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There is an increasing body of evidence suggesting that vascular disease could contribute to cognitive decline and overt dementia. Of particular interest is atherosclerosis, as it is not only associated with dementia, but could be a potential mechanism through which cardiovascular disease directly impacts brain health. In this work, we evaluated the differences in functional near infrared spectroscopy (fNIRS)-based measures of brain activation, task performance, and the change in central hemodynamics (mean arterial pressure (MAP) and heart rate (HR)) during a Stroop color-word task in individuals with atherosclerosis, defined as bilateral carotid plaques (n = 33) and healthy age-matched controls (n = 33). In the healthy control group, the left prefrontal cortex (LPFC) was the only region showing evidence of activation when comparing the incongruous with the nominal Stroop test. A smaller extent of brain activation was observed in the Plaque group compared with the healthy controls (1) globally, as measured by oxygenated hemoglobin (p = 0.036) and (2) in the LPFC (p = 0.02) and left sensorimotor cortices (LMC)(p = 0.008) as measured by deoxygenated hemoglobin. There were no significant differences in HR, MAP, or task performance (both in terms of the time required to complete the task and number of errors made) between Plaque and control groups. These results suggest that carotid atherosclerosis is associated with altered functional brain activation patterns despite no evidence of impaired performance of the Stroop task or central hemodynamic changes., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods., (Copyright © 2015. Published by Elsevier Inc.)

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., (Copyright © 2015. Published by Elsevier Inc.)

Stable MR acquisition is essential for reliable measurement of brain atrophy in longitudinal studies. One attractive recent advance in MRI is to speed up acquisition using parallel imaging (e.g. reducing volumetric T1-weighted acquisition scan times from around 9 to 5 min). In some studies, a decision to change to an accelerated acquisition may have been deliberately taken, while in others repeat scans may occasionally be accidentally acquired with an accelerated acquisition. In ADNI, non-accelerated and accelerated scans were acquired in the same scanning session on each individual. We investigated the impact on brain atrophy as measured by k-means normalized boundary shift integral (KN-BSI) and deformation-based morphometry when changing from non-accelerated to accelerated MRI acquisitions over a 12-month interval using scans of 422 subjects from ADNI. KN-BSIs were calculated using both a non-accelerated baseline scan and non-accelerated 12-month scans (i.e. consistent acquisition), and a non-accelerated baseline scan and an accelerated 12-month scan (i.e. changed acquisition). Fluid-based non-rigid registration was also performed on those scans to estimate the brain atrophy rate. We found that the effect on KN-BSI and fluid-based non-rigid registration depended on the scanner manufacturer. For KN-BSI, in Philips and Siemens scanners, the change had very little impact on the measured atrophy rate (increase of 0.051% in Philips and -0.035% in Siemens from consistent acquisition to changed acquisition), whereas, in GE, the change caused a mean reduction of 0.65% in the brain atrophy rate. This is likely due to the difference in tissue contrast between gray matter and cerebrospinal fluid in the non-accelerated and accelerated scans in GE, which uses IR-FSPGR instead of MP-RAGE. For fluid-based non-rigid registration, the change caused a mean increase of 0.29% in the brain atrophy rate in the changed acquisition compared with consistent acquisition in Philips, whereas in GE and Siemens, the change had less impact on the mean atrophy rate (increase of 0.18% in GE and 0.049% in Siemens). Moving from non-accelerated baseline scans to accelerated scans for follow-up may have surprisingly little effect on computed atrophy rates depending on the exact sequence details and the scanner manufacturer; even accidentally inconsistent scans of this nature may still be useful., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R(2)=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV., (Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.)

We introduce a novel image-processing framework for tracking longitudinal changes in white matter microstructure using diffusion tensor imaging (DTI). Charting the trajectory of such temporal changes offers new insight into disease progression but to do so accurately faces a number of challenges. Recent developments have highlighted the importance of processing each subject's data at multiple time points in an unbiased way. In this paper, we aim to highlight a different challenge critical to the processing of longitudinal DTI data, namely the approach to image alignment. Standard approaches in the literature align DTI data by registering the corresponding scalar-valued fractional anisotropy (FA) maps. We propose instead a DTI registration algorithm that leverages full tensor information to drive improved alignment. This proposed pipeline is evaluated against the standard FA-based approach using a DTI dataset from an ongoing study of Alzheimer's disease (AD). The dataset consists of subjects scanned at two time points and at each time point the DTI acquisition consists of two back-to-back repeats in the same scanning session. The repeated scans allow us to evaluate the specificity of each pipeline, using a test-retest design, and assess precision, using bootstrap-based method. The results show that the tensor-based pipeline achieves both higher specificity and precision than the standard FA-based approach. Tensor-based registration for longitudinal processing of DTI data in clinical studies may be of particular value in studies assessing disease progression., (Copyright © 2013 Elsevier Inc. All rights reserved.)

The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild-moderate Alzheimer's subjects and 23 controls. It consists of 708 scans conducted by the same radiographer with the same scanner and sequences at intervals of 2, 6, 14, 26, 38 and 52 weeks, 18 and 24 months from baseline, with accompanying information on gender, age and Mini Mental State Examination (MMSE) scores. Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and compare techniques, particularly for measurement of longitudinal volume change in serially acquired MR., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Brain atrophy measurement is increasingly important in studies of neurodegenerative diseases such as Alzheimer's disease (AD), with particular relevance to trials of potential disease-modifying drugs. Automated registration-based methods such as the boundary shift integral (BSI) have been developed to provide more precise measures of change from a pair of serial MR scans. However, when a method treats one image of the pair (typically the baseline) as the reference to which the other is compared, this systematic asymmetry risks introducing bias into the measurement. Recent concern about potential biases in longitudinal studies has led to several suggestions to use symmetric image registration, though some of these methods are limited to two time-points per subject. Therapeutic trials and natural history studies increasingly involve several serial scans, it would therefore be useful to have a method that can consistently estimate brain atrophy over multiple time-points. Here, we use the log-Euclidean concept of a within-subject average to develop affine registration and differential bias correction methods suitable for any number of time-points, yielding a longitudinally consistent multi-time-point BSI technique. Baseline, 12-month and 24-month MR scans of healthy controls, subjects with mild cognitive impairment and AD patients from the Alzheimer's Disease Neuroimaging Initiative are used for testing the bias in processing scans with different amounts of atrophy. Four tests are used to assess bias in brain volume loss from BSI: (a) inverse consistency with respect to ordering of pairs of scans 12 months apart; (b) transitivity consistency over three time-points; (c) randomly ordered back-to-back scans, expected to show no consistent change over subjects; and (d) linear regression of the atrophy rates calculated from the baseline and 12-month scans and the baseline and 24-month scans, where any additive bias should be indicated by a non-zero intercept. Results indicate that the traditional BSI processing pipeline does not exhibit significant bias due to its use of windowed sinc interpolation, but with linear interpolation and asymmetric registration, bias can be pronounced. Either improved interpolation or symmetric registration alone can greatly reduce this bias, and our proposed method combining both aspects shows no significant bias in any of the four experiments., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Thompson and Holland (2010) highlight a biologically implausible deceleration of atrophy in results previously published in this journal (Hua et al., 2010); the results were derived using tensor based morphometry on images from the Alzheimer's Disease Neuroimaging Initiative. They speculate that bias may have been introduced due to asymmetric interpolation in global image registration, and/or to the use of a statistically defined region of interest. In their reply, Hua et al. (this issue) acknowledge the presence of a bias, but show that it stems largely from an asymmetry in the local image registration algorithm (an asymmetry common to methods in many published studies using nonlinear registration). Hua et al. demonstrate that the bias can largely be removed using a revised symmetric algorithm. This correspondence raises important issues relating to the lack of ground truth against which image analysis methodologies designed to determine atrophy patterns and rates can be assessed; and the increasing importance of striving to avoid potential biases as these techniques become utilised in clinical trials. In the absence of a "gold standard", we discuss a number of steps against which methodologies designed to quantify atrophy from serial scans can be assessed., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Whole brain extraction is an important pre-processing step in neuroimage analysis. Manual or semi-automated brain delineations are labour-intensive and thus not desirable in large studies, meaning that automated techniques are preferable. The accuracy and robustness of automated methods are crucial because human expertise may be required to correct any suboptimal results, which can be very time consuming. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to extract whole brains from 682 1.5T and 157 3T T(1)-weighted MR baseline images from the Alzheimer's Disease Neuroimaging Initiative database. Semi-automated brain segmentations with manual editing and checking were used as the gold-standard to compare with the results. The median Jaccard index of MAPS was higher than HWA, BET and BSE in 1.5T and 3T scans (p<0.05, all tests), and the 1st to 99th centile range of the Jaccard index of MAPS was smaller than HWA, BET and BSE in 1.5T and 3T scans ( p<0.05, all tests). HWA and MAPS were found to be best at including all brain tissues (median false negative rate ≤0.010% for 1.5T scans and ≤0.019% for 3T scans, both methods). The median Jaccard index of MAPS were similar in both 1.5T and 3T scans, whereas those of BET, BSE and HWA were higher in 1.5T scans than 3T scans (p<0.05, all tests). We found that the diagnostic group had a small effect on the median Jaccard index of all four methods. In conclusion, MAPS had relatively high accuracy and low variability compared to HWA, BET and BSE in MR scans with and without atrophy., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Imaging studies of cerebral volumes often adjust for factors such as age that may confound between-subject comparisons. However the use of nuisance covariates in imaging studies is inconsistent, which can make interpreting results across studies difficult. Using magnetic resonance images of 78 healthy controls we assessed the effects of age, gender, head size and scanner upgrade on region of interest (ROI) volumetry, cortical thickness and voxel-based morphometric (VBM) measures. We found numerous significant associations between these variables and volumetric measures: cerebral volumes and cortical thicknesses decreased with increasing age, men had larger volumes and smaller thicknesses than women, and increasing head size was associated with larger volumes. The relationships between most ROIs and head size volumes were non-linear. With age, gender, head size and upgrade in one model we found that volumes and thicknesses decreased with increasing age, women had larger volumes than men (VBM, whole-brain and white matter volumes), increasing head size was associated with larger volumes but not cortical thickness, and scanner upgrade had an effect on thickness and some volume measures. The effects of gender on cortical thickness when adjusting for head size, age and upgrade showed some non-significant effect (women>men), whereas the independent effect of head size showed little pattern. We conclude that age and head size should be considered in ROI volume studies, age, gender and upgrade should be considered for cortical thickness studies and all variables require consideration for VBM analyses. Division of all volumes by head size is unlikely to be adequate owing to their non-proportional relationship., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Neural network breakdown is a key issue in neurodegenerative disease, but the mechanisms are poorly understood. Here we investigated patterns of brain atrophy produced by defined molecular lesions in the two common forms of genetically mediated frontotemporal lobar degeneration (FTLD). Nine patients with progranulin (GRN) mutations and eleven patients with microtubule-associated protein tau (MAPT) mutations had T1 MR brain imaging. Brain volumetry and grey and white matter voxel-based morphometry (VBM) were used to assess patterns of cross-sectional atrophy in the two groups. In a subset of patients with longitudinal MRI rates of whole-brain atrophy were derived using the brain-boundary-shift integral and a VBM-like analysis of voxel-wise longitudinal volume change was performed. The GRN mutation group showed asymmetrical atrophy whereas the MAPT group showed symmetrical atrophy. Brain volumes were smaller in the GRN group with a faster rate of whole-brain atrophy. VBM delineated a common anterior cingulate-prefrontal-insular pattern of atrophy in both disease groups. Additional disease-specific profiles of grey and white matter loss were identified on both cross-sectional and longitudinal imaging: GRN mutations were associated with asymmetrical inferior frontal, temporal and inferior parietal lobe grey matter atrophy and involvement of long intrahemispheric association white matter tracts, whereas MAPT mutations were associated with symmetrical anteromedial temporal lobe and orbitofrontal grey matter atrophy and fornix involvement. The findings suggest that the effects of GRN and MAPT mutations are expressed in partly overlapping but distinct anatomical networks that link specific molecular dysfunction with clinical phenotype., (Copyright 2009 Elsevier Inc. All rights reserved.)

Volume and change in volume of the hippocampus are both important markers of Alzheimer's disease (AD). Delineation of the structure on MRI is time-consuming and therefore reliable automated methods are required. We describe an improvement (multiple-atlas propagation and segmentation (MAPS)) to our template library-based segmentation technique. The improved technique uses non-linear registration of the best-matched templates from our manually segmented library to generate multiple segmentations and combines them using the simultaneous truth and performance level estimation (STAPLE) algorithm. Change in volume over 12months (MAPS-HBSI) was measured by applying the boundary shift integral using MAPS regions. Methods were developed and validated against manual measures using subsets from Alzheimer's Disease Neuroimaging Initiative (ADNI). The best method was applied to 682 ADNI subjects, at baseline and 12-month follow-up, enabling assessment of volumes and atrophy rates in control, mild cognitive impairment (MCI) and AD groups, and within MCI subgroups classified by subsequent clinical outcome. We compared our measures with those generated by Surgical Navigation Technologies (SNT) available from ADNI. The accuracy of our volumes was one of the highest reported (mean(SD) Jaccard Index 0.80(0.04) (N=30)). Both MAPS baseline volume and MAPS-HBSI atrophy rate distinguished between control, MCI and AD groups. Comparing MCI subgroups (reverters, stable and converters): volumes were lower and rates higher in converters compared with stable and reverter groups (p< or =0.03). MAPS-HBSI required the lowest sample sizes (78 subjects) for a hypothetical trial. In conclusion, the MAPS and MAPS-HBSI methods give accurate and reliable volumes and atrophy rates across the clinical spectrum from healthy aging to AD., (Copyright 2010 Elsevier Inc. All rights reserved.)

Newly proposed diagnostic criteria for Alzheimer's disease include cerebrospinal fluid (CSF) tau levels as one core supportive criterion. The published high sensitivity and specificity figures for CSF tau levels in Alzheimer's disease are offset by the large range of proposed cutoff values (9.6 pg/mL to 1140 pg/mL). This study aimed to provide guidance on how to establish, validate and audit CSF tau cutoff values using an unbiased, two-stage multicentre strategy. Both receiver operator characteristics (ROC) optimised and population-based cutoff values were calculated on a pilot dataset (n=99), validated in a large dataset (n=560) and then compared to the literature. The data suggest using an ROC optimised cutoff level of 323+/-51.7 pg/mL allowing for the published inter-laboratory coefficient of variation of 16%. This cutoff level was confirmed in a prospective audit (n=100). As demand for CSF tau levels will increase globally, the accuracy of local CSF hTau cutoff levels can be compared against this benchmark., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

We describe an improved method of measuring brain atrophy rates from serial MRI for multi-site imaging studies of Alzheimer's disease (AD). The method (referred to as KN-BSI) improves an existing brain atrophy measurement technique-the boundary shift integral (classic-BSI), by performing tissue-specific intensity normalization and parameter selection. We applied KN-BSI to measure brain atrophy rates of 200 normal and 141 AD subjects using baseline and 1-year MRI scans downloaded from the Alzheimer's Disease Neuroimaging Initiative database. Baseline and repeat images were reviewed as pairs by expert raters and given quality scores. Including all image pairs, regardless of quality score, mean KN-BSI atrophy rates were 0.09% higher (95% CI 0.03% to 0.16%, p=0.007) than classic-BSI rates in controls and 0.07% higher (-0.01% to 0.16%, p=0.07) higher in ADs. The SD of the KN-BSI rates was 22% lower (15% to 29%, p<0.001) in controls and 13% lower (6% to 20%, p=0.001) in ADs, compared to classic-BSI. Using these results, the estimated sample size (needed per treatment arm) for a hypothetical trial of a treatment for AD (80% power, 5% significance to detect a 25% reduction in atrophy rate) would be reduced from 120 to 81 (a 32% reduction, 95% CI=18% to 45%, p<0.001) when using KN-BSI instead of classic-BSI. We concluded that KN-BSI offers more robust brain atrophy measurement than classic-BSI and substantially reduces sample sizes needed in clinical trials., (Copyright 2010 Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) and semantic dementia (SD) are characterized by different patterns of global and temporal lobe atrophy which can be studied using magnetic resonance imaging (MRI). Manual delineation of regions of interest is time-consuming. FreeSurfer is a freely available automated technique which has a facility to label cortical and subcortical brain regions automatically. As with all automated techniques comparison with existing methods is important. Eight temporal lobe structures in each hemisphere were delineated using FreeSurfer and compared with manual segmentations in 10 control, 10 AD, and 10 SD subjects. The reproducibility errors for the manual segmentations ranged from 3% to 6%. Differences in protocols between the two methods led to differences in absolute volumes with the greatest differences between methods found bilaterally in the hippocampus, entorhinal cortex and fusiform gyrus (p<0.005). However, good correlations between the methods were found for most regions, with the highest correlations shown for the ventricles, whole brain and left medial-inferior temporal gyrus (r>0.9), followed by the bilateral amygdala and hippocampus, left superior temporal gyrus, right medial-inferior temporal gyrus and left temporal lobe (r>0.8). Overlap ratios differed between methods bilaterally in the amygdala, superior temporal gyrus, temporal lobe, left fusiform gyrus and right parahippocampal gyrus (p<0.01). Despite differences in protocol and volumes, both methods showed similar atrophy patterns in the patient groups compared with controls, and similar right-left differences, suggesting that both methods accurately distinguish between the three groups., (Copyright (c) 2009 Elsevier Inc. All rights reserved.)

Objective: Caudate atrophy rate measured from serial MRI is proposed as a biomarker of HD progression that may be of use in assessing putative disease-modifying agents. Manual measurement techniques are the most widely applied but are time-consuming. We describe and evaluate an automated technique based on a local registration and boundary shift integral (BSI) approach at the caudate-CSF and caudate-white matter boundaries; caudate boundary shift integral (CBSI)., Methods: Two-year caudate volume change was measured in controls, premanifest HD and early HD using the CBSI and compared with a detailed manual measure in terms of 1) raw caudate volume change, 2) group differentiation, 3) associations with clinical variables and 4) rater requirements. CBSI additivity was assessed by comparing measurements over a single scan pair (baseline-->2 years), with the sum of measurements from two scan pairs (baseline-->1 year-->2 years)., Results: Techniques produced comparable caudate volume change measurements, although CBSI under-reported by 0.04 ml relative to manual. Both techniques distinguished controls, premanifest and early HD with a stepwise increase in rates across groups. Higher rates (CBSI and manual) were associated with increased proximity to estimated disease onset but not clinical change scores. CBSI reduced rater requirements by 2/3 (2 h per subject) relative to manual for this three time-point investigation. CBSI measurements over one scan pair showed good agreement with the sum of measurements from two scan pairs., Conclusions: CBSI results were comparable to a manual measure but with reduced rater requirements. CBSI may be of use in large-scale studies of HD.

Rates of brain atrophy derived from serial magnetic resonance (MR) studies may be used to assess therapies for Alzheimer's disease (AD). These measures may be confounded by changes in scanner voxel sizes. For this reason, the Alzheimer's Disease Neuroimaging Initiative (ADNI) included the imaging of a geometric phantom with every scan. This study compares voxel scaling correction using a phantom with correction using a 9 degrees of freedom (9DOF) registration algorithm. We took 129 pairs of baseline and 1-year repeat scans, and calculated the volume scaling correction, previously measured using the phantom. We used the registration algorithm to quantify any residual scaling errors, and found the algorithm to be unbiased, with no significant (p=0.97) difference between control (n=79) and AD subjects (n=50), but with a mean (SD) absolute volume change of 0.20 (0.20) % due to linear scalings. 9DOF registration was shown to be comparable to geometric phantom correction in terms of the effect on atrophy measurement and unbiased with respect to disease status. These results suggest that the additional expense and logistic effort of scanning a phantom with every patient scan can be avoided by registration-based scaling correction. Furthermore, based upon the atrophy rates in the AD subjects in this study, sample size requirements would be approximately 10-12% lower with (either) correction for voxel scaling than if no correction was used.

There is great interest in using automatic computational neuroanatomy tools to study ageing and neurodegenerative disease. Voxel-based morphometry (VBM) is one of the most widely used of such techniques. VBM performs voxel-wise statistical analysis of smoothed spatially normalised segmented Magnetic Resonance Images. There are several reasons why the analysis should include only voxels within a certain mask. We show that one of the most commonly used strategies for defining this mask runs a major risk of excluding from the analysis precisely those voxels where the subjects' brains were most vulnerable to atrophy. We investigate the issues related to mask construction, and recommend the use of alternative strategies which greatly decrease this danger of false negatives.

The evaluation of atrophy quantification methods based on magnetic resonance imaging have been usually hindered by the lack of realistic gold standard data against which to judge these methods or to help refine them. Recently [Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D., Fox, N., 2006. Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med.l Imaging 25, 1417-1430], we presented a technique in which atrophy is realistically simulated in different tissue compartments or neuroanatomical structures with a phenomenological model. In this study, we have generated a cohort of realistic simulated Alzheimer's disease (AD) images with known amounts of atrophy, mimicking a set of 19 real controls and 27 probable AD subjects, with an improved version of our atrophy simulation methodology. This database was then used to assess the accuracy of several well-known computational anatomy methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of longitudinal atrophy in brain structures using MR images. SIENA and BSI results correlated very well with gold standard data (Pearson coefficient of 0.962 and 0.969 respectively), achieving small mean absolute differences with respect to the gold standard (percentage change from baseline volume): BSI of 0.23%+/-0.26%; SIENA of 0.22%+/-0.28%. Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD-based technique outperformed the fluid one in all evaluated structures (mean absolute differences from the gold standard in percentage change from baseline volume): whole brain, FFD=0.31%, fluid=0.58%; lateral ventricles, FFD=0.79%; fluid=1.45%; left hippocampus, FFD=0.82%; fluid=1.42%; right hippocampus, FFD=0.95%; fluid=1.62%. The largest errors for both local techniques occurred in the sulcal CSF (FFD=2.27%; fluid=3.55%) regions. For large structures such as the whole brain, these mean absolute differences, relative to the applied atrophy, represented similar percentages for the BSI, SIENA and FFD techniques (controls/patients): BSI, 51.99%/16.36%; SIENA, 62.34%/21.59%; FFD, 41.02%/24.95%. For small structures such as the hippocampi, these percentages were larger, especially for controls where errors were approximately equal to the small applied changes (controls/patients): FFD, 92.82%/43.61%. However, these apparently large relative errors have not prevented the global or hippocampal measures from finding significant group separation in our study. The evaluation framework presented here will help in quantifying whether the accuracy of future methodological developments is sufficient for analysing change in smaller or less atrophied local brain regions. Results obtained in our experiments with realistic simulated data confirm previously published estimates of accuracy for both evaluated global techniques. Regarding Jacobian Integration methods, the FFD-based one demonstrated promising results and potential for being used in clinical studies alongside (or in place of) the more common global methods. The generated gold standard data has also allowed us to identify some stages and sets of parameters in the evaluated techniques--the brain extraction step in the global techniques and the number of multi-resolution levels and the stopping criteria in the registration-based methods--that are critical for their accuracy.

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Voxel-based morphometry [Ashburner, J. and Friston, K.J., 2000. Voxel-based morphometry-the methods. NeuroImage 11(6 Pt 1), 805-821] is a commonly used tool for studying patterns of brain change in development or disease and neuroanatomical correlates of subject characteristics. In performing a VBM study, many methodological options are available; if the study is to be easily interpretable and repeatable, the processing steps and decisions must be clearly described. Similarly, unusual methods and parameter choices should be justified in order to aid readers in judging the importance of such options or in comparing the work with other studies. This editorial suggests core principles that should be followed and information that should be included when reporting a VBM study in order to make it transparent, replicable and useful.

Hippocampal atrophy rates have been used in a number of studies in Alzheimer's disease (AD) to assess disease progression and are being increasingly utilized as an outcome measure in clinical trials of new pharmaceutical agents. Owing to the labor-intensive nature of hippocampal segmentation, more automated approaches are required for such analysis. In this study we compared methods of automatically segmenting the hippocampus (single-person template and template library) on the baseline image in a group of probable AD (n=36) and control (n=19) subjects with serial images. Using the method that gave most similar results to manual, three automated methods of calculating change within the hippocampal region were compared: fluid change calculated using (1) Jacobian change or (2) region propagation and (3) boundary shift. Rates were compared with manual measures. We found that segmentation of baseline hippocampus was most accurate using a template library combined with morphological operations (intensity thresholding plus one conditional dilation). This gave a voxel similarity of 0.69 (0.05) and 0.72 (0.06) in controls and probable AD subjects respectively compared with manual measures. Atrophy rates within these regions were most similar to the manual rates using the boundary shift integral (mean difference from manual rate 0.03% (1.29) in controls and 0.48% (2.44) in AD). A template library segmentation approach, together with morphological operations, provides a segmentation accurate enough to quantify relative change over time. The change over time can then be calculated automatically using boundary shift or fluid measures, with boundary shift giving most similar results to manual.

Measures of structural brain change based on longitudinal MR imaging are increasingly important but can be degraded by intensity non-uniformity. This non-uniformity can be more pronounced at higher field strengths, or when using multichannel receiver coils. We assessed the ability of the non-parametric non-uniform intensity normalization (N3) technique to correct non-uniformity in 72 volumetric brain MR scans from the preparatory phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Normal elderly subjects (n=18) were scanned on different 3-T scanners with a multichannel phased array receiver coil at baseline, using magnetization prepared rapid gradient echo (MP-RAGE) and spoiled gradient echo (SPGR) pulse sequences, and again 2 weeks later. When applying N3, we used five brain masks of varying accuracy and four spline smoothing distances (d=50, 100, 150 and 200 mm) to ascertain which combination of parameters optimally reduces the non-uniformity. We used the normalized white matter intensity variance (standard deviation/mean) to ascertain quantitatively the correction for a single scan; we used the variance of the normalized difference image to assess quantitatively the consistency of the correction over time from registered scan pairs. Our results showed statistically significant (p<0.01) improvement in uniformity for individual scans and reduction in the normalized difference image variance when using masks that identified distinct brain tissue classes, and when using smaller spline smoothing distances (e.g., 50-100 mm) for both MP-RAGE and SPGR pulse sequences. These optimized settings may assist future large-scale studies where 3-T scanners and phased array receiver coils are used, such as ADNI, so that intensity non-uniformity does not influence the power of MR imaging to detect disease progression and the factors that influence it.

Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimer's disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.

The brain bases of specific human behaviours in health and disease are not well established. In this voxel-based morphometric (VBM) study we demonstrate neuroanatomical signatures of different abnormalities of eating behaviour (pathological sweet tooth and increased food consumption, or hyperphagia) in individuals with frontotemporal lobar degeneration (FTLD). Sixteen male patients with FTLD were assessed using the Manchester and Oxford Universities Scale for the Psychopathological Assessment of Dementia and classified according to the presence or absence of abnormal eating behaviours. Volumetric brain magnetic resonance imaging was performed in all patients and in a group of nine healthy age-matched male controls and grey matter changes were assessed using an optimised VBM protocol. Compared with healthy controls, the FTLD group had a typical pattern of extensive bilateral grey matter loss predominantly involving the frontal and temporal lobes. Within the FTLD group, grey matter changes associated with different abnormal behaviours were assessed independently using a covariate-only model. The development of pathological sweet tooth was associated with grey matter loss in a distributed brain network including bilateral posterolateral orbitofrontal cortex (Brodmann areas 12/47) and right anterior insula. Hyperphagia was associated with more focal grey matter loss in anterolateral OFC bilaterally (Brodmann area 11). In accord with emerging evidence in humans and other species, our findings implicate distinct components of a multi-component brain network in the control of specific aspects of eating behaviour.

MRI-derived rates of hippocampal atrophy may serve as surrogate markers of disease progression in mild cognitive impairment (MCI). Manual delineation is the gold standard in hippocampal volumetry; however, this technique is time-consuming and subject to errors. We aimed to compare regional non-linear (fluid) registration measurement of hippocampal atrophy rates against manual delineation in MCI. Hippocampi of 18 subjects were manually outlined twice on MRI scan-pairs (interval+/-SD: 2.01+/-0.11 years), and volumes were subtracted to calculate change over time. Following global affine and local rigid registration, regional fluid registration was performed from which atrophy rates were derived from the Jacobian determinants over the hippocampal region. Atrophy rates as derived by fluid registration were computed using both forward (repeat onto baseline) and backward (baseline onto repeat) registration. Reliability for both methods and agreement between methods was assessed. Mean+/-SD hippocampal atrophy rates (%/year) derived by manual delineation were: left: 2.13+/-1.62; right: 2.36+/-1.78 and for regional fluid registration: forward: left: 2.39+/-1.68; right: 2.49+/-1.52 and backward: left: 2.21+/-1.51; right: 2.42+/-1.49. Mean hippocampal atrophy rates did not differ between both methods. Reliability for manual hippocampal volume measurements (cross-sectional) was high (intraclass correlation coefficient (ICC): baseline and follow-up, left and right, >0.99). However, the resulting ICC for manual measurements of hippocampal volume change (longitudinal) was considerably lower (left: 0.798; right: 0.850) compared with regional fluid registration (forward: left: 0.985; right: 0.988 and backward: left: 0.975; right: 0.989). We conclude that regional fluid registration is more reliable than manual delineation in assessing hippocampal atrophy rates, without sacrificing sensitivity to change. This method may be useful to quantify hippocampal volume change, given the reduction in operator time and improved precision.

We compared two methods of measuring cerebral atrophy in a cohort of 38 clinically probable Alzheimer's disease (AD) subjects and 22 age-matched normal controls, using metrics of zero atrophy, consistency, scaled atrophy and AD/control group separation. The two methods compared were the boundary shift integral (BSI) and a technique based on the integration of Jacobian determinants from non-rigid registration. For each subject, we used two volumetric magnetic resonance (MR) scans at baseline and a third obtained 1 year later. The case of zero atrophy was established by registering the same-day baseline scan pair, which should approximate zero change. Consistency was established by registering the 1-year follow-up scan to each of the baseline scans, giving two measurements of atrophy that should be very similar, while scaled atrophy was established by reducing one of the same-day scans by a fixed amount, and rigidly registering this to the other same-day scan. Group separation was ascertained by calculating atrophy rates over the two 1-year measures for the control and AD subjects. The results showed the Jacobian integration technique was significantly more accurate in calculating scaled atrophy (P < 0.001) and was able to distinguish between control and AD subjects more clearly (P < 0.01).

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

Manual segmentation of the hippocampus is the gold standard in volumetric hippocampal magnetic resonance imaging (MRI) analysis; however, this is difficult to achieve reproducibly. This study explores whether application of local registration and calculation of the hippocampal boundary shift integral (HBSI) can reduce random variation compared with manual measures. Hippocampi were outlined on the baseline and registered-repeat MRIs of 32 clinically diagnosed Alzheimer's disease (AD) patients and 47 matched controls (37-86 years) with a wide range of scanning intervals (175-1173 days). The scans were globally registered using 9 degrees of freedom and subsequently locally registered using 6 degrees of freedom and HBSI was then calculated automatically. HBSI significantly reduced the mean rate (P < 0.01) and variation in controls (P < 0.001) and increased group separation between AD cases and controls. When comparing HBSI atrophy rates with manually derived atrophy rates at 90% sensitivity, specificities were 98% and 81%, respectively. From logistic regression models, a 1% increase in HBSI atrophy rates was associated with an 11-fold (CI 3, 36) increase in the odds of a diagnosis of AD. For manually derived atrophy rates, the equivalent odds ratio was 3 (CI 2,4). We conclude that HBSI-derived atrophy rates reduce operator time and error, and are at least as effective as the manual equivalent as a diagnostic marker and are a potential marker of progression in longitudinal studies and trials.

Purpose: Mild cognitive impairment (MCI) is thought to be the prodromal phase to Alzheimer's disease (AD). We analyzed patterns of gray matter (GM) loss to examine what characterizes MCI and what determines the difference with AD., Materials and Methods: Thirty-three subjects with AD, 14 normal elderly controls (NCLR), and 22 amnestic MCI subjects were included and underwent brain MR imaging. Global GM volume was assessed using segmentation and local GM volume was assessed using voxel-based morphometry (VBM); VBM was optimized for template mismatch and statistical mass., Results: AD subjects had significantly (12.3%) lower mean global GM volume when compared to controls (517 +/- 58 vs. 590 +/- 52 ml; P < 0.001). Global GM volume in the MCI group (552 +/- 52) was intermediate between these two: 6.2% lower than AD and 6.5% higher than the controls but not significantly different from either group. VBM showed that subjects with MCI had significant local reductions in gray matter in the medial temporal lobe (MTL), the insula, and thalamus compared to NCLR subjects. By contrast, when compared to subjects with AD, MCI subjects had more GM in the parietal association areas and the anterior and the posterior cingulate., Conclusion: GM loss in the MTL characterizes MCI, while GM loss in the parietal and cingulate cortices might be a feature of AD.

Longitudinal MR imaging is increasingly being used to measure cerebral atrophy progression in dementia and other neurological disorders. Differences in intensity inhomogeneity between serial scans can confound these measurements. This differential bias also distorts nonlinear registration and makes both manual and automated segmentation of tissue type less reliable. A technique is described for the correction of this differential bias that makes no assumptions about signal distribution, bias field or signal homogeneity. Instead, the bias field calculation is performed on the basis that the remaining structure in the difference image of registered serial scans has small-scale structure. The differential bias field is of much larger scale and can thus be obtained by applying an appropriate filter to the difference image. The serial scan pair is then corrected for the differential bias field and atrophy measurement can be performed on the corrected scan pair. Application of a known, simulated bias field to real serial MR images was shown to alter atrophy measurements significantly. The differential correction method recovered the applied differential bias field and thereby improved atrophy measurements. This method was then applied to serial imaging in patients with dementia using a set of serial scan pairs with visually identified, significant differential bias and a set of scan pairs with negligible differential bias. Differential bias correction specifically reduced the variance of the atrophy measure significantly for the scans with significant differential bias.

This article introduces an automated method for the computation of changes in brain volume from sequential magnetic resonance images (MRIs) using an iterative principal component analysis (IPCA) and demonstrates its ability to characterize whole-brain atrophy rates in patients with Alzheimer's disease (AD). The IPCA considers the voxel intensity pairs from coregistered MRIs and identifies those pairs a sufficiently large distance away from the iteratively determined PCA major axis. Analyses of simulated and real MRI data support the underlying assumption of a linear relationship in paired voxel intensities, identify an outlier distance threshold that optimizes the trade-off between sensitivity and specificity in the detection of small volume changes while accounting for global intensity changes, and demonstrate an ability to detect changes as small as 0.04% of brain volume without confounding effects of between-scan shifts in voxel intensity. In eight patients with probable AD and eight age-matched normal control subjects, the IPCA was comparable to the established but partly manual digital subtraction (DS) method in characterizing annual rates of whole-brain atrophy: resulting rates were correlated (Spearman rank correlation = 0.94, P < 0.0005) and comparable in distinguishing probable AD from normal aging (IPCA-detected atrophy rates: 2.17 +/- 0.52% per year in the patients vs. 0.41 +/- 0.22% per year in the controls [Wilcoxon-Mann-Whitney test P = 7.8 x 10(-4)]; DS-detected atrophy rates: 3.51 +/- 1.31% per year in the patients vs. 0.48 +/- 0.29% per year in the controls [P = 7.8 x 10(-4)]). The IPCA could be used in tracking the progression of AD, evaluating the disease-modifying effects of putative treatments, and investigating the course of other normal and pathological changes in brain morphology.

We compared voxel-based morphometry (VBM) with independent accurate region-of-interest (ROI) measurements of temporal lobe structures in order to validate the usefulness of this fully automated and unbiased technique in Alzheimer's disease (AD) and semantic dementia (SD). In AD, ROI analyses appear more sensitive to volume loss in the amygdalae, whereas VBM analyses appear more sensitive to right middle temporal gyrus and regional hippocampal volume loss. In SD, ROI analyses appear more sensitive to left middle and inferior temporal gyrus volume loss, whereas VBM appears more sensitive to regional hippocampal volume loss. In addition the significance of volume reductions was generally less in VBM owing to more stringent corrections for multiple comparisons. In conclusion, the automated technique detects a general trend of atrophy similar to that of expertly labeled ROI measurements in AD and SD, although there are discrepancies in the ranking of severity and in the significance of volume reductions that are more marked in AD.

The application of voxel-level three-dimensional registration to serial magnetic resonance imaging (MRI) is described. This fluid registration determines deformation fields modeling brain change, which are consistent with a model describing a viscous fluid. The objective was to validate the measurement of hippocampal volumetric change by fluid registration in Alzheimer's disease (AD) against current methodologies. The hippocampus was chosen for this study because it is difficult to measure reproducibly by manual segmentation and is widely studied; however, the technique is applicable to any structure which can be delineated on a scan. First, suitable values for the viscosity-body-force-ratio, alpha (0.01), and the number of iterations (300), were established and the convergence, repeatability, linearity, and accuracy investigated and compared with expert manual segmentation. A simple model of hippocampal atrophy was used to compare simulated volumetric change against that obtained by fluid registration. Finally the serial segmentation was compared with the current gold standard technique-expert human labeling with a volume repeatability of approximately 4%-in 27 subjects (15 normal controls, 12 clinically diagnosed with Alzheimer's disease). The scan-rescan volumetric consistency of serial segmentation by fluid-registration was shown to be superior to human serial segmentors ( approximately 2%). The mean absolute volume difference between fluid and manual segmentation was 0.7%. Fluid registration has potential importance for tracking longitudinal structural changes in brain particularly in the context of the clinical trial where large numbers of subjects may have multiple MR scans., (Copyright 2001 Academic Press.)

Alzheimer's disease is increasingly seen as an heterogeneous disorder with a variety of molecular pathologies converging on a final common pathway of abnormal amyloid deposition and tau phosphorylation. These result in the appearance of the senile plaques and neurofibrillary tangles and in the subsequent development of a cortical dementia with a prominent memory deficit, reflecting the regional distribution of pathology. Age and mode of onset, additional neurological features and family history have all been used as a basis for classification. A family history has proved most robust with the identification of three genetic loci associated with autosomal dominant familial Alzheimer's disease (FAD). Genetically defined pedigrees are important for exploring the relationships between specific molecular pathology and clinical phenotype and, by following at risk individuals, identifying the earliest features.