age and sex-matched PD patients with ( n = 19) or without ( n = 18) ICB. An incentive-based task was administered to a subset of patients ( n = 20) to quantify positively or negatively reinforced learning. Whole-brain voxelwise analyses and region-of-interest-based mixed linear effects modeling were performed. Elevated ventral striatal connectivity to the anterior cingulate gyrus ( P = 0.013), orbitofrontal cortex ( P = 0.034), insula ( P = 0.044), putamen ( P = 0.014), globus pallidus ( P < 0.01), and thalamus ( P < 0.01) was observed in patients with ICB. A strong trend for elevated amygdala-to-midbrain connectivity was found in ICB patients on dopamine agonist. Ventral striatum-to-subgenual cingulate connectivity correlated with reward learning ( P < 0.01), but not with punishment-avoidance learning. These data indicate that PD-ICB patients have elevated network connectivity in the mesocorticolimbic network. Behaviorally, proficient reward-based learning is related to this enhanced limbic and ventral striatal connectivity. Hum Brain Mapp 39:509-521, 2018. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: Evaluation of [18F]fluoromisonidazole ([18F]FMISO)-positron emission tomography (PET) imaging as a metric for evaluating early response to trastuzumab therapy with histological validation in a murine model of HER2+ breast cancer.Procedures: Mice with BT474, HER2+ tumors, were imaged with [18F]FMISO-PET during trastuzumab therapy. Pimonidazole staining was used to confirm hypoxia from imaging.Results: [18F]FMISO-PET indicated significant decreases in hypoxia beginning on day 3 (P < 0.01) prior to changes in tumor size. These results were confirmed with pimonidazole staining on day 7 (P < 0.01); additionally, there was a significant positive linear correlation between histology and PET imaging (r 2  = 0.85).Conclusions: [18F]FMISO-PET is a clinically relevant modality which provides the opportunity to (1) predict response to HER2+ therapy before changes in tumor size and (2) identify decreases in hypoxia which has the potential to guide subsequent therapy. [ABSTRACT FROM AUTHOR]

Background and Purpose Clinically, Parkinson's disease ( PD) presents with asymmetric motor symptoms. The left nigrostriatal system appears more susceptible to early degeneration than the right, and a left-lateralized pattern of early neuropathological changes is also described in several neurodegenerative conditions, including Alzheimer's disease, frontotemporal dementia, and Huntington's disease. In this study, we evaluated hemispheric differences in estimated rates of atrophy in a large, well-characterized cohort of PD patients. Methods Our cohort included 205 PD patients who underwent clinical assessments and T1-weighted brain MRI's. Patients were classified into Early ( n = 109) and Late stage ( n = 96) based on disease duration, defined as greater than or less than 10 years of motor symptoms. Cortical thickness was determined using FreeSurfer, and a bootstrapped linear regression model was used to estimate differences in rates of atrophy between Early and Late patients. Results Our results show that patients classified as Early stage exhibit a greater estimated rate of cortical atrophy in left frontal regions, especially the left insula and olfactory sulcus. This pattern was replicated in left-handed patients, and was not influenced by the degree of motor symptom asymmetry (i.e., left-sided predominant motor symptoms). Patients classified as Late stage exhibited greater atrophy in the bilateral occipital, and right hemisphere-predominant cortical areas. Conclusions We show that cortical degeneration in PD differs between cerebral hemispheres, and findings suggest a pattern of early left, and late right hemisphere with posterior cortical atrophy. Further investigation is warranted to elucidate the underlying mechanisms of this asymmetry and pathologic implications. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies have demonstrated how imaging of the breast with patients lying prone using a supportive positioning device markedly facilitates longitudinal and/or multimodal image registration. In this contribution, the authors' primary objective was to determine if there are differences in the standardized uptake value (SUV) derived from [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in breast tumors imaged in the standard supine position and in the prone position using a specialized positioning device. Methods: A custom positioning device was constructed to allow for breast scanning in the prone position. Rigid and nonrigid phantom studies evaluated differences in prone and supine PET. Clinical studies comprised 18F-FDG-PET of 34 patients with locally advanced breast cancer imaged in the prone position (with the custom support) followed by imaging in the supine position (without the support). Mean and maximum values (SUVpeak and SUVmax, respectively) were obtained from tumor regions-of-interest for both positions. Prone and supine SUV were linearly corrected to account for the differences in 18F-FDG uptake time. Correlation, Bland-Altman, and nonparametric analyses were performed on uptake time-corrected and uncorrected data. Results: SUV from the rigid PET breast phantom imaged in the prone position with the support device was 1.9% lower than without the support device. In the nonrigid PET breast phantom, prone SUV with the support device was 5.0% lower than supine SUV without the support device. In patients, the median (range) difference in uptake time between prone and supine scans was 16.4 min (13.4-30.9 min), which was significantly--but not completely--reduced by the linear correction method. SUVpeak and SUVmax from prone versus supine scans were highly correlated, with concordance correlation coefficients of 0.91 and 0.90, respectively. Prone SUVpeak and SUVmax were significantly lower than supine in both original and uptake time-adjusted data across a range of index times (P << 0.0001,Wilcoxon signed rank test). Before correcting for uptake time differences, Bland-Altman analyses revealed proportional bias between prone and supine measurements (SUVpeak and SUVmax) that increased with higher levels of FDG uptake. After uptake time correction, this bias was significantly reduced (P < 0.01). Significant prone-supine differences, with regard to the spatial distribution of lesions relative to isocenter, were observed between the two scan positions, but this was poorly correlated with the residual (uptake time-corrected) prone-supine SUVpeak difference (P = 0.78). Conclusions: Quantitative 18F-FDG-PET/CT of the breast in the prone position is not deleteriously affected by the support device but yields SUV that is consistently lower than those obtained in the standard supine position. SUV differences between scans arising from FDG uptake time differences can be substantially reduced, but not removed entirely, with the current correction method. SUV from the two scan orientations is quantitatively different and should not be assumed equivalent or interchangeable within the same subject. These findings have clinical relevance in that they underscore the importance of patient positioning while scanning as a clinical variable that must be accounted for with longitudinal PET measurement, for example, in the assessment of treatment response. [ABSTRACT FROM AUTHOR]