Quadratic relationship between systolic blood pressure and white matter lesions in individuals with hypertension
Woldstad, Christopher; Rusinek, Henry; Sweeney, Elizabeth; Butler, Tracy; Li, Yi; Tanzi, Emily; Mardy, Christopher; Harvey, Patrick; de Leon, Mony J; Glodzik, Lidia
BACKGROUND:There is a well documented relationship between cardiovascular risk factors and the development of brain injury, which can lead to cognitive dysfunction. Hypertension (HTN) is a condition increasing the risk of silent and symptomatic ischemic brain lesions. Although benefits of hypertension treatment are indisputable, the target blood pressure value where the possibility of tissue damage is most reduced remains under debate. METHOD/METHODS:Our group performed a cross-sectional (nâ€Š=â€Š376) and longitudinal (nâ€Š=â€Š188) study of individuals without dementia or stroke (60% women nâ€Š=â€Š228, age 68.5â€ŠÂ±â€Š7.4â€Šyears; men nâ€Š=â€Š148, age 70.7â€ŠÂ±â€Š6.9â€Šyears). Participants were split into hypertensive (nâ€Š=â€Š169) and normotensive (nâ€Š=â€Š207) groups. MR images were obtained on a 3T system. Linear modeling was performed in hypertensive and normotensive cohorts to investigate the relationship between systolic (SBP) and diastolic (DBP) blood pressure, white matter lesion (WML), and brain volumes. RESULTS:Participants in the hypertensive cohort showed a quadratic relationship between SBP and WML, with the lowest amounts of WML being measured in participants with readings at approximately 124â€ŠmmHg. Additionally, the hypertensive cohort also exhibited a quadratic relationship between DBP and mean hippocampal volume; participants with readings at approximately 77â€ŠmmHg showing the largest volumes. Longitudinally, all groups experienced WML growth, despite different BP trajectories, further suggesting that WML expansion may occur despite or because of BP reduction in individuals with compromised vascular system. CONCLUSION/CONCLUSIONS:Overall, our study suggests that in the hypertensive group there is a valley of mid-range blood pressures displaying less pathology in the brain.
Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment
Huang, Yu; Moreno, Raquel; Malani, Rachna; Meng, Alicia; Swinburne, Nathaniel; Holodny, Andrei I; Choi, Ye; Rusinek, Henry; Golomb, James B; George, Ajax; Parra, Lucas C; Young, Robert J
In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, pâ€‰>â€‰0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.
Cortical Myelin Profile Variations in Healthy Aging Brain: A T1w/T2w Ratio Study
Sui, Yu-Veronica; Masurkar, Arjun V; Rusinek, Henry; Reisberg, Barry; Lazar, Mariana
Demyelination is observed in both healthy aging and age-related neurodegenerative disorders. While the significance of myelin within the cortex is well acknowledged, studies focused on intracortical demyelination and depth-specific structural alterations in normal aging are lacking. Using the recently available Human Connectome Project Aging dataset, we investigated intracortical myelin in a normal aging population using the T1w/T2w ratio. To capture the fine changes across cortical depths, we employed a surface-based approach by constructing cortical profiles traveling perpendicularly through the cortical ribbon and sampling T1w/T2w values. The curvatures of T1w/T2w cortical profiles may be influenced by differences in local myeloarchitecture and other tissue properties, which are known to vary across cortical regions. To quantify the shape of these profiles, we parametrized the level of curvature using a nonlinearity index (NLI) that measures the deviation of the profile from a straight line. We showed that NLI exhibited a steep decline in aging that was independent of local cortical thinning. Further examination of the profiles revealed that lower T1w/T2w near the gray-white matter boundary and superficial cortical depths were major contributors to the apparent NLI variations with age. These findings suggest that demyelination and changes in other T1w/T2w related tissue properties in normal aging may be depth-specific and highlight the potential of NLI as a unique marker of microstructural alterations within the cerebral cortex.
Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs
Liu, Sheng; Masurkar, Arjun V; Rusinek, Henry; Chen, Jingyun; Zhang, Ben; Zhu, Weicheng; Fernandez-Granda, Carlos; Razavian, Narges
Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.
Radiomics-Based Image Phenotyping of Kidney Apparent Diffusion Coefficient Maps: Preliminary Feasibility & Efficacy
Li, Lu-Ping; Leidner, Alexander S; Wilt, Emily; Mikheev, Artem; Rusinek, Henry; Sprague, Stuart M; Kohn, Orly F; Srivastava, Anand; Prasad, Pottumarthi V
Given the central role of interstitial fibrosis in disease progression in chronic kidney disease (CKD), a role for diffusion-weighted MRI has been pursued. We evaluated the feasibility and preliminary efficacy of using radiomic features to phenotype apparent diffusion coefficient (ADC) maps and hence to the clinical classification(s) of the participants. The study involved 40 individuals (10 healthy and 30 with CKD (eGFR < 60 mL/min/1.73 m2)). Machine learning methods, such as hierarchical clustering and logistic regression, were used. Clustering resulted in the identification of two clusters, one including all individuals with CKD (n = 17), while the second one included all the healthy volunteers (n = 10) and the remaining individuals with CKD (n = 13), resulting in 100% specificity. Logistic regression identified five radiomic features to classify participants as with CKD vs. healthy volunteers, with a sensitivity and specificity of 93% and 70%, respectively, and an AUC of 0.95. Similarly, four radiomic features were able to classify participants as rapid vs. non-rapid CKD progressors among the 30 individuals with CKD, with a sensitivity and specificity of 71% and 43%, respectively, and an AUC of 0.75. These promising preliminary data should support future studies with larger numbers of participants with varied disease severity and etiologies to improve performance.
Decreased CSF clearance and increased brain amyloid in Alzheimer's disease
Li, Yi; Rusinek, Henry; Butler, Tracy; Glodzik, Lidia; Pirraglia, Elizabeth; Babich, John; Mozley, P David; Nehmeh, Sadek; Pahlajani, Silky; Wang, Xiuyuan; Tanzi, Emily B; Zhou, Liangdong; Strauss, Sara; Carare, Roxana O; Theise, Neil; Okamura, Nobuyuki; de Leon, Mony J
BACKGROUND:F-THK5117, we previously reported that the ventricular CSF clearance of the PET tracer was reduced in AD and associated with elevated brain AÎ² levels. METHODS:C-PiB to estimate CSF clearance calculated from early dynamic PET frames in 9 normal controls and 15 AD participants. RESULTS:F-THK5351) and brain AÎ² load (râ€‰=â€‰â€‰-â€‰0.64, nâ€‰=â€‰24, pâ€‰<â€‰0.01). With a larger sample size, we extended our observations to show that reduced CSF clearance is associated with reductions in cortical thickness and cognitive performance. CONCLUSIONS:Overall, the findings support the hypothesis that failed CSF clearance is a feature of AD that is related to AÎ² deposition and to the pathology of AD. Longitudinal studies are needed to determine whether failed CSF clearance is a predictor of progressive amyloidosis or its consequence.
Quantitative imaging features predict spinal tap response in normal pressure hydrocephalus
Lotan, Eyal; Damadian, Brianna E; Rusinek, Henry; Griffin, Megan; Ades-Aron, Benjamin; Lu, Ning; Golomb, James; George, Ajax E
PURPOSE/OBJECTIVE:Gait improvement following high-volume lumbar puncture (HVLP) and continuous lumbar drain (cLD) is widely used to predict shunt response in patients with suspected normal pressure hydrocephalus (NPH). Here, we investigate differences in MRI volumetric and traditional measures between HVLP/cLD responders and non-responders to identify imaging features that may help predict HVLP/cLD response. METHODS:Eighty-two patients with suspected NPH were studied retrospectively. Gait testing was performed before and immediately/24Â h/72Â h after HVLP/cLD. A positive response was defined as improvement in gait post-procedure. Thirty-six responders (26 men; mean age 79.3â€‰Â±â€‰6.3) and 46 non-responders (25 men; mean age 77.2â€‰Â±â€‰6.1) underwent pre-procedure brain MRI including a 3D T1-weighted sequence. Subcortical regional volumes were segmented using FreeSurfer. After normalizing for total intracranial volume, two-way type III ANCOVA test and chi-square test were used to characterize statistical group differences. Evans' index, callosal angle (CA), and disproportionately enlarged subarachnoid space hydrocephalus were assessed. Multivariable logistic regression models were tested using Akaike information criterion to determine which combination of metrics most accurately predicts HVLP/cLD response. RESULTS:Responders and non-responders demonstrated no differences in total ventricular and white/gray matter volumes. CA (men only) and third and fourth ventricular volumes were smaller; and hippocampal volume was larger in responders (pâ€‰<â€‰0.05). Temporal horns volume correlated with degree of improvement in gait velocity in responders (pâ€‰=â€‰0.0006). The regression model was 76.8% accurate for HVLP/cLD response. CONCLUSION/CONCLUSIONS:CA and third and fourth ventricular volumes and hippocampal volume may serve as potentially useful imaging features that may help predict spinal tap response and hence potentially shunt response.
Repeatability, robustness, and reproducibility of texture features on 3Â Tesla liver MRI
Prabhu, Vinay; Gillingham, Nicolas; Babb, James S; Mali, Rahul D; Rusinek, Henry; Bruno, Mary T; Chandarana, Hersh
OBJECTIVE:Texture features are proposed for classification and prognostication, with lacking information about variability. We assessed 3Â T liver MRI feature variability. METHODS:Five volunteers underwent standard 3Â T MRI, and repeated with identical and altered parameters. Two readers placed regions of interest using 3DSlicer. Repeatability (between standard and repeat scan), robustness (between standard and parameter changed scan), and reproducibility (two reader variation) were computed using coefficient of variation (CV). RESULTS:67%, 49%, and 61% of features had good-to-excellent (CVÂ â‰¤Â 10%) repeatability on ADC, T1, and T2, respectively, least frequently for first order (19-35%). 22%, 19%, and 21% of features had good-to-excellent robustness on ADC, T1, and T2, respectively. 52%, 35%, and 25% of feature measurements had good-to-excellent inter-reader reproducibility on ADC, T1, and T2, respectively, with highest good-to-excellent reproducibility for first order features on ADC/T1. CONCLUSION/CONCLUSIONS:We demonstrated large variations in texture features on 3Â T liver MRI. Further study should evaluate methods to reduce variability.
3D finite-element brain modeling of lateral ventricular wall loading to rationalize periventricular white matter hyperintensity locations
CaÃ§oilo, Andreia; Rusinek, Henry; Weickenmeier, Johannes
Aging-related periventricular white matter hyperintensities (pvWMHs) are a common observation in medical images of the aging brain. The underlying tissue damage is part of the complex pathophysiology associated with age-related microstructural changes and cognitive decline. PvWMH formation is linked to blood"“brain barrier dysfunction from cerebral small vessel disease as well as the accumulation of cerebrospinal fluid in periventricular tissue due to progressive denudation of the ventricular wall. In need of a unifying theory for pvWMH etiology, image-based finite-element modeling is used to demonstrate that ventricular expansion from age-related cerebral atrophy and hemodynamic loading leads to maximum mechanical loading of the ventricular wall in the same locations that show pvWMHs. Ventricular inflation, induced via pressurization of the ventricular wall, creates significant ventricular wall stretch and stress on the ependymal cells lining the wall, that are linked to cerebrospinal fluid leaking from the lateral ventricles into periventricular white matter tissue. Eight anatomically accurate 3D brain models of cognitively healthy subjects with a wide range of ventricular shapes are created. For all models, our simulations show that mechanomarkers of mechanical wall loading are consistently highest in pvWMHs locations (p < 0.05). Maximum principal strain, the ependymal cell thinning ratio, and wall curvature are on average 14%, 8%, and 24% higher in pvWMH regions compared to the remaining ventricular wall, respectively. Computational modeling provides a powerful framework to systematically study pvWMH formation and growth with the goal to develop pharmacological interventions in the future.
Higher body mass index is associated with worse hippocampal vasoreactivity to carbon dioxide
Glodzik, Lidia; Rusinek, Henry; Butler, Tracy; Li, Yi; Storey, Pippa; Sweeney, Elizabeth; Osorio, Ricardo S; Biskaduros, Adrienne; Tanzi, Emily; Harvey, Patrick; Woldstad, Christopher; Maloney, Thomas; de Leon, Mony J
Background and objectives/UNASSIGNED:) in a group of cognitively normal middle-aged and older adults. Methods/UNASSIGNED:Our study was a retrospective analysis of prospectively collected data. Subjects were enrolled for studies assessing the role of hippocampal hemodynamics as a biomarker for AD among cognitively healthy elderly individuals (age > 50). Participants without cognitive impairment, stroke, and active substance abuse were recruited between January 2008 and November 2017 at the NYU Grossman School of Medicine, former Center for Brain Health. All subjects underwent medical, psychiatric, and neurological assessments, blood tests, and MRI examinations. To estimate CVR, we increased their carbon dioxide levels using a rebreathing protocol. Relationships between BMI and brain measures were tested using linear regression. Results/UNASSIGNED:in women (Î² = -0.20, unstandardized B = -0.08, 95% CI -0.13, -0.02). Discussion/UNASSIGNED:These findings lend support to the notion that obesity is a risk factor for hippocampal hemodynamic impairment and suggest targeting obesity as an important prevention strategy. Prospective studies assessing the effects of weight loss on brain hemodynamic measures and inflammation are warranted.