Simon Fristed Eskildsen

Active surface approach for extraction of the human cerebral cortex from MRI

Segmentation of the human cerebral cortex from MRI has been subject of much attention during the last decade. Methods based on active surfaces for representing and extracting the cortical boundaries have shown promising results. We present an active surface method, that extracts the inner and outer cortical boundaries using a combination of different vector fields and a local weighting method based on the intrinsic properties of the deforming surface. Our active surface model deforms polygonal meshes to fit the boundaries of the cerebral cortex using a force balancing scheme. As a result of the local weighting strategy and a self-intersection constraint, the method is capable of modelling tight sulci where the image edge is missing or obscured. The performance of the method is evaluated using both real and simulated MRI data.

BEaST: brain extraction based on nonlocal segmentation technique.

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimers Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimers disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimers Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.

Predicting Alzheimers disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as regions of interest within these patterns. The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study. First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing and validation schemes to ensure that datasets used to build the predictive models are not used in testing and validation.

Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder

Järnum H, Eskildsen SF, Steffensen EG, Lundbye‐Christensen S, Simonsen CW, Thomsen IS, Fründ E‐T, Théberge J, Larsson E‐M. Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder.

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease.

In this paper, we propose an innovative approach to robustly and accurately detect Alzheimers disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%.

Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease.

Detection of Alzheimers disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive — pMCI vs. stable — sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.

Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimers disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimers Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.

Reduced cortical thickness of brain areas involved in pain processing in patients with chronic pancreatitis.

BACKGROUND & AIMS Patients with painful chronic pancreatitis (CP) might have abnormal brain function. We assessed cortical thickness in brain areas involved in visceral pain processing. METHODS We analyzed brain morphologies of 19 patients with painful CP and compared them with 15 healthy individuals (controls) by using a 3T magnetic resonance scanner. By using an automated method with surface-based cortical segmentation, we assessed cortical thickness of the primary (SI) and secondary (SII) somatosensory cortex; prefrontal cortex (PFC); frontal cortex (FC); anterior (ACC), mid (MCC), and posterior (PCC) cingulate cortex; and insula. The occipital middle sulcus was used as a control area. The pain score was determined on the basis of the average daily amount of pain during 1 week. RESULTS Compared with controls, patients with CP had reduced overall cortical thickness (P = .0012), without effects of modification for diabetes, alcoholic etiologies, or opioid treatment (all P values >.05). In patients with CP, the cortical thickness was decreased in SII (P = .002, compared with controls), PFC (P = .046), FC (P = .0003), MCC (P = .001), and insula (P = .002). There were no differences in cortical thickness between CP patients and controls in the control area (P = .20), SI (P = .06), ACC (P = .95), or PCC (P = .42). Cortical thickness in the affected areas correlated with pain score (r = 0.47, P = .003). CONCLUSIONS In patients with CP, brain areas involved in pain processing have reduced cortical thickness. As a result of long-term, ongoing pain input to the neuromatrix, cortical thickness might serve as a measure for overall pain system dysfunction, as observed in other diseases characterized by chronic pain.

Cortical thickness alterations in social anxiety disorder

Social anxiety disorder (SAD) has been associated with aberrant processing of socio-emotional stimuli and failure to adaptively regulate emotion, corroborated by functional neuroimaging studies. However, only a few studies of structural brain abnormalities in SAD have been reported, and among these only one investigated cortical thickness. In the present study we used magnetic resonance imaging (MRI) in conjunction with an automated method to measure cortical thickness in patients with SAD (n=14) and healthy controls (n=12). Results showed significantly increased thickness of the left inferior temporal cortex in SAD patients relative to controls. Within the patient group, a negative association was found between social anxiety symptom severity and thickness of the right rostral anterior cingulate cortex. The observed alterations in brain structure may help explain previous findings of dysfunctional regulation and processing of emotion in SAD.