Faculty Bibliography

INTRODUCTION/BACKGROUND:In addition to neurogenic orthostatic hypotension (nOH), patients with synucleinopathies frequently have hypertension when supine. The long-term consequences of both abnormalities are difficult to disentangle. We aimed to determine if supine hypertension is associated with target organ damage and worse survival in patients with nOH. METHODS:Patients with nOH due to multiple system atrophy (MSA), Parkinson disease (PD), or pure autonomic failure (PAF) were classified into those with or without supine hypertension (systolic BP of at least 140 mmHg or diastolic BP of at least 90 mmHg). Organ damage was assessed by measuring cerebral white matter hyperintensities (WMH), left ventricular hypertrophy (LVH), and renal function. We prospectively followed patients for 30 months (range: 12-66 months) and recorded incident cardiovascular events and all-cause mortality. RESULTS:Fifty-seven patients (35 with probable MSA, 14 with PD and 8 with PAF) completed all evaluations. In addition to nOH (average fall 35 ± 21/17 ± 14 mmHg, systolic/diastolic, mean ± SD), 38 patients (67%) had supine hypertension (systolic BP > 140 mmHg). Compared to those without hypertension, patients with hypertension had higher blood urea nitrogen levels (P = 0.005), lower estimated glomerular filtration rate (P = 0.008), higher prevalence of LVH (P = 0.040), and higher WMH volume (P = 0.019). Longitudinal follow-up of patients for over 2 years (27.1 ± 14.5 months) showed that supine hypertension was independently associated with earlier incidence of cardiovascular events and death (HR = 0.25; P = 0.039). CONCLUSIONS:Supine hypertension in patients with nOH was associated with an increased risk for target organ damage, cardiovascular events, and premature death. Defining management strategies and safe blood pressure ranges in patients with nOH remains an important research question.

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+ aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~ 1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation)

BACKGROUND:H MRS with the ability to separate tissue-type partial volume contribution(s). PURPOSE/OBJECTIVE:H MRSI voxel averaging is sensitive to regional WM metabolic abnormalities. STUDY TYPE/METHODS:Retrospective cross-sectional cohort study. POPULATION/METHODS:Twenty-seven subjects: 15 symptomatic mTBI patients, 12 matched controls. FIELD STRENGTH/SEQUENCE/UNASSIGNED:. ASSESSMENT/RESULTS:N-acetyl-aspartate (NAA), creatine, choline, and myo-inositol concentrations estimated in predominantly WM regions: body, genu, and splenium of the corpus callosum, corona radiata, frontal, and occipital WM. STATISTICAL TESTS/UNASSIGNED:Analysis of covariance (ANCOVA) to compare patients with controls in terms of regional concentrations. The effect sizes (Cohen's d) of the mean differences were compared across regions and with previously published global data obtained with linear regression of the WM over the entire VOI in the same dataset. RESULTS:Despite patients' global VOI WM NAA being significantly lower than the controls', no regional differences were observed for any metabolite. Regional NAA comparisons, however, were all unidirectional (patients' NAA concentrations < controls') within a narrow range: 0.3 ≤ Cohen's d ≤ 0.6. DATA CONCLUSION/UNASSIGNED:H MRS studies, given that these results are confirmed in other cohorts. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019.

In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MR images. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-word approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex matched healthy controls), and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

BACKGROUND AND PURPOSE/OBJECTIVE:Working memory impairment is one of the most troubling and persistent symptoms after mild traumatic brain injury (MTBI). Here we investigate how working memory deficits relate to detectable WM microstructural injuries to discover robust biomarkers that allow early identification of patients with MTBI at the highest risk of working memory impairment. MATERIALS AND METHODS/METHODS:Multi-shell diffusion MR imaging was performed on a 3T scanner with 5 b-values. Diffusion metrics of fractional anisotropy, diffusivity and kurtosis (mean, radial, axial), and WM tract integrity were calculated. Auditory-verbal working memory was assessed using the Wechsler Adult Intelligence Scale, 4th ed, subtests: 1) Digit Span including Forward, Backward, and Sequencing; and 2) Letter-Number Sequencing. We studied 19 patients with MTBI within 4 weeks of injury and 20 healthy controls. Tract-Based Spatial Statistics and ROI analyses were performed to reveal possible correlations between diffusion metrics and working memory performance, with age and sex as covariates. RESULTS:= .04), mainly present in the right superior longitudinal fasciculus, which was not observed in healthy controls. Patients with MTBI also appeared to lose the normal associations typically seen in fractional anisotropy and axonal water fraction with Letter-Number Sequencing. Tract-Based Spatial Statistics results also support our findings. CONCLUSIONS:Differences between patients with MTBI and healthy controls with regard to the relationship between microstructure measures and working memory performance may relate to known axonal perturbations occurring after injury.

Introduction: Although often characterized as a disease of white matter, gray matter (GM) pathology has been shown to play an important role in multiple sclerosis (MS).
Objective(s): We used diffusion kurtosis imaging (DKI), a clinically feasible extension of diffusion tensor imaging (DTI) to characterize pathology in cortical and subcortical GM regions in MS patients compared to controls and study how selected DKI parameters correlate with disease severity in comparison to traditional volumetric approaches.
Method(s): 36 MS patients and 24 age and gender matched controls were enrolled in the study. MS patients completed a Patient Determined Disease Steps Score (PDDS). All patients received MRI on a 3T MR Scanner (Siemens, Skyra, or Prisma), which included whole brain 3D magnetization-prepared rapid gradientecho (MPRAGE) (1 mm³ isotropic resolution) for extracting volumetrics and monopolar diffusion-weighted echo-planar imaging (EPI) (voxel size = 1.7 x 1.7 x 3 mm³, b=0, 250, 1000, and 2000 s/m² along 84 directions, TE/TR = 100/3500 ms, GRAPPA with acceleration 2, and multiband 2) for deriving diffusion metrics. Volume metrics from automatic segmentation from MPRAGE and diffusion metrics which included mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) were derived for 7 subcortical and 5 cortical GM regions. We determined the partial correlations between PDDS and either GM volume or diffusion parameters covarying for gender and age. We also determined the differences in volume and diffusion metrics between MS patients and controls using ANCOVA with age as the covariate.
Result(s): We observed statistically significant differences in volumes between MS patients and controls for the amygdala, caudate, putamen, nucleus accumbens, cingulate lobe, and subcortical gray volumes with p-values ranging from 0.001 to 0.044. Statistically significant group differences were observed in a majority of the ROI for FA, MD, and MK. Overall, FA was increased, MD was increased, and MK was decreased for most ROI in MS patients compared to controls. There was an increased number of significant partial correlations between PDDS and diffusion metrics compared to PDDS and volume metrics, specifically positive correlations for occipital lobe MD and FA and negative correlations for hippocampal FA.
Conclusion(s): Our results suggest that DKI metrics are sensitive to changes in GM and complimentary to GM volumetrics as an index of GM pathology

Objective: To use quantitative metrics from a large heterogenous population of MS PATHS (Partners Advancing Technology for Health Solutions) patients to derive an integrated view of MS disease course.
Background(s): A commonly used diagram to describe MS disease course shows how various measures change over time. The curves are derived hypothetically, and the best fit patterns, e.g. linear, accelerating, are uncertain. It is also unknown whether the diagrams reflect the current era of disease modifying therapies.
Method(s): In MS PATHS, 2 standardized MRI acquisition sequences (3D FLAIR and 3D T1 on Siemens 3T scanners) were incorporated into routine MS MRI protocols at all participating institutions. A software prototype (MSPie) was developed for automated calculation of brain parenchymal fraction (BPF), total T2 lesion volume (T2LV), and new T2 lesion counts (newT2). The Multiple Sclerosis Performance Test (MSPT) was used to complete neuroperformance tests and questionnaires, including Patient Determined Disease Steps (PDDS) and self-reported relapses. Serum was collected as part of an MS PATHS biomarker sub-study and analyzed by SIMOA kit assay to measure serum neurofilament light (sNfL). Cross-sectional data from patients with MRI metrics were analyzed using linear regression to calculate slopes, and tests for quadratic terms to test linearity, for each measure vs disease duration.
Result(s): 5215 unique patients (mean[sd] age=45.9[11.9]; disease duration=11.9[8.8] years) had MRI metrics. Over nearly 4 decades of MS, BPF showed a linear decrease (slope=-0.16%/year) while PDDS and T2LV showed a linear increase, with annual slopes of 0.076/year and 0.51ml/year, respectively. Linear terms (slopes) were highly significant (p< 10^-15); whereas quadratic terms were weak (p< 0.05). Markers of inflammatory activity, including newT2 and relapses, stayed constant/decreased over the course of MS, with annual slopes of -0.01 (p=0.174) and -0.01 (p< 10^-6), respectively. Log(sNfL) increased linearly (slope= 0.015/year, p< 10^-14).
Conclusion(s): Standardization of MRIs across an international network is feasible, enabling high quality MRI-based metrics and systematic learning from routine patient care. Although limited by the cross-sectional nature of the analyses, these results show strong linearity observed for various measures of disease progression, suggesting that MS neither stabilizes nor accelerates in later stages, unlike some hypothetical diagrams of disease evolution