Prophylactic Antibiotic Choice and Deep Infection in Lower Extremity Endoprosthetic Reconstruction: Comparison of Cefazolin, Cefazolin-Vancomycin, and Alternative Regimens.

INTRODUCTION: Infection is a common mode of failure in lower extremity endoprostheses. The Prophylactic Antibiotic Regimens in Tumor Surgery trial reported that 5 days of cefazolin had no difference in surgical site infection compared with 24 hours of cefazolin. Our purpose was to evaluate infection rates of patients receiving perioperative cefazolin monotherapy, cefazolin-vancomycin dual therapy, or alternative antibiotic regimens. METHODS: A single-center retrospective review was conducted on patients who received lower extremity endoprostheses from 2008 to 2021 with minimum 1-year follow-up. Three prophylactic antibiotic regimen groups were compared: cefazolin monotherapy, cefazolin-vancomycin dual therapy, and alternative regimens. The primary outcome was deep infection, defined by a sinus tract, positive culture, or clinical diagnosis. Secondary outcomes were revision surgery, microorganisms isolated, and superficial wound issues. RESULTS: The overall deep infection rate was 10% (30/294) at the median final follow-up of 3.0 years (IQR 1.7 to 5.4). The deep infection rates in the cefazolin, cefazolin-vancomycin, and alternative regimen groups were 8% (6/72), 10% (18/179), and 14% (6/43), respectively (P = 0.625). Patients not receiving cefazolin had an 18% deep infection rate (6/34) and 21% revision surgery rate (7/34) compared with a 9% deep infection rate (24/260) (P = 0.13) and 12% revision surgery rate (31/260) (P = 0.17) in patients receiving cefazolin. In those not receiving cefazolin, 88% (30/34) were due to a documented penicillin allergy, only two being anaphylaxis. All six patients in the alternative regimen group who developed deep infections did not receive cefazolin secondary to nonanaphylactic penicillin allergy. CONCLUSION: The addition of perioperative vancomycin to cefazolin in lower extremity endoprosthetic reconstructions was not associated with a lower deep infection rate. Patients who did not receive cefazolin trended toward higher rates of deep infection and revision surgery, although not statistically significant. The most common reason for not receiving cefazolin was a nonanaphylactic penicillin allergy, highlighting the continued practice of foregoing cefazolin unnecessarily.

Effects of open-label transdermal nicotine antidepressant augmentation on affective symptoms and executive function in late-life depression.

BACKGROUND: Late-life depression (LLD) is characterized by a poor response to antidepressant medications and diminished cognitive performance, particularly in executive functioning. There is currently no accepted pharmacotherapy for LLD that effectively treats both mood and cognitive symptoms. This study investigated whether transdermal nicotine augmentation of standard antidepressant medications benefitted mood and cognitive symptoms in LLD. METHODS: Nonsmoking participants aged 60 years or older with unremitted LLD on stable SSRI or SNRI medications (N = 29) received transdermal nicotine patches up to a 21 mg daily dose over 12 weeks. Clinical measures assessed depression severity, secondary affective symptoms, and cognitive performance. Nicotine metabolite concentrations were obtained from blood samples. RESULTS: Depression severity significantly decreased over the trial, with a 76 % response rate and 59 % remission rate. Change in depression severity was positively associated with nicotine exposure. Participants also exhibited improvement in self-reported affective symptoms (apathy, insomnia, rumination, and generalized anxiety symptoms), negativity bias, and disability. Executive function test performance significantly improved, specifically in measures of cognitive control, as did subjective cognitive performance. Adverse events were generally mild, with 75 % of the sample tolerating the maximum dose. CONCLUSION: The current study extends our previous pilot open-label trial in LLD, supporting feasibility and tolerability of transdermal nicotine patches as antidepressant augmentation. Although preliminary, this open-label study supports the potential benefit of transdermal nicotine patches for both mood and cognitive symptoms of LLD. Further research, including definitive randomized, blinded trials, is warranted to confirm these findings and explore long-term risk and benefit. TRIAL REGISTRATION: The study was registered with clinicaltrials.gov (NCT04433767).

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

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.

Polyethylene Glycol-Mediated Axonal Fusion Promotes Early Sensory Recovery After Digital Nerve Injury: A Randomized Clinical Trial.

BACKGROUND: Peripheral nerve repair is limited by Wallerian degeneration coupled with the slow and inconsistent rates of nerve regrowth. In more proximal injuries, delayed nerve regeneration can cause debilitating muscle atrophy. Topical application of polyethylene glycol (PEG) during neurorrhaphy facilitates the fusion of severed axonal membranes, immediately restoring action potentials across the coaptation site. In preclinical animal models, PEG-fusion resulted in remarkable early functional recovery. METHODS: This is the first randomized clinical trial comparing functional outcomes between PEG-fusion and standard neurorrhaphy. Participants with digital nerve transections were followed up at 2 weeks, 1 month, and 3 months postoperatively. The primary outcome was assessed using the Medical Research Council Classification (MRCC) rating for sensory recovery at each timepoint. Semmes-Weinstein monofilaments and static two-point discrimination determined MRCC ratings. Postoperative quality of life was measured using the Michigan Hand Questionnaire (MHQ). RESULTS: Forty-eight transected digital nerves (25 control, 23 PEG) across twenty-two patients were analyzed. PEG-fused nerves demonstrated significantly higher MRCC scores at 2 weeks (OR 16.95, 95% CI: 1.79 - 160.38, p = 0.008) and 1 month (OR 13.40, 95% CI: 1.64 - 109.77, p = 0.009). Participants in the PEG cohort also had significantly higher average MHQ scores at 2 weeks (Hodge's g 1.28, 95% CI: 0.23 - 2.30, p = 0.0163) and 1 month (Hodge's g 1.02, 95% CI: 0.04 - 1.99, p = 0.049). No participants had adverse events related to the study drug. CONCLUSION: PEG-fusion promotes early sensory recovery and improved patient well-being following peripheral nerve repair of digital nerves.

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification.

Brain functional connectivity (FC) networks inferred from functional magnetic resonance imaging (fMRI) have shown altered or aberrant brain functional connectome in various neuropsychiatric disorders. Recent application of deep neural networks to connectome-based classification mostly relies on traditional convolutional neural networks (CNNs) using input FCs on a regular Euclidean grid to learn spatial maps of brain networks neglecting the topological information of the brain networks, leading to potentially sub-optimal performance in brain disorder identification. We propose a novel graph deep learning framework that leverages non-Euclidean information inherent in the graph structure for classifying brain networks in major depressive disorder (MDD). We introduce a novel graph autoencoder (GAE) architecture, built upon graph convolutional networks (GCNs), to embed the topological structure and node content of large fMRI networks into low-dimensional representations. For constructing the brain networks, we employ the Ledoit-Wolf (LDW) shrinkage method to efficiently estimate high-dimensional FC metrics from fMRI data. We explore both supervised and unsupervised techniques for graph embedding learning. The resulting embeddings serve as feature inputs for a deep fully-connected neural network (FCNN) to distinguish MDD from healthy controls (HCs). Evaluating our model on resting-state fMRI MDD dataset, we observe that the GAE-FCNN outperforms several state-of-the-art methods for brain connectome classification, achieving the highest accuracy when using LDW-FC edges as node features. The graph embeddings of fMRI FC networks also reveal significant group differences between MDD and HCs. Our framework demonstrates the feasibility of learning graph embeddings from brain networks, providing valuable discriminative information for diagnosing brain disorders.

Longitudinal Assessment of Pancreas Volume by MRI Predicts Progression to Stage 3 Type 1 Diabetes.

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.

Genetic risk converges on regulatory networks mediating early type 2 diabetes.

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet ß cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and ß cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by ß cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the ß cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by ß cells. RFX6 perturbation in primary human islet cells alters ß cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.

Exocrine pancreas in type 1 and type 2 diabetes: different patterns of fibrosis, metaplasia, angiopathy, and adiposity.

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 systematic analysis of wellpreserved tissue sections from the pancreatic head, body, and tail of organ donors with T1D (n = 20), type 2 diabetes (T2D, n = 25), and donors with no diabetes (ND, n = 74). Among ND donors, we found that acinar-to-ductal metaplasia (ADM), angiopathy, and pancreatic adiposity increased with age, while ADM and adiposity also increased with BMI. Compared to age- and sex-matched ND organs, T1D pancreata had greater acinar atrophy and angiopathy with fewer intralobular adipocytes. T2D pancreata had greater ADM, angiopathy, and total T lymphocytes, but no difference in adipocyte number, compared to ND organs. While total pancreatic fibrosis was increased in both T1D and T2D, the pattern was different with T1D pancreata having greater periductal and perivascular fibrosis, whereas T2D pancreata had greater lobular and parenchymal fibrosis. Thus, the exocrine pancreas undergoes distinct changes as individuals age or develop T1D or T2D.

Accentuated Paralimbic and Reduced Mesolimbic D2/3-Impulsivity Associations in Parkinson's Disease.

Impulsivity is a behavioral trait that is elevated in many neuropsychiatric disorders. Parkinson's disease (PD) patients can exhibit a specific pattern of reward-seeking impulsive-compulsive behaviors (ICBs), as well as more subtle changes to generalized trait impulsivity. Prior studies in healthy controls (HCs) suggest that trait impulsivity is regulated by D2/3 autoreceptors in mesocorticolimbic circuits. While altered D2/3 binding is noted in ICB+ PD patients, there is limited prior assessment of the trait impulsivity-D2/3 relationship in PD, and no prior direct comparison with patterns in HCs. We examined 54 PD (36 M; 18 F) and 31 sex- and age-matched HC (21 M; 10 F) subjects using [18F]fallypride, a high-affinity D2/3 receptor ligand, to measure striatal and extrastriatal D2/3 nondisplaceable binding potential (BPND). Subcortical and cortical assessment exclusively used ROI or exploratory-voxelwise methods, respectively. All completed the Barratt Impulsiveness Scale, a measure of trait impulsivity. Subcortical ROI analyses indicated a negative relationship between trait impulsivity and D2/3 BPND in the ventral striatum and amygdala of HCs but not in PD. By contrast, voxelwise methods demonstrated a positive trait impulsivity-D2/3 BPND correlation in ventral frontal olfactocentric-paralimbic cortex of subjects with PD but not HCs. Subscale analysis also highlighted different aspects of impulsivity, with significant interactions between group and motor impulsivity in the ventral striatum, and attentional impulsivity in the amygdala and frontal paralimbic cortex. These results suggest that dopamine functioning in distinct regions of the mesocorticolimbic circuit influence aspects of impulsivity, with the relative importance of regional dopamine functions shifting in the neuropharmacological context of PD.SIGNIFICANCE STATEMENT The biological determinants of impulsivity have broad clinical relevance, from addiction to neurodegenerative disorders. Here, we address biomolecular distinctions in Parkinson's disease. This is the first study to evaluate a large cohort of Parkinson's disease patients and age-matched healthy controls with a measure of trait impulsivity and concurrent [18F]fallypride PET, a method that allows quantification of D2/3 receptors throughout the mesocorticolimbic network. We demonstrate widespread differences in the trait impulsivity-dopamine relationship, including (1) loss of subcortical relationships present in the healthy brain and (2) emergence of a new relationship in a limbic cortical area. This illustrates the loss of mechanisms of behavioral regulation present in the healthy brain while suggesting a potential compensatory response and target for future investigation.

The Role of a Dopamine-Dependent Limbic-Motor Network in Sensory Motor Processing in Parkinson Disease.

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 [18F]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.

Structural brain differences in essential tremor and Parkinson's disease deep brain stimulation patients.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the most common tremor disorders and are common indications for deep brain stimulation (DBS). In some patients, PD and ET symptoms overlap and diagnosis can be challenging based on clinical criteria alone. The objective of this study was to identify structural brain differences between PD and ET DBS patients to help differentiate these disorders and improve our understanding of the different brain regions involved in these pathologic processes. METHODS: We included ET and PD patients scheduled to undergo DBS surgery in this observational study. Patients underwent 3T brain MRI while under general anesthesia as part of their procedure. Cortical thicknesses and subcortical volumes were quantified from T1-weighted images using automated multi-atlas segmentation. We used logistic regression analysis to identify brain regions associated with diagnosis of ET or PD. RESULTS: 149 ET and 265 PD patients were included. Smaller volumes in the pallidum and thalamus and reduced thickness in the anterior orbital gyrus, lateral orbital gyrus, and medial precentral gyrus were associated with greater odds of ET diagnosis. Conversely, reduced volumes in the caudate, amygdala, putamen, and basal forebrain, and reduced thickness in the orbital part of the inferior frontal gyrus, supramarginal gyrus, and posterior cingulate were associated with greater odds of PD diagnosis. CONCLUSIONS: These findings identify structural brain differences between PD and ET patients. These results expand our understanding of the different brain regions involved in these disorders and suggest that structural MRI may help to differentiate patients with these two disorders.

Racial Differences in the Presentation and Progression of Huntington's Disease.

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.

Harmonization of repetition time and scanner effects on estimates of brain hemodynamic response function.

Multisite contributions are essential to improve the reliability and statistical power of imaging studies but introduce a complexity because of different acquisition protocols and scanners. The hemodynamic response function (HRF) is the transform that relates neural activity to the measured blood oxygenation level-dependent (BOLD) signal in MRI and contains information about the latency, amplitude, and duration of neuronal activations. Acquisition variabilities, without adding harmonization techniques, can severely limit our ability to characterize spatial effects. To address this problem, we propose to study and remove variabilities of the sampling rate and scanners on estimates of the HRF. We computed the HRF using a blind deconvolution method in 547 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) across 62 sites and 18 scanners. The approach consists of studying the changes of the response according to repetition times (TR) and scanner models. We applied ComBAT, a statistical multi-site harmonization technique, to evaluate and reduce the scanner and repetition time effects and used the Wilcoxon rank sum test to assess the performance of the harmonization. Results show high scanner and repetition time variabilities (|d| ≥ 0.38, p = 4.5 × 10-5) across features, indicating that using harmonization is crucial in multi-site studies. ComBAT successfully removes the sampling effects and reduces the variance between scanners for 7 out of 10 of the HRF features (|d| ≤ 0.05, p = 0.0052). Scanners effects have been characterized on multi-site datasets, but the repetition time impact has been less studied. We showed that the use of different values of repetition time leads to changes in HRF behavior. Regression modeling changes in the HRF on the harmonized data are not significant (p = 0.0401) which does not allow to conclude how HRF changes with aging.

Cognitive, Disability, and Treatment Outcome Implications of Symptom-Based Phenotyping in Late-Life Depression.

OBJECTIVE: Late-life depression is associated with substantial heterogeneity in clinical presentation, disability, and response to antidepressant treatment. We examined whether self-report of severity of common symptoms, including anhedonia, apathy, rumination, worry, insomnia, and fatigue were associated with differences in presentation and response to treatment. We also examined whether these symptoms improved during treatment with escitalopram. DESIGN: Eighty-nine older adults completed baseline assessments, neuropsychological testing and providing self-reported symptom and disability scales. They then entered an 8-week, placebo-controlled randomized trial of escitalopram, and self-report scales were repeated at the trial's end. Raw symptom scale scores were combined into three standardized symptom phenotypes and models examined how symptom phenotype severity was associated with baseline measures and depression improvement over the trial. RESULTS: While rumination/worry appeared independent, severity of apathy/anhedonia and fatigue/insomnia were associated with one another and with greater self-reported disability. Greater fatigue/insomnia was also associated with slower processing speed, while rumination/worry was associated with poorer episodic memory. No symptom phenotype severity score predicted a poorer overall response to escitalopram. In secondary analyses, escitalopram did not improve most phenotypic symptoms more than placebo, aside for greater reductions in worry and total rumination severity. CONCLUSION: Deeper symptom phenotype characterization may highlight differences in the clinical presentation of late-life depression. However, when compared to placebo, escitalopram did not improve many of the symptoms assessed. Further work is needed to determine whether symptom phenotypes inform longer-term course of illness, and which treatments may best benefit specific symptoms.

Corticostriatal beta oscillation changes associated with cognitive function in Parkinson's disease.

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.

Parasagittal dural space hypertrophy and amyloid-ß deposition in Alzheimer's disease.

One of the pathological hallmarks of Alzheimer's and related diseases is the increased accumulation of protein amyloid-ß in the brain parenchyma. As such, recent studies have focused on characterizing protein and related clearance pathways involving perivascular flow of neurofluids, but human studies of these pathways are limited owing to limited methods for evaluating neurofluid circulation non-invasively in vivo. Here, we utilize non-invasive MRI methods to explore surrogate measures of CSF production, bulk flow and egress in the context of independent PET measures of amyloid-ß accumulation in older adults. Participants (N = 23) were scanned at 3.0 T with 3D T2-weighted turbo spin echo, 2D perfusion-weighted pseudo-continuous arterial spin labelling and phase-contrast angiography to quantify parasagittal dural space volume, choroid plexus perfusion and net CSF flow through the aqueduct of Sylvius, respectively. All participants also underwent dynamic PET imaging with amyloid-ß tracer 11C-Pittsburgh Compound B to quantify global cerebral amyloid-ß accumulation. Spearman's correlation analyses revealed a significant relationship between global amyloid-ß accumulation and parasagittal dural space volume (rho = 0.529, P = 0.010), specifically in the frontal (rho = 0.527, P = 0.010) and parietal (rho = 0.616, P = 0.002) subsegments. No relationships were observed between amyloid-ß and choroid plexus perfusion nor net CSF flow. Findings suggest that parasagittal dural space hypertrophy, and its possible role in CSF-mediated clearance, may be closely related to global amyloid-ß accumulation. These findings are discussed in the context of our growing understanding of the physiological mechanisms of amyloid-ß aggregation and clearance via neurofluids.

Longitudinal MRI Shows Progressive Decline in Pancreas Size and Altered Pancreas Shape in Type 1 Diabetes.

CONTEXT: Individuals with type 1 diabetes (T1D) have a smaller pancreas, but longitudinal changes in pancreas size and shape are unclear. OBJECTIVE: We monitored changes in pancreas size and shape after diagnosis with T1D. DESIGN: We conducted a prospective cohort study at an academic medical center between 2014 and 2022. PATIENTS AND HEALTHY CONTROLS: Individuals with T1D (n = 91) or controls (n = 90) underwent magnetic resonance imaging (MRI) of the pancreas, including longitudinal MRI in 53 individuals with new-onset T1D. INTERVENTION: Interventions included MRI and continuous glucose monitoring (CGM). MAIN OUTCOME MEASURES: Pancreas size and shape were measured from MRI. For participants who used CGM, measures of glycemic variability were calculated. RESULTS: On longitudinal imaging, pancreas volume and pancreas volume index normalized for body weight declined during the first year after diagnosis. Pancreas volume index continued to decline through the fifth year after diagnosis. A cross-sectional study of individuals with diabetes duration up to 60 years demonstrated that pancreas size in adults negatively correlated with age and disease duration, whereas pancreas volume and pancreas volume index remained stable in controls. Pancreas volume index correlated inversely with low blood glucose index, a measure of risk for hypoglycemia. Pancreas shape was altered in individuals with T1D and further diverged from controls over the first 5 years after diagnosis. Pancreas size and shape are altered in nondiabetic individuals at genetic risk for T1D. Combined pancreas size and shape analysis better distinguished the pancreas of individuals with T1D from controls than size alone. CONCLUSIONS: Pancreas size declines most rapidly near the clinical diagnosis of T1D and continues to decline throughout adulthood. Declines in pancreas size are accompanied by changes in pancreas shape.