Per Suppa

Fully Automated Atlas-Based Hippocampal Volumetry for Detection of Alzheimer's Disease in a Memory Clinic Setting

Hippocampal volume is a promising biomarker to enhance the accuracy of the diagnosis of dementia due to Alzheimers disease (AD). However, whereas hippocampal volume is well studied in patient samples from clinical trials, its value in clinical routine patient care is still rather unclear. The aim of the present study, therefore, was to evaluate fully automated atlas-based hippocampal volumetry for detection of AD in the setting of a secondary care expert memory clinic for outpatients. One-hundred consecutive patients with memory complaints were clinically evaluated and categorized into three diagnostic groups: AD, intermediate AD, and non-AD. A software tool based on open source software (Statistical Parametric Mapping SPM8) was employed for fully automated tissue segmentation and stereotactical normalization of high-resolution three-dimensional T1-weighted magnetic resonance images. Predefined standard masks were used for computation of grey matter volume of the left and right hippocampus which then was scaled to the patients total grey matter volume. The right hippocampal volume provided an area under the receiver operating characteristic curve of 84% for detection of AD patients in the whole sample. This indicates that fully automated MR-based hippocampal volumetry fulfills the requirements for a relevant core feasible biomarker for detection of AD in everyday patient care in a secondary care memory clinic for outpatients. The software used in the present study has been made freely available as an SPM8 toolbox. It is robust and fast so that it is easily integrated into routine workflow.

Fully Automated Atlas-Based Hippocampus Volumetry for Clinical Routine: Validation in Subjects with Mild Cognitive Impairment from the ADNI Cohort

Hippocampus volumetry based on magnetic resonance imaging (MRI) has not yet been translated into everyday clinical diagnostic patient care, at least in part due to limited availability of appropriate software tools. In the present study, we evaluate a fully-automated and computationally efficient processing pipeline for atlas based hippocampal volumetry using freely available Statistical Parametric Mapping (SPM) software in 198 amnestic mild cognitive impairment (MCI) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI1). Subjects were grouped into MCI stable and MCI to probable Alzheimers disease (AD) converters according to follow-up diagnoses at 12, 24, and 36 months. Hippocampal grey matter volume (HGMV) was obtained from baseline T1-weighted MRI and then corrected for total intracranial volume and age. Average processing time per subject was less than 4 minutes on a standard PC. The area under the receiver operator characteristic curve of the corrected HGMV for identification of MCI to probable AD converters within 12, 24, and 36 months was 0.78, 0.72, and 0.71, respectively. Thus, hippocampal volume computed with the fully-automated processing pipeline provides similar power for prediction of MCI to probable AD conversion as computationally more expensive methods. The whole processing pipeline has been made freely available as an SPM8 toolbox. It is easily set up and integrated into everyday clinical patient care.

Atlas based brain volumetry: How to distinguish regional volume changes due to biological or physiological effects from inherent noise of the methodology.

Fully-automated regional brain volumetry based on structural magnetic resonance imaging (MRI) plays an important role in quantitative neuroimaging. In clinical trials as well as in clinical routine multiple MRIs of individual patients at different time points need to be assessed longitudinally. Measures of inter- and intrascanner variability are crucial to understand the intrinsic variability of the method and to distinguish volume changes due to biological or physiological effects from inherent noise of the methodology. To measure regional brain volumes an atlas based volumetry (ABV) approach was deployed using a highly elastic registration framework and an anatomical atlas in a well-defined template space. We assessed inter- and intrascanner variability of the method in 51 cognitively normal subjects and 27 Alzheimer dementia (AD) patients from the Alzheimers Disease Neuroimaging Initiative by studying volumetric results of repeated scans for 17 compartments and brain regions. Median percentage volume differences of scan-rescans from the same scanner ranged from 0.24% (whole brain parenchyma in healthy subjects) to 1.73% (occipital lobe white matter in AD), with generally higher differences in AD patients as compared to normal subjects (e.g., 1.01% vs. 0.78% for the hippocampus). Minimum percentage volume differences detectable with an error probability of 5% were in the one-digit percentage range for almost all structures investigated, with most of them being below 5%. Intrascanner variability was independent of magnetic field strength. The median interscanner variability was up to ten times higher than the intrascanner variability.

Performance of Hippocampus Volumetry with FSL-FIRST for Prediction of Alzheimer’s Disease Dementia in at Risk Subjects with Amnestic Mild Cognitive Impairment

MRI-based hippocampus volume, a core feasible biomarker of Alzheimers disease (AD), is not yet widely used in clinical patient care, partly due to lack of validation of software tools for hippocampal volumetry that are compatible with routine workflow. Here, we evaluate fully-automated and computationally efficient hippocampal volumetry with FSL-FIRST for prediction of AD dementia (ADD) in subjects with amnestic mild cognitive impairment (aMCI) from phase 1 of the Alzheimers Disease Neuroimaging Initiative. Receiver operating characteristic analysis of FSL-FIRST hippocampal volume (corrected for head size and age) revealed an area under the curve of 0.79, 0.70, and 0.70 for prediction of aMCI-to-ADD conversion within 12, 24, or 36 months, respectively. Thus, FSL-FIRST provides about the same power for prediction of progression to ADD in aMCI as other volumetry methods.

Combination of Structural MRI and FDG-PET of the Brain Improves Diagnostic Accuracy in Newly Manifested Cognitive Impairment in Geriatric Inpatients.

BACKGROUND The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. OBJECTIVE To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. METHODS Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimers disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. RESULTS Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001). CONCLUSION These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.

Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis.

A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (≥0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.

Fully Automatic MRI-Based Hippocampus Volumetry Using FSL-FIRST: Intra-Scanner Test-Retest Stability, Inter-Field Strength Variability, and Performance as Enrichment Biomarker for Clinical Trials Using Prodromal Target Populations at Risk for Alzheimer’s Disease

BACKGROUND MRI-based hippocampus volume is a core clinical biomarker for identification of Alzheimers disease (AD). OBJECTIVE To assess robustness of automatic hippocampus volumetry with the freely available FSL-FIRST software with respect to short-term repeat and across field strength imaging. FSL-FIRST hippocampus volume (FIRST-HV) was also evaluated as enrichment biomarker for mild cognitive impairment (MCI) trials. METHODS Robustness of FIRST-HV was assessed in 51 healthy controls (HC), 74 MCI subjects, and 28 patients with AD dementia from ADNI1, each with two pairs of back-to-back scans, one at 1.5T one at 3T. Enrichment performance was tested in a second sample of 287 ADNI MCI subjects. RESULTS FSL-FIRST worked properly in all four scans in 147 out of 153 subjects of the first sample (49 HC, 72 MCI, 26 AD). In these subjects, FIRST-HV did not differ between the first and the second scan within an imaging session, neither at 1.5T nor at 3T (p≥0.302). FIRST-HV was on average 0.78% larger at 3T compared to 1.5T (p = 0.012). The variance of the FIRST-HV difference was larger in the inter-field strength setting than in the intra-scanner settings (p < 0.0005). Computer simulations suggested that the additional variability encountered in the inter-field strength scenario does not cause a relevant degradation of FIRST-HVs prognostic performance in MCI. FIRST-HV based enrichment resulted in considerably increased effect size of the 2-years change of cognitive measures. CONCLUSION The impact of intra-scanner test-retest and inter-field strength variability of FIRST-HV on clinical tasks is negligible. In addition, FIRST-HV is useful for enrichment in clinical MCI trials.

ASSOCIATION OF FDG UPTAKE AND RS-FMRI–BASED EIGENVECTOR CENTRALITY IN COGNITIVE IMPAIRMENT

between two sides in PA scores (p1⁄40.52). The 90 percentile cut-off of the age-specific distribution increased from 0 (at age 20-29) to 1 (at age 30-59) and then up to 2 for subjects over 60s (Figure). No significant difference was observed between APOε4 carriers and non-carriers, neither in the whole sample (p1⁄40.23 right; p1⁄40.28 left), nor in the subsets over 60 (p1⁄40.55 right; p1⁄40.61 left). Conclusions:This study provides the first normative values for PA in the general population. According to the literature, our data suggest the need to define age specific thresholds for normality in clinical assessments. References: Koedam E L G E et al. Visual assessment of posterior atrophy development of a MRI rating scale. Eur Radiol. 2011; 21:2618-2625. Galluzzi S et al. The Italian Brain Normative Archive of structural MR scans: norms for medial temporal atrophy and white matter lesions. Aging Clin Exp Res. 2009; 21(4-5):266-276.

Magnetic resonance imaging-based hippocampus volume for prediction of dementia in mild cognitive impairment: Why does the measurement method matter so little?

Magnetic resonance imaging (MRI)-based hippocampus volume (HV) is the best established imaging marker to support the prediction of AD dementia (ADD) in mild cognitive impairment (MCI), although its utility in clinical patient care has not yet been fully demonstrated [1,2]. HV can be scored on an ordinal scale based on visual inspection of MRI [3], or it can be estimated quantitatively by manual or automatic delineation of the hippocampus in MRI. While visual scoring tends to perform worse in MCI-to-ADD prediction, manual delineation and automatic methods show very similar performance [4]. Furthermore, there is hardly any difference among the numerous automatic methods with respect to predictive power in MCI. In the head-to-head comparison of four HV measurement methods in MCI subjects of the Alzheimer’s Disease Neuroimaging Initiative by the EuropeanMedicines Agency, the area (AUC) under the receiver operating characteristic (ROC) curve for 2-year prediction of ADD ranged between 0.7290 and 0.7565, and among three of the four methods, the AUC ranged between 0.7516 and 0.7565 [5]. This appears surprising at first sight given that the quantitative methods differ strongly in accuracy and precision with respect to the anatomical delineation of the hippocampus. Time spent by the rater and computer processing time also differ strongly [4]. Here, we aim to provide a simple mathematical explanation of the stability of the performance of MRI-based HV in MCI with respect to the HV measurement method. Let us assume that the true, error-free HV follows a Gaussian distribution in both MCI stable subjects and in MCI-to-ADD progressors:

A NOVEL MARKER FOR THE CHARACTERIZATION OF THE PATTERN OF WHITE MATTER MRI HYPERINTENSITIES: THE WEIGHTED CONFLUENCY SUM SCORE

P3-223 A NOVEL MARKER FOR THE CHARACTERIZATION OF THE PATTERN OF WHITE MATTER MRI HYPERINTENSITIES: THE WEIGHTED CONFLUENCY SUM SCORE Catharina Lange, Per Suppa, Anja Maeurer, Kerstin Ritter, Uwe Pietrzyk, Elisabeth Steinhagen-Thiessen, Jochen B. Fiebach, Lothar Spies, Ralph Buchert, Charite Universitaetsmedizin Berlin, Berlin, Germany; 2 Jung Diagnostics GmbH, Hamburg, Germany; Evangelisches Geriatriezentrum Berlin, Berlin, Germany; Forschungszentrum Juelich, Juelich, Germany; University of Wuppertal, Wuppertal, Germany. Contact e-mail: catharina.lange@charite.de