Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Background Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist. Purpose To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype. Study Type Prospective. Subjects In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments. Field Strength/Sequence 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences. Assessment Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently. Statistical Testing Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis. Results Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84). Data Conclusion Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019.

Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

Bénédicte Marie Maréchal

Background and Purpose- Poststroke fatigue affects a large proportion of stroke survivors and is associated with a poor quality of life. In a recent trial, modafinil was shown to be an effective agent in reducing poststroke fatigue; however, not all patients reported a significant decrease in fatigue with therapy. We sought to investigate clinical and radiological predictors of fatigue reduction with modafinil therapy in a stroke survivor cohort. Methods- Twenty-six participants with severe fatigue (multidimensional fatigue inventory-20 >= 60) underwent magnetic resonance imaging at baseline and during the last week of a 6-week treatment period of 200 mg modafinil taken daily. Resting-state functional magnetic resonance imaging and high-resolution structural imaging data were obtained, and functional connectivity and regional brain volumes within the fronto-striato-thalamic network were obtained. Linear regression analysis was used to identify predictors of modafinil-induced fatigue reduction. Results- Multiple regression analysis showed that baseline multidimensional fatigue inventory-20 score (beta=0.576, P=0.006) and functional connectivity between the dorsolateral prefrontal cortex and the caudate nucleus (beta=-0.424, P=0.008) were significant predictors of modafinil-associated decreases in poststroke fatigue (adjusted r(2)=0.52, area under the receiver operator characteristic curve=0.939). Conclusions- Fronto-striato-thalamic functional connectivity predicted modafinil response for poststroke fatigue. Fatigue in other neurological disease has been attributed to altered function of the fronto-striato-thalamic network and may indicate that poststroke fatigue has a similar mechanism to other neurological injury related fatigue. Self-reported fatigue in patients with normal fronto-striato-thalamic functional connectivity may have a different mechanism and require alternate therapeutic approache

LIPPINCOTT WILLIAMS & WILKINS2019

Toward Reliable MR-Based Brain Structure Morphometry

Bénédicte Marie Maréchal

Dementing neurodegenerative disorders have more and more prominent public health implications with the most frequent cause being the currently non reversible Alzheimer's Disease (AD). However, over the past 15 years, an abundant literature has demonstrated the particular value of structural MRI from which brain morphological changes can be detected using automated volumetry and morphometry. These techniques offer promise to diagnose AD in early potentially-reversible stages as regional atrophy is currently "the" structural hallmark. Nevertheless, an essential question arises: to what extent are these quantitative measures dependent upon image quality? MRI quality can be mainly affected by machine-specific or patient-related artifacts, with motion being the most significant source of degradations. As we are currently not aware of any technique that reliably captures and corrects patient motion particularly for long high-resolution anatomical scans, rigorous quality assessment is hence of great importance to reliably derive qualitative or quantitative diagnostic information and consists in the first challenge addressed in this thesis. We developed a fully-automated method to classify the image quality based on a single MR image. Quality measures are derived by analyzing the air background of magnitude image and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring and ghosting. The method has been validated on 1670 multi-slice 2D, 3D T1- T2- PD- weighted 1.5T and 3T head scans acquired on Siemens and GE scanners operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the Alzheimer's Disease Neuroimaging Initiative (ADNI) quality control center (taken as the reference standard). The derived quality indices are independent of the MR system used and agree with the reference standard with sensitivity and specificity higher than 90 %. With acceptable computational performance (less than 10 seconds), we incorporated our algorithm in the Siemens scanner software for routine clinical use. As for many other domains, quality criterion does not exhibit all-or-none properties (i.e., a unique quality cutoff value cannot discriminate high- versus low-quality scans) but is modulated by two main aspects addressed in this thesis: acquisition parameters (what kind of image?) and application (what for?). On the one hand, depending on acquisition parameters, more or less strong signal in the object is generated inducing more or less visible artifacts. Therefore, our quality criterion has to be adjusted accordingly. On the other hand, it appears quite natural that different quality criteria are required for algorithms segmenting global gray matter contrary to small brain structure such as hippocampus. Hence comes the second challenge addressed in this thesis: developing models that allow customizing quality criterion according to the acquisition parameters and the required performance of a target application. Taking up the first part of such challenge, we investigated certain acquisition parameters effects (i.e., echo time, bandwidth, signal suppression, field strength) and proposed an exploratory model with reasonable predictive performance (86.6 % sensitivity 92 % specificity). In order to customize quality cutoff levels according to the required performance of a target application, we developed a framework based on motion simulations. The idea is to progressively and realistically corrupt an image with motion and analyze the influence of such degradations (i.e., measured by means of our quality assessment technique) on the accuracy of a brain morphometry algorithm. To be faithful to the challenging research context of early diagnosis of AD, we targeted the automated morphometry of gray matter (GM) tissue (i.e., important imaging-based marker of pathological atrophy). Our framework reveals that 2 % error on the global volume of GM can be induced by a 1 mm patient motion halfway through the acquisition (critical low-frequency sampling of rectilinear k-space). However, this small movement is reliably caught by our quality index whose variation can be further characterized as an exponential function of the error. This model thus allows determining whether an image is of sufficient quality to warrant further quantitative analysis. In conclusion, this thesis underscores the importance of a rigorous quality assessment of MR images to support high-quality outputs of quantitative morphometry tools, ultimately increasing probability for higher diagnostic accuracy. Overall, we envision that such quality assessment could greatly improve clinical workflow through its ability to rule-out the need for a repeat scan while the patient is still in the magnet bore. Just as importantly, it would be an important step forward for incorporating morphometry analysis into regular brain-imaging protocols and thus, further exploit the clinical potential of MRI.

EPFL2011

Prediction of Survival and Risk Assessment Using Joint Analysis of Microarray Gene Expression Data

Haleh Yasrebi

Gene expression profiles have been widely used in molecular classification, diagnosis and prediction, particularly in the area of oncology where accurate and early diagnosis is needed for appropriate treatment. Avoiding under-/over-treatment when it is not necessary can extend a patient's survival and prevent disease recurrence. These high-throughput assay technologies have generated terabytes of data exploited extensively to provide insights on cancer biology and the underlying mechanism of disease progression. The ultimate goal is to identify possibly tailored treatment and therapy for personalized medicine. Analysis of microarray data is constrained by the following characteristics: (i) noisy due to missing or erroneous values; (ii) high dimensional due to a large number of genes versus a few number of samples in which their expression levels are measured; (iii) costly due to expensive microarray experiments. Abundant microarray gene expression data should be processed by appropriate computational and statistical learning methodologies such as machine learning techniques. These methods are robust to noisy data and have a great capacity to analyze high dimensional data. Their computational power is nevertheless limited to sample size based on which these methods are built. These algorithms have been widely applied to microarray gene expression data to identify a set of genes known as a gene signature whose expressions are highly correlated to a target value or outcome such as disease status, tumor subtype, a patient's survival time, risk of mortality or cancer relapse. Prediction of survival time and a patient's risk which is unknown at diagnosis presents a more challenging task for machine learning methods than tumor subtype or disease classification, which is already established by oncologists. The properties of microarray data cited above, the limitation of the number of samples in cancer patients and dependency of the machine learning methods' performance on sample size justify joint analysis of microarray data to increase the number of samples. We applied joint analysis methods to breast and lung cancer data sets to improve survival prediction and risk assessment. In overall, no significant improvement or deterioration of the performance accuracy was obtained with joint analysis. However, increasing sample size helped to identify robust or stable gene signatures predictive of survival time and risk assessment. Our achievements and learned-lessons from joint analysis of microarray gene expression data can be used as a guideline for future research studies in classification and prediction.

EPFL2010

Prediction of Survival and Risk Assessment Using Joint Analysis of Microarray Gene Expression Data

Haleh Yasrebi

EPFL2010

Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

Bénédicte Marie Maréchal

LIPPINCOTT WILLIAMS & WILKINS2019

Toward Reliable MR-Based Brain Structure Morphometry

Bénédicte Marie Maréchal

EPFL2011