Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network

OBJECTIVES

Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM).

MATERIALS AND METHODS

The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods.

RESULTS

The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM.

CONCLUSION

CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM.

CLINICAL RELEVANCE STATEMENT

A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases.

KEY POINTS

• The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry.

Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry

PURPOSE

Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal ("NDB cleaning") and to test its impact on the performance of VBM for detection of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD).

METHODS

T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD.

RESULTS

Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36-0.58] to 0.61 [0.49-0.71]).

CONCLUSION

NDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings.

Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors

Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested through segmentation maps. Our proposal incorporates a cascaded multi-path U-Net optimised with a multi-objective loss which allows its paths to generate different brain regions accurately. We provided our model with baseline scans and real follow-up segmentation maps from two longitudinal datasets, ADNI and OASIS, and observed that our framework could produce synthetic follow-up scans that matched the real ones (Total scans= 584; Median absolute error: 0.03 ± 0.02; Structural similarity index: 0.98 ± 0.02; Dice similarity coefficient: 0.95 ± 0.02; Percentage of brain volume change: 0.24 ± 0.16; Jacobian integration: 1.13 ± 0.05). Compared to two relevant works generating brain lesions using U-Nets and conditional generative adversarial networks (CGAN), our proposal outperformed them significantly in most cases (p < 0.01), except in the delineation of brain edges where the CGAN took the lead (Jacobian integration: Ours - 1.13 ± 0.05 vs CGAN - 1.00 ± 0.02; p < 0.01). We examined whether changes induced with our framework were detected by FAST, SPM, SIENA, SIENAX, and the Jacobian integration method. We observed that induced and detected changes were highly correlated (Adj. R² > 0.86). Our preliminary results on harmonised datasets showed the potential of our framework to be applied to various data collections without further adjustment.

Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy

Objectives

The brain atrophy commonly occurs in elderly and in some childhood conditions making the techniques for quantifying brain volume needful. Since the automated quantitative methods of brain volume assessment have limited availability in developing countries, it was the purpose of this study to design and test an alternative formula that is applicable to all healthcare levels.

Methods

The multi-linear diagonal brain fraction formula (DBF) was designed from dimensions of brain relative to skull and ventricles. To test a developed formula, a total of 347 subjects aged between 0 and 18 years who had brain CT scans performed recruited and subjected to a systematic measurement of their brains in a diagonal brain fashion.

Results

Out of 347 patients evaluated, 62 subjects (17.8 %) were found to be cases of brain atrophy. The three radiological measurements which included sulcal width (SW), ventricular width (VW) and Evans Index (EI) were concurrently performed. SW and VW showed good age correlation. Similar tests were extended to diagonal brain fraction (DBF) and skull vertical horizontal ratio (VHR) in which DBF showed significant age correlation.

Conclusions

The DBF formula shows significant ability of differentiating changes of brain volume suggesting that it can be utilized as an alternative brain fraction quantification method bearing technical simplicity in assessing gross brain volume.

Advances in knowledge

The designed formula is unique in that it captures even the possible asymmetrical volume loss of brain through diagonal lines. The proposed scores being in term of ratios give four grades of brain atrophy.

Brain volume loss in individuals over time: Source of variance and limits of detectability

BACKGROUND

Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability.

GOAL

To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals.

MATERIAL AND METHODS

Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 ​h for 12 ​h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 ​Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference.

RESULTS

The RMSE of PBVC over 12 ​h, for both scanners combined, ("Hours", 0.15%), was similar to the day-to-day variation over 1 week ("Days", 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p ​< ​0.01).

CONCLUSION

Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6-0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.

Assessing Biological and Methodological Aspects of Brain Volume Loss in Multiple Sclerosis

Importance

Before using brain volume loss (BVL) as a marker of therapeutic response in multiple sclerosis (MS), certain biological and methodological issues must be clarified.

Objectives

To assess the dynamics of BVL as MS progresses and to evaluate the repeatability and exchangeability of BVL estimates with Jacobian Integration (JI) and Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) (specifically, the Structural Image Evaluation, Using Normalisation, of Atrophy-Cross-Sectional [SIENA-X] tool or FMRIB's Integrated Registration and Segmentation Tool [FIRST]).

Design, Setting, and Participants

A cohort of patients who had either clinically isolated syndrome or MS was enrolled from February 2011 through October 2015. All underwent a series of annual magnetic resonance imaging (MRI) scans. Images from 2 cohorts of healthy volunteers were used to evaluate short-term repeatability of the MRI measurements (n = 34) and annual BVL (n = 20). Data analysis occurred from January to May 2017.

Main Outcomes and Measures

The goodness of fit of different models to the dynamics of BVL throughout the MS disease course was assessed. The short-term test-retest error was used as a measure of JI and FSL repeatability. The correlations (R2) of the changes quantified in the brain using JI and FSL, together with the accuracy of the annual BVL cutoffs to discriminate patients with MS from healthy volunteers, were used to measure compatibility of imaging methods.

Results

A total of 140 patients with clinically isolated syndrome or MS were enrolled, including 95 women (67.9%); the group had a median (interquartile range) age of 40.7 (33.6-48.1) years. Patients underwent 4 MRI scans with a median (interquartile range) interscan period of 364 (351-379) days. The 34 healthy volunteers (of whom 18 [53%] were women; median [IQR] age, 33.5 [26.2-42.5] years) and 20 healthy volunteers (of whom 10 [50%] were women; median [IQR] age, 33.0 [28.7-39.2] years) underwent 2 MRI scans within a median (IQR) of 24.5 (0.0-74.5) days and 384.5 (366.3-407.8) days for the short-term and long-term MRI follow-up, respectively. The BVL rates were higher in the first 5 years after MS onset (R2 = 0.65 for whole-brain volume change and R2 = 0.52 for gray matter volume change) with a direct association with steroids (β = 0.280; P = .02) and an inverse association with age at MS onset, particularly in the first 5 years (β = 0.015; P = .047). The reproducibility of FSL (SIENA) and JI was similar for whole-brain volume loss, while JI gave more precise, less biased estimates for specific brain regions than FSL (SIENA-X and FIRST). The correlation between whole-brain volume loss using JI and FSL was high (R2 = 0.92), but the same correlations were poor for specific brain regions. The area under curve of the whole-brain volume change to discriminate between patients with MS and healthy volunteers was similar, although the thresholds and accuracy index were distinct for JI and FSL.

Conclusions and Relevance

The proposed BVL threshold of less than 0.4% per year as a marker of therapeutic efficiency should be reconsidered because of the different dynamics of BVL as MS progresses and because of the limited reproducibility and variability of estimates using different imaging methods.

Longitudinal analysis of brain atrophy in Alzheimer's disease and frontotemporal dementia

Objectives

Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are among the leading causes of early-onset dementia. This study aimed to assess the rate of whole brain atrophy by comparing bvFTD and AD.

Methods

Two patients (one man with AD, and one woman with bvFTD) had neuropsychological and neuroimaging assessment by using automated techniques for cross-sectional and longitudinal atrophy measurements.

Results

In the patient with AD, magnetic resonance imaging (MRI) showed decreased bilateral hippocampal and mesial-temporal volume. However, conventional images showed no difference between baseline (T0) and after 1 year (T1). In the patient with bvFTD, MRI showed bilateral frontotemporal lobe atrophy and a moderate increase in atrophy between T0 and T1, particularly in the temporal lobes. A cross-sectional cerebral volume examination showed a considerable reduction in brain volume in the patient with bvFDT and a moderate reduction in the patient with AD. A longitudinal cerebral volume examination showed a lower percentage brain volume change in the patient with bvFTS compared with the patient with AD.

Conclusions

Our results suggest that bvFTD has more neurodegenerative progression. MRI findings should be considered as a reliable marker of disease progression in the brain. Our findings offer potential for monitoring treatment outcomes.

The Role of Nutrition in Cognitive Function and Brain Ageing in the Elderly

PURPOSE OF REVIEW

The purposes of this review were to examine literature published over the last 5 years and to evaluate the role of nutrition in cognitive function and brain ageing, focussing on the Mediterranean diet (MeDi), Dietary Approaches to Stop Hypertension (DASH), and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets.

RECENT FINDINGS

Results suggest that higher adherence to a healthy dietary pattern is associated with preservation of brain structure and function as well as slower cognitive decline, with the MIND diet substantially slowing cognitive decline, over and above the MeDi and DASH diets. Whilst results to-date suggest adherence to a healthy diet, such as the MeDi, DASH, or MIND, is an important modifiable risk factor in the quest to develop strategies aimed at increasing likelihood of healthy brain ageing, further work is required to develop dietary guidelines with the greatest potential benefit for public health; a research topic of increasing importance as the world's population ages.

Early imaging predictors of longer term multiple sclerosis risk and severity in acute optic neuritis

Background

Biomarkers are urgently required for predicting the likely progression of multiple sclerosis (MS) at the earliest stages of the disease to aid in personalised therapy.

Objective

We aimed to examine early brain volumetric and microstructural changes and retinal nerve fibre layer thinning as predictors of longer term MS severity in patients with clinically isolated syndromes (CIS).

Methods

Lesion metrics, brain and regional atrophy, diffusion fractional anisotropy and retinal nerve fibre layer thickness were prospectively assessed in 36 patients with CIS over the first 12 months after presentation and compared with clinical outcomes at longer term follow-up [median (IQR) = 8.5 (7.8–8.9) years].

Results

In total, 25 (69%) patients converted to MS and had greater baseline lesion volume (p = 0.008) and number (p = 0.03)than CIS patients. Over the initial 12 months, new lesions (p = 0.0001), retinal nerve fibre layer thinning (p = 0.04) and ventricular enlargement (p = 0.03) were greater in MS than CIS patients. In MS patients, final Expanded Disability Status Scale score correlated with retinal nerve fibre layer thinning over the first 12 months (ρ = −0.67, p = 0.001).

Conclusions

Additional to lesion metrics, early measurements of fractional anisotropy and retinal nerve fibre layer thinning are informative about longer term clinical outcomes in CIS.

Novel imaging phantom for accurate and robust measurement of brain atrophy rates using clinical MRI

Brain volume loss, or atrophy, has been proven to be an important characteristic of neurological diseases such as Alzheimer's disease and multiple sclerosis. To use atrophy rate as a reliable clinical biomarker and to increase statistical power in clinical treatment trials, measurement variability needs to be minimized. Among other sources, systematic differences between different MR scanners are suspected to contribute to this variability. In this study we developed and performed initial validation tests of an MR-compatible phantom and analysis software for robust and reliable evaluation of the brain volume loss. The phantom contained three inflatable models of brain structures, i.e. cerebral hemisphere, putamen, and caudate nucleus. Software to reliably quantify volumes form the phantom images was also developed. To validate the method, the phantom was imaged using 3D T1-weighted protocols at three clinical 3T MR scanners from different vendors. Calculated volume change from MRI was compared with the known applied volume change using ICC and mean absolute difference. As assessed by the ICC, the agreement between our developed software and the applied volume change for different structures ranged from 0.999-1 for hemisphere, 0.976-0.998 for putamen, and 0.985-0.999 for caudate nucleus. The mean absolute differences between measured and applied volume change were 109-332 μL for hemisphere, 2.9-11.9 μL for putamen, and 2.2-10.1 μL for caudate nucleus. This method offers a reliable and robust measurement of volume change using MR images and could potentially be used to standardize clinical measurement of atrophy rates.

Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years?

To quantify the "segmentation noise" of several widely used fully automatic methods for measuring longitudinal hippocampal atrophy in Alzheimer's disease and compare the results to the segmentation noise of manual segmentation over both 1 and 3 years. The segmentation noise of 5 longitudinal hippocampal atrophy measurement methods was quantified, including checking its Gaussianity, using 264 subjects from the ADNI1 back-to-back (BTB) data set over both 1 year and 3 year intervals. The segmentation methods were FreeSurfer 5.3.0 both cross sectional and longitudinal, FreeSurfer 6.0.0 longitudinal, MAPS-HBSI and FSL/FIRST 5.0.8. The BTB manual segmentation of 75 ADNI subjects from a previous study provided the manual distributions for comparison. All methods, including the manual segmentation, violated the Gaussianity assumption. Two methods, FreeSurfer 6.0.0 and MAPS-HBSI, had a segmentation noise substantially less than a surrogate for manual segmentation. FreeSurfer 5.3.0 longitudinal was confirmed as a surrogate for manual segmentation. The violation of the Gaussian assumption by the segmentation methods assessed, including manual, suggests results of previous studies that assumed Gaussian statistics without confirmation may need review. Fully automatic FreeSurfer 6.0.0 and MAPS-HBSI both have lower segmentation noise than manual requiring less than two thirds of the subjects to detect the same treatment effect.

Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: A clinical evaluation of diagnostic accuracy

Objectives

To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting.

Methods

In 42 memory clinic patients, we evaluated whether automated quantification of brain tissue volumes, hippocampal volume and white matter lesion volume improves diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), compared to visual interpretation. Reference data were derived from a dementia-free aging population (n = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard.

Results

Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD.

Conclusion

Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.

•

Brain imaging from an individual patient can be compared to population-derived reference data to determine deviation from the population average.

•

Age adjusted reference values of brain volume improve accuracy of dementia diagnosis in a memory clinic, especially for AD.

•

These reference values should be interpreted in conjunction with visual assessment of brain MRI's to achieve this benefit.

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI

In this article, SIENA-XL, a new segmentation-based longitudinal pipeline is introduced, for: (i) increasing the precision of longitudinal volume change estimation for white (WM) and gray (GM) matter separately, compared with cross-sectional segmentation methods such as SIENAX; and (ii) avoiding potential biases in registration-based methods when Jacobians are used, with a smoothing extent larger than spatial scale between tissue-interfaces, which is where atrophy usually occurs. SIENA-XL implements a new brain extraction procedure and a multi-time-point intensity equalization step before performing the final segmentation that also includes separate segmentation of deep GM structures by using FMRIB's Integrated Registration and Segmentation Tool. The detection of GM and WM volume changes with SIENA-XL was evaluated using different healthy control (HC) and multiple sclerosis (MS) MRI datasets and compared with the traditional SIENAX and two Jacobian-based approaches, SPM12 and SIENAX-JI (a version of SIENAX including Jacobian integration - JI). In scan-rescan data from HCs, SIENA-XL showed: (i) a significant decrease in error, of 50-70% when compared with SIENAX; (ii) no significant differences in error when compared with SIENAX-JI and SPM12 in a scan-rescan HC dataset that included repositioning. When tested in a HC dataset with scan-rescan both at baseline and after 1 year of follow-up, SIENA-XL showed: (i) significantly higher precision (P < 0.01) than SIENAX; (ii) no significant differences to SIENAX-JI and SPM12. Finally, in a dataset of 79 MS patients with a 2 years follow-up, SIENA-XL showed a substantial reduction of sample size, by comparison with SIENAX, SIENAX-JI, and SPM12, for detecting treatment effects of 25, 30, and 50%. Hum Brain Mapp 39:1063-1077, 2018. © 2017 Wiley Periodicals, Inc.

Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL-a longitudinal brain volumetry study in healthy adults

Brain volume loss (BVL) has gained increasing interest for monitoring tissue damage in neurodegenerative diseases including multiple sclerosis (MS). In this longitudinal study, 117 healthy participants (age range 37.3-82.6 years) received at least 2 magnetic resonance imaging examinations. BVL (in %) was determined with the Structural Image Evaluation using Normalisation of Atrophy/FMRIB Software Library and annualized. Mean BVL per year was 0.15%, 0.30%, 0.46%, and 0.61% at ages 45, 55, 65, and 75 years, respectively. The corresponding BVL per year values of the age-dependent 95th percentiles were 0.52%, 0.77%, 1.05% and 1.45%. Pathological BVL can be assumed if an individual BVL per year exceeds these thresholds for a given age. The mean BVL per year determined in this longitudinal study was consistent with results from a cross-sectional study that was published recently. The cut-off for a pathological BVL per year at the age of 45 years (0.52%) was consistent with the cut-off suggested previously to distinguish between physiological and pathological BVL in MS patients. Different cut-off values, however, need to be considered when interpreting BVL assessed in cohorts of higher ages.

Reproducibility of Deep Gray Matter Atrophy Rate Measurement in a Large Multicenter Dataset

BACKGROUND AND PURPOSE

Precise in vivo measurement of deep GM volume change is a highly demanded prerequisite for an adequate evaluation of disease progression and new treatments. However, quantitative data on the reproducibility of deep GM structure volumetry are not yet available. In this paper we aim to investigate this reproducibility using a large multicenter dataset.

MATERIALS AND METHODS

We have assessed the reproducibility of 2 automated segmentation software packages (FreeSurfer and the FMRIB Integrated Registration and Segmentation Tool) by quantifying the volume changes of deep GM structures by using back-to-back MR imaging scans from the Alzheimer Disease Neuroimaging Initiative's multicenter dataset. Five hundred sixty-two subjects with scans at baseline and 1 year were included. Reproducibility was investigated in the bilateral caudate nucleus, putamen, amygdala, globus pallidus, and thalamus by carrying out descriptives as well as multilevel and variance component analysis.

RESULTS

Median absolute back-to-back differences varied between GM structures, ranging from 59.6-156.4 μL for volume change, and 1.26%-8.63% for percentage volume change. FreeSurfer had a better performance for the outcome of longitudinal volume change for the bilateral amygdala, putamen, left caudate nucleus (P < .005), and right thalamus (P < .001). For longitudinal percentage volume change, Freesurfer performed better for the left amygdala, bilateral caudate nucleus, and left putamen (P < .001). Smaller limits of agreement were found for FreeSurfer for both outcomes for all GM structures except the globus pallidus. Our results showed that back-to-back differences in 1-year percentage volume change were approximately 1.5-3.5 times larger than the mean measured 1-year volume change of those structures.

CONCLUSIONS

Longitudinal deep GM atrophy measures should be interpreted with caution. Furthermore, deep GM atrophy measurement techniques require substantially improved reproducibility, specifically when aiming for personalized medicine.

Reliable measurements of brain atrophy in individual patients with multiple sclerosis

INTRODUCTION

As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient.

METHOD

In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole-brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1-weighted MRI scans.

RESULTS

MS metrix is compared with SIENA with respect to test-retest error and consistency, resulting in an average test-retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test-retest is 0.19% (MS metrix) and 0.31% (SIENA).

CONCLUSION

Therefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow-up of treatment and disease progression in MS patients.

Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease

Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.

Reproducibility of hippocampal atrophy rates measured with manual, FreeSurfer, AdaBoost, FSL/FIRST and the MAPS-HBSI methods in Alzheimer's disease

The purpose of this study is to assess the reproducibility of hippocampal atrophy rate measurements of commonly used fully-automated algorithms in Alzheimer disease (AD). The reproducibility of hippocampal atrophy rate for FSL/FIRST, AdaBoost, FreeSurfer, MAPS independently and MAPS combined with the boundary shift integral (MAPS-HBSI) were calculated. Back-to-back (BTB) 3D T1-weighted MPRAGE MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI1) study at baseline and year one were used. Analysis on 3 groups of subjects was performed - 562 subjects at 1.5T, a 75 subject group that also had manual segmentation and 111 subjects at 3T. A simple and novel statistical test based on the binomial distribution was used that handled outlying data points robustly. Median hippocampal atrophy rates were -1.1%/year for healthy controls, -3.0%/year for mildly cognitively impaired and -5.1%/year for AD subjects. The best reproducibility was observed for MAPS-HBSI (1.3%), while the other methods tested had reproducibilities at least 50% higher at 1.5T and 3T which was statistically significant. For a clinical trial, MAPS-HBSI should require less than half the subjects of the other methods tested. All methods had good accuracy versus manual segmentation. The MAPS-HBSI method has substantially better reproducibility than the other methods considered.

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 Alzheimer's 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.

Novel composite MRI scale correlates highly with disability in multiple sclerosis patients

Understanding genotype-phenotype relationships or development/validation of biomarkers requires large multicenter cohorts integrated by universal quantification of crucial phenotypical traits, such as central nervous system (CNS) tissue destruction. We hypothesized that mathematical modeling-guided combination of biologically meaningful, semi-quantitative MRI elements characterized by high signal-to-noise ratio will provide such reliable, universal tool for measuring CNS tissue destruction. We retrospectively graded 15 elements in MRI scans performed in 419 untreated subjects with or without neurological diseases, while being blinded to their prospectively acquired clinical scores. We then used 305 subjects for disability-guided mathematical modeling to select and combine MRI elements that had non-redundant contributions to clinical disability, resulting in Combinatorial MRI Scale (COMRIS). We validated our model on the remaining 114 independent subjects. COMRIS requires 5-10 min per scan on average to compute and demonstrates highly significant (p < 0.0001) and validation-consistent Spearman correlation coefficients (0.75, 0.76, and 0.65) for the expanded disability status scale (EDSS), Scripps neurological rating scale (SNRS), and symbol digit modality test (SDMT) measures of neurological disability, respectively. Because COMRIS is not greatly influenced by MRI scanners or protocols and can be computed even in the presence of some motion artifacts, it does not require censoring out patients and it provides comparable results across different cohorts. As such, it represents a broadly available clinical and research tool that can facilitate multicenter research studies and comparative analyses across patient cohorts and research projects.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Volume of hippocampal substructures in borderline personality disorder

Borderline personality disorder (BPD) may be associated with smaller hippocampi in comparison to hippocampal size in controls. However, specific pathology in hippocampal substructures (i.e., head, body and tail) has not been sufficiently investigated. To address hippocampal structure in greater detail, we studied 39 psychiatric inpatients and outpatients with a DSM-IV diagnosis of BPD and 39 healthy controls. The hippocampus and its substructures were segmented manually on magnetic resonance imaging scans. The volumes of hippocampal substructures (and total hippocampal volume) did not differ between BPD patients and controls. Exploratory analysis suggests that patients with a lifetime history of posttraumatic stress disorder (PTSD) may have a significantly smaller hippocampus - affecting both the hippocampal head and body - in comparison to BPD patients without comorbid PTSD (difference in total hippocampal volume: -10.5%, 95%CI -2.6 to -18.5, significant). Also, patients fulfilling seven or more DSM-IV BPD criteria showed a hippocampal volume reduction, limited to the hippocampal head (difference in volume of the hippocampal head: -16.5%, 95%CI -6.1 to -26.8, significant). Disease heterogeneity in respect to, for example, symptom severity and psychiatric comorbidities may limit direct comparability between studies; the results presented here may reflect hippocampal volumes in patients who are "less" affected or they may simply be a chance finding. However, there is also the possibility that global effects of BPD on the hippocampus may have previously been overestimated.

Accelerated vs. unaccelerated serial MRI based TBM-SyN measurements for clinical trials in Alzheimer's disease

OBJECTIVE

Our primary objective was to compare the performance of unaccelerated vs. accelerated structural MRI for measuring disease progression using serial scans in Alzheimer's disease (AD).

METHODS

We identified cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD subjects from all available Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with usable pairs of accelerated and unaccelerated scans. There were a total of 696 subjects with baseline and 3 month scans, 628 subjects with baseline and 6 month scans and 464 subjects with baseline and 12 month scans available. We employed the Symmetric Diffeomorphic Image Normalization method (SyN) for normalization of the serial scans to obtain tensor based morphometry (TBM) maps which indicate the structural changes between pairs of scans. We computed a TBM-SyN summary score of annualized structural changes over 31 regions of interest (ROIs) that are characteristically affected in AD. TBM-SyN scores were computed using accelerated and unaccelerated scan pairs and compared in terms of agreement, group-wise discrimination, and sample size estimates for a hypothetical therapeutic trial.

RESULTS

We observed a number of systematic differences between TBM-SyN scores computed from accelerated and unaccelerated pairs of scans. TBM-SyN scores computed from accelerated scans tended to have overall higher estimated values than those from unaccelerated scans. However, the performance of accelerated scans was comparable to unaccelerated scans in terms of discrimination between clinical groups and sample sizes required in each clinical group for a therapeutic trial. We also found that the quality of both accelerated vs. unaccelerated scans were similar.

CONCLUSIONS

Accelerated scanning protocols reduce scan time considerably. Their group-wise discrimination and sample size estimates were comparable to those obtained with unaccelerated scans. The two protocols did not produce interchangeable TBM-SyN estimates, so it is arguably important to use either accelerated pairs of scans or unaccelerated pairs of scans throughout the study duration.

Longitudinal assessment of global and regional atrophy rates in Alzheimer's disease and dementia with Lewy bodies

BACKGROUND & OBJECTIVE

Percent whole brain volume change (PBVC) measured from serial MRI scans is widely accepted as a sensitive marker of disease progression in Alzheimer's disease (AD). However, the utility of PBVC in the differential diagnosis of dementia remains to be established. We compared PBVC in AD and dementia with Lewy bodies (DLB), and investigated associations with clinical measures.

METHODS

72 participants (14 DLBs, 25 ADs, and 33 healthy controls (HCs)) underwent clinical assessment and 3 Tesla T1-weighted MRI at baseline and repeated at 12 months. We used FSL-SIENA to estimate PBVC for each subject. Voxelwise analyses and ANCOVA compared PBVC between DLB and AD, while correlational tests examined associations of PBVC with clinical measures.

RESULTS

AD had significantly greater atrophy over 1 year (1.8%) compared to DLB (1.0%; p = 0.01) and HC (0.9%; p < 0.01) in widespread regions of the brain including periventricular areas. PBVC was not significantly different between DLB and HC (p = 0.95). There were no differences in cognitive decline between DLB and AD. In the combined dementia group (AD and DLB), younger age was associated with higher atrophy rates (r = 0.49, p < 0.01).

CONCLUSIONS

AD showed a faster rate of global brain atrophy compared to DLB, which had similar rates of atrophy to HC. Among dementia subjects, younger age was associated with accelerated atrophy, reflecting more aggressive disease in younger people. PBVC could aid in differentiating between DLB and AD, however its utility as an outcome marker in DLB is limited.

Empowering imaging biomarkers of Alzheimer's disease

In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.

Correlation between hippocampal sulcus width and severity of obstructive sleep apnea syndrome

The aim of the present study was to evaluate the relationship between obstructive sleep apnea syndrome (OSAS) severity and the hippocampal sulcus width in a cohort of subjects with OSAS and controls. A total of 149 OSAS patients and 60 nonapneic controls were included in the study. Overnight polysomnograpy was performed in all patients. Hippocampal sulcus width of the patients was measured by a radiologist blinded to the diagnosis of the patients. Other variables noted for each patient were as follows: gender, age, body mass index, apnea hypopnea index, Epworth sleepiness scale, sleep efficacy, mean saturation, lowest O2 saturation, longest apnea duration, neck circumference, waist circumference, hip circumference. A total of 149 OSAS patients were divided into three groups: mild OSAS (n = 54), moderate OSAS (n = 40), severe OSAS (n = 55) groups. The control group consisted of patients with AHI <5 (n = 60). Hippocampal sulcus width was 1.6 ± 0.83 mm in the control group; while 1.9 ± 0.81 mm in mild OSAS, 2.1 ± 0.60 mm in moderate OSAS, and 2.9 ± 0.58 mm in severe OSAS groups (p < 0.001). Correlation analysis of variables revealed that apnea hypopnea index (rs = 0.483, p < 0.001) was positively correlated with hippocampal sulcus width. Our findings demonstrated that severity of OSAS might be associated with various pathologic mechanisms including increased hippocampal sulcus width.

The SIENA/FSL whole brain atrophy algorithm is no more reproducible at 3T than 1.5 T for Alzheimer's disease

The back-to-back (BTB) acquisition of MP-RAGE MRI scans of the Alzheimer׳s Disease Neuroimaging Initiative (ADNI1) provides an excellent data set with which to check the reproducibility of brain atrophy measures. As part of ADNI1, 131 subjects received BTB MP-RAGEs at multiple time points and two field strengths of 3T and 1.5 T. As a result, high quality data from 200 subject-visit-pairs was available to compare the reproducibility of brain atrophies measured with FSL/SIENA over 12 to 18 month intervals at both 3T and 1.5 T. Although several publications have reported on the differing performance of brain atrophy measures at 3T and 1.5 T, no formal comparison of reproducibility has been published to date. Another goal was to check whether tuning SIENA options, including -B, -S, -R and the fractional intensity threshold (f) had a significant impact on the reproducibility. The BTB reproducibility for SIENA was quantified by the 50th percentile of the absolute value of the difference in the percentage brain volume change (PBVC) for the BTB MP-RAGES. At both 3T and 1.5 T the SIENA option combination of "-B f=0.2", which is different from the default values of f=0.5, yielded the best reproducibility as measured by the 50th percentile yielding 0.28 (0.23-0.39)% and 0.26 (0.20-0.32)%. These results demonstrated that in general 3T had no advantage over 1.5 T for the whole brain atrophy measure - at least for SIENA. While 3T MRI is superior to 1.5 T for many types of measurements, and thus worth the additional cost, brain atrophy measurement does not seem to be one of them.

Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

BACKGROUND

To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated.

AIM

To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD.

METHODS

From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations.

RESULTS

Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7.5% of the hippocampi for FreeSurfer and in 6.9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±7.2% for FreeSurfer, ±9.7% for expert manual delineation, and ±10.0% for FIRST. Methods ranked the same for reproducibility of 1-year μL volume change, with LoA of ±218 μL for FreeSurfer, ±319 μL for expert manual delineation, and ±333 μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change.

CONCLUSION

Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.

Dysglycemia, brain volume and vascular lesions on MRI in a memory clinic population

OBJECTIVE

It is unclear, if the association between abnormalities in glucose metabolism (dysglycemia) and impaired cognitive functioning is primarily driven by degenerative or vascular brain damage. We therefore examined the relation between dysglycemia and brain volume and vascular lesions on MRI in a memory clinic population.

METHODS

The relations between markers of glycemia (HbA1c and fasting glucose levels) and normalized brain volume, medial temporal lobe atrophy and vascular lesions (white matter hyperintensities, lacunes) were assessed in 274 consecutive patients attending a memory clinic, using linear regression analyses.

RESULTS

Clinical diagnoses were subjective complaints (n=117), mild cognitive impairment (n=62), Alzheimer's disease (n=61) and other type of dementia (n=34). Twenty patients had a history of diabetes. Across the whole study population there was no relation between HbA1c or fasting glucose and the brain MRI measurements, after adjustments for age, sex and diagnostic group. Secondary analyses after stratification by diabetes status, diagnosis and median age (67 years) did not change the results.

CONCLUSION

In this memory clinic population, dysglycemia was not associated with either brain volume or vascular lesions. Apparently, dysglycemia is not associated with a specific class of brain pathology in patients with cognitive complaints.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Validation of the automated method VIENA: an accurate, precise, and robust measure of ventricular enlargement

BACKGROUND

In many retrospective studies and large clinical trials, high-resolution, good-contrast 3DT1 images are unavailable, hampering detailed analysis of brain atrophy. Ventricular enlargement then provides a sensitive indirect measure of ongoing central brain atrophy. Validated automated methods are required that can reliably measure ventricular enlargement and are robust across magnetic resonance (MR) image types.

AIM

To validate the automated method VIENA for measuring the percentage ventricular volume change (PVVC) between two scans.

MATERIALS AND METHODS

Accuracy was assessed using four image types, acquired in 15 elderly patients (five with Alzheimer's disease, five with mild cognitive impairment, and five cognitively normal elderly) and 58 patients with multiple sclerosis (MS), by comparing PVVC values from VIENA to manual outlining. Precision was assessed from data with three imaging time points per MS patient, by measuring the difference between the direct (one-step) and indirect (two-step) measurement of ventricular volume change between the first and last time points. The stringent concordance correlation coefficient (CCC) was used to quantify absolute agreement.

RESULTS

CCC of VIENA with manual measurement was 0.84, indicating good absolute agreement. The median absolute difference between two-step and one-step measurement with VIENA was 1.01%, while CCC was 0.98. Neither initial ventricular volume nor ventricular volume change affected performance of the method.

DISCUSSION

VIENA has good accuracy and good precision across four image types. VIENA therefore provides a useful fully automated method for measuring ventricular volume change in large datasets.

CONCLUSION

VIENA is a robust, accurate, and precise method for measuring ventricular volume change.

Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80-in two-fold nested cross-validation-is 104 MCI subjects (95% CI: [94,139]) for a 1-year drug trial, and 75AD subjects [64,102]. This compares favorably with other MRI analysis methods. The standard "statistical ROI" approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2 years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.

Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS.

Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease

BACKGROUND

The promise of Alzheimer's disease biomarkers has led to their incorporation in new diagnostic criteria and in therapeutic trials; however, significant barriers exist to widespread use. Chief among these is the lack of internationally accepted standards for quantitative metrics. Hippocampal volumetry is the most widely studied quantitative magnetic resonance imaging measure in Alzheimer's disease and thus represents the most rational target for an initial effort at standardization.

METHODS AND RESULTS

The authors of this position paper propose a path toward this goal. The steps include the following: (1) Establish and empower an oversight board to manage and assess the effort, (2) adopt the standardized definition of anatomic hippocampal boundaries on magnetic resonance imaging arising from the European Alzheimer's Disease Centers-Alzheimer's Disease Neuroimaging Initiative hippocampal harmonization effort as a reference standard, (3) establish a scientifically appropriate, publicly available reference standard data set based on manual delineation of the hippocampus in an appropriate sample of subjects (Alzheimer's Disease Neuroimaging Initiative), and (4) define minimum technical and prognostic performance metrics for validation of new measurement techniques using the reference standard data set as a benchmark.

CONCLUSIONS

Although manual delineation of the hippocampus is the best available reference standard, practical application of hippocampal volumetry will require automated methods. Our intent was to establish a mechanism for credentialing automated software applications to achieve internationally recognized accuracy and prognostic performance standards that lead to the systematic evaluation and then widespread acceptance and use of hippocampal volumetry. The standardization and assay validation process outlined for hippocampal volumetry was envisioned as a template that could be applied to other imaging biomarkers.