Guy B. Williams

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. We employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. Though autocorrelation modeling in AFNI is not perfect, its performance is much higher than the performance of autocorrelation modeling in FSL and SPM. The residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. Our results show superior performance of SPM’s alternative pre-whitening: FAST, over SPM’s default. The reliability of task fMRI studies would increase with more accurate autocorrelation modeling. Furthermore, reliability could increase if the packages provided diagnostic plots. This way the investigator would be aware of pre-whitening problems.

This review summarises the findings and applications from neuroimaging studies in dementia with Lewy bodies (DLB), highlighting key differences between DLB and other subtypes of dementia. We also discuss the increasingly important role of imaging biomarkers in differential diagnosis and outline promising areas for future research in DLB. DLB shares common clinical, neuropsychological and pathological features with Parkinson’s disease dementia and other dementia subtypes, such as Alzheimer’s disease. Despite the development of consensus diagnostic criteria, the sensitivity for differential diagnosis of DLB in clinical practice remains low and many DLB patients will be misdiagnosed. The importance of developing accurate imaging markers in dementia is highlighted by the potential for treatments targeting specific molecular abnormalities as well as the responsiveness to cholinesterase inhibitors and marked neuroleptic sensitivity of DLB. We review various brain imaging techniques that have been applied to investigate DLB, including the characteristic nigrostriatal degeneration in DLB using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) tracers. Dopamine transporter loss has proven to reliably differentiate DLB from other dementias and has been incorporated into the revised clinical diagnostic criteria for DLB. To date, this remains the 'gold standard' for diagnostic imaging of DLB. Regional cerebral blood flow, 18 F-fluorodeoxygluclose-PET and SPECT have also identified marked deficits in the occipital regions with relative sparing of the medial temporal lobe when compared to Alzheimer’s disease. In addition, structural, diffusion, and functional magnetic resonance imaging techniques have shown alterations in structure, white matter integrity, and functional activity in DLB. We argue that the multimodal identification of DLB-specific biomarkers has the potential to improve ante-mortem diagnosis and contribute to our understanding of the pathological background of DLB and its progression.

Improved registration of grey matter segments in SPM has been achieved with the DARTEL algorithm. Previous work from our group suggested, however, that such improvements may not translate to studies of clinical groups. To address the registration issue in atrophic brains, this paper relaxed the condition of diffeomorphism, central to DARTEL, and made use of a viscous fluid registration model with limited regularisation constraints to register the modulated grey matter probability maps to an intra-population template. Quantitative analysis of the registration results after the additional viscous fluid step showed no worsening of co-localisation of fiducials compared to DARTEL or unified segmentation methods, and the resulting voxel based morphometry (VBM) analyses were able to better identify atrophic regions and to produce results with fewer apparent false positives. DARTEL showed great sensitivity to atrophy, but the resulting VBM maps presented broad, amorphous regions of significance that are hard to interpret. We propose that the condition of diffeomorphism is not necessary for basic VBM studies in atrophic populations, but also that it has disadvantages that must be taken into consideration before a study. The presented viscous fluid registration method is proposed for VBM studies to enhance sensitivity and localizing power.

Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD*), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2* = 1/T2*). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2* (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.

Diffusion MR data sets produce large numbers of streamlines which are hard to visualize, interact with, and interpret in a clinically acceptable time scale, despite numerous proposed approaches. As a solution we present a simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds. Each QB cluster can be represented by a single centroid streamline; collectively these centroid streamlines can be taken as an effective representation of the tractography. We provide a number of tests to show how the QB reduction has good consistency and robustness. We show how the QB reduction can help in the search for similarities across several subjects.

The network activated during normal route learning shares considerable homology with the network of degeneration in the earliest symptomatic stages of Alzheimer's disease (AD). This inspired the virtual route learning test (VRLT) in which patients learn routes in a virtual reality environment. This study investigated the neural basis of VRLT performance in AD to test whether impairment was underpinned by a network or by the widely held explanation of hippocampal degeneration. VRLT score in a mild AD cohort was regressed against gray matter (GM) density and diffusion tensor metrics of white matter (WM) (n = 30), and, cerebral glucose metabolism (n = 26), using a mass univariate approach. GM density and cerebral metabolism were then submitted to a multivariate analysis [support vector regression (SVR)] to examine whether there was a network associated with task performance. Univariate analyses of GM density, metabolism and WM axial diffusion converged on the vicinity of the retrosplenial/posterior cingulate cortex, isthmus and, possibly, hippocampal tail. The multivariate analysis revealed a significant, right hemisphere-predominant, network level correlation with cerebral metabolism; this comprised areas common to both activation in normal route learning and early degeneration in AD (retrosplenial and lateral parietal cortices). It also identified right medio-dorsal thalamus (part of the limbic-diencephalic hypometabolic network of early AD) and right caudate nucleus (activated during normal route learning). These results offer strong evidence that topographical memory impairment in AD relates to damage across a network, in turn offering complimentary lesion evidence to previous studies in healthy volunteers for the neural basis of topographical memory. The results also emphasize that structures beyond the mesial temporal lobe (MTL) contribute to memory impairment in AD—it is too simplistic to view memory impairment in AD as a synonym for hippocampal degeneration.

An enhanced method for correction of image distortion due to B₀‐field inhomogeneities in echo planar imaging (EPI) is presented. The algorithm is based on the measurement of the point spread function (PSF) associated with each image voxel using a reference scan. The expected distortion map in the phase encode direction is then estimated using a nonparametric inference algorithm known as Gaussian process modeling. The algorithm is shown to be robust to the presence of regions of low signal‐to‐noise in the image and large inhomogeneities. Magn Reson Med, 2008.