Gerard R. Ridgway

Permutation inference for the general linear model.

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm.

Fast free-form deformation using graphics processing units

A large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our approach we perform registration of T1-weighted MR images in less than 1 min and show the same level of accuracy as a classical serial implementation when performing segmentation propagation. This technology could be of significant utility in time-critical applications such as image-guided interventions, or in the processing of large data sets.

Head size, age and gender adjustment in MRI studies: a necessary nuisance?

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.

Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease

Volume and change in volume of the hippocampus are both important markers of Alzheimers 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 Alzheimers 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.

Ten simple rules for reporting voxel-based morphometry studies.

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.

Apparent Fibre Density: a novel measure for the analysis of diffusion-weighted magnetic resonance images.

This article proposes a new measure called Apparent Fibre Density (AFD) for the analysis of high angular resolution diffusion-weighted images using higher-order information provided by fibre orientation distributions (FODs) computed using spherical deconvolution. AFD has the potential to provide specific information regarding differences between populations by identifying not only the location, but also the orientations along which differences exist. In this work, analytical and numerical Monte-Carlo simulations are used to support the use of the FOD amplitude as a quantitative measure (i.e. AFD) for population and longitudinal analysis. To perform robust voxel-based analysis of AFD, we present and evaluate a novel method to modulate the FOD to account for changes in fibre bundle cross-sectional area that occur during spatial normalisation. We then describe a novel approach for statistical analysis of AFD that uses cluster-based inference of differences extended throughout space and orientation. Finally, we demonstrate the capability of the proposed method by performing voxel-based AFD comparisons between a group of Motor Neurone Disease patients and healthy control subjects. A significant decrease in AFD was detected along voxels and orientations corresponding to both the corticospinal tract and corpus callosal fibres that connect the primary motor cortices. In addition to corroborating previous findings in MND, this study demonstrates the clear advantage of using this type of analysis by identifying differences along single fibre bundles in regions containing multiple fibre populations.

Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration

Background: Frontotemporal lobar degeneration (FTLD) is a clinically, genetically, and pathologically heterogeneous neurodegenerative disorder. Two subtypes commonly present with a language disorder: semantic dementia (SemD) and progressive nonfluent aphasia (PNFA). Methods: Patients meeting consensus criteria for PNFA and SemD who had volumetric MRI of sufficient quality to allow cortical thickness analysis were recruited from a tertiary referral clinic: 44 (11 pathologically confirmed) patients with SemD and 32 (4 pathologically confirmed) patients with PNFA and 29 age-matched and gender-matched healthy controls were recruited. Cortical thickness analysis was performed using the Freesurfer software tools. Results: Patients with SemD had significant cortical thinning in the left temporal lobe, particularly temporal pole, entorhinal cortex, and parahippocampal, fusiform, and inferior temporal gyri. A similar but less extensive pattern of loss was seen in the right temporal lobe and (with increasing severity) also in left orbitofrontal, inferior frontal, insular, and cingulate cortices. Patients with PNFA had involvement particularly of the left superior temporal lobe, inferior frontal lobe, and insula, and (with increasing severity) other areas in the left frontal, lateral temporal, and anterior parietal lobes. Similar patterns were seen in the pathologically confirmed cases. Patterns of cortical thinning differed between groups: SemD had significantly more cortical thinning in the temporal lobes bilaterally while PNFA had significantly more thinning in the frontal and parietal lobes. Conclusions: The language variants of frontotemporal lobar degeneration have distinctive and significantly different patterns of cortical thinning. Increasing disease severity is associated with spread of cortical thinning and the pattern of spread is consistent with progression of clinical deficits.

Progressive logopenic/phonological aphasia: Erosion of the language network

The primary progressive aphasias (PPA) are paradigmatic disorders of language network breakdown associated with focal degeneration of the left cerebral hemisphere. Here we addressed brain correlates of PPA in a detailed neuroanatomical analysis of the third canonical syndrome of PPA, logopenic/phonological aphasia (LPA), in relation to the more widely studied clinico-anatomical syndromes of semantic dementia (SD) and progressive nonfluent aphasia (PNFA). 32 PPA patients (9 SD, 14 PNFA, 9 LPA) and 18 cognitively normal controls had volumetric brain MRI with regional volumetry, cortical thickness, grey and white matter voxel-based morphometry analyses. Five of nine patients with LPA had cerebrospinal fluid biomarkers consistent with Alzheimer (AD) pathology (AD-PPA) and 2/9 patients had progranulin (GRN) mutations (GRN-PPA). The LPA group had tissue loss in a widespread left hemisphere network. Compared with PNFA and SD, the LPA group had more extensive involvement of grey matter in posterior temporal and parietal cortices and long association white matter tracts. Overlapping but distinct networks were involved in the AD-PPA and GRN-PPA subgroups, with more anterior temporal lobe involvement in GRN-PPA. The importance of these findings is threefold: firstly, the clinico-anatomical entity of LPA has a profile of brain damage that is complementary to the network-based disorders of SD and PNFA; secondly, the core phonological processing deficit in LPA is likely to arise from temporo-parietal junction damage but disease spread occurs through the dorsal language network (and in GRN-PPA, also the ventral language network); and finally, GRN mutations provide a specific molecular substrate for language network dysfunction.

Distinct profiles of brain atrophy in frontotemporal lobar degeneration caused by progranulin and tau mutations.

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.

Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer's disease

A significant minority of Alzheimers disease patients present with posterior cortical atrophy (PCA). PCA is characterized by visuospatial and visuoperceptual deficits, and relatively preserved memory, whereas patients with typical Alzheimers disease (tAD) mostly present with early episodic memory deficits. We used two unbiased image analysis techniques to assess atrophy patterns in 48 PCA, 30 tAD, and 50 healthy controls. FreeSurfer was used to measure cortical thickness, and volumetric grey matter differences were assessed using voxel-based morphometry (VBM). Both PCA and tAD showed widespread reductions compared with controls using both techniques. Direct comparison of PCA and tAD revealed thinner cortex predominantly in the right superior parietal lobe in the PCA group compared with tAD, whereas the tAD group showed thinning in the left entorhinal cortex compared with PCA. Similar results were obtained in the VBM analysis. These distinct patterns of atrophy may have diagnostic utility. In a clinical context, a relatively spared medial temporal lobe in the presence of posterior parietal atrophy may imply PCA, and should not discount AD.