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.

Posterior cortical atrophy (PCA) is a neurodegenerative syndrome that is characterized by a progressive decline in visuospatial, visuoperceptual, literacy and praxic skills. The progressive neurodegeneration affecting parietal, occipital and occipito-temporal cortices which underlies PCA is attributable to Alzheimer's disease (AD) in the majority of patients. However, alternative underlying aetiologies including Dementia with Lewy Bodies (DLB), corticobasal degeneration (CBD) and prion disease have also been identified, and not all PCA patients have atrophy on clinical imaging. This heterogeneity has led to diagnostic and terminological inconsistencies, caused difficulty comparing studies from different centres, and limited the generalizability of clinical trials and investigations of factors driving phenotypic variability. Significant challenges remain in identifying the factors associated with both the selective vulnerability of posterior cortical regions and the young age of onset seen in PCA. Greater awareness of the syndrome and agreement over the correspondence between syndrome-and disease-level classifications are required in order to improve diagnostic accuracy, research study design and clinical management.

IntroductionA classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings.MethodsConsensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA.ResultsA three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum.DiscussionThere was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work.

Amyloid-β, a hallmark of Alzheimer's disease, begins accumulating up to two decades before the onset of dementia, and can be detected in vivo applying amyloid-β positron emission tomography tracers such as carbon-11-labelled Pittsburgh compound-B. A variety of thresholds have been applied in the literature to define Pittsburgh compound-B positron emission tomography positivity, but the ability of these thresholds to detect early amyloid-β deposition is unknown, and validation studies comparing Pittsburgh compound-B thresholds to post-mortem amyloid burden are lacking. In this study we first derived thresholds for amyloid positron emission tomography positivity using Pittsburgh compound-B positron emission tomography in 154 cognitively normal older adults with four complementary approaches: (i) reference values from a young control group aged between 20 and 30 years; (ii) a Gaussian mixture model that assigned each subject a probability of being amyloid-β-positive or amyloid-β-negative based on Pittsburgh compound-B index uptake; (iii) a k-means cluster approach that clustered subjects into amyloid-β-positive or amyloid-β-negative based on Pittsburgh compound-B uptake in different brain regions (features); and (iv) an iterative voxel-based analysis that further explored the spatial pattern of early amyloid-β positron emission tomography signal. Next, we tested the sensitivity and specificity of the derived thresholds in 50 individuals who underwent Pittsburgh compound-B positron emission tomography during life and brain autopsy (mean time positron emission tomography to autopsy 3.1 ± 1.8 years). Amyloid at autopsy was classified using Consortium to Establish a Registry for Alzheimer's Disease (CERAD) criteria, unadjusted for age. The analytic approaches yielded low thresholds (standard uptake value ratiolow = 1.21, distribution volume ratiolow = 1.08) that represent the earliest detectable Pittsburgh compound-B signal, as well as high thresholds (standard uptake value ratiohigh = 1.40, distribution volume ratiohigh = 1.20) that are more conservative in defining Pittsburgh compound-B positron emission tomography positivity. In voxel-wise contrasts, elevated Pittsburgh compound-B retention was first noted in the medial frontal cortex, then the precuneus, lateral frontal and parietal lobes, and finally the lateral temporal lobe. When compared to post-mortem amyloid burden, low proposed thresholds were more sensitive than high thresholds (sensitivities: distribution volume ratiolow 81.0%, standard uptake value ratiolow 83.3%; distribution volume ratiohigh 61.9%, standard uptake value ratiohigh 62.5%) for CERAD moderate-to-frequent neuritic plaques, with similar specificity (distribution volume ratiolow 95.8%; standard uptake value ratiolow, distribution volume ratiohigh and standard uptake value ratiohigh 100.0%). A receiver operator characteristic analysis identified optimal distribution volume ratio (1.06) and standard uptake value ratio (1.20) thresholds that were nearly identical to the a priori distribution volum...

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ObjectiveTo develop a visual rating scale for posterior atrophy (PA) assessment and to analyse whether this scale aids in the discrimination between Alzheimer’s disease (AD) and other dementias.MethodsMagnetic resonance imaging of 118 memory clinic patients were analysed for PA (range 0–3), medial temporal lobe atrophy (MTA) (range 0–4) and global cortical atrophy (range 0–3) by different raters. Weighted-kappas were calculated for inter- and intra-rater agreement. Relationships between PA and MTA with the MMSE and age were estimated with linear-regression analysis.ResultsIntra-rater agreement ranged between 0.93 and 0.95 and inter-rater agreement between 0.65 and 0.84. Mean PA scores were higher in AD compared to controls (1.6 ± 0.9 and 0.6 ± 0.7, p < 0.01), and other dementias (0.8 ± 0.8, p < 0.01). PA was not associated with age compared to MTA (B = 1.1 (0.8) versus B = 3.1 (0.7), p < 0.01)). PA and MTA were independently negatively associated with the MMSE (B = −1.6 (0.5), p < 0.01 versus B = −1.4 (0.5), p < 0.01).ConclusionThis robust and reproducible scale for PA assessment conveys independent information in a clinical setting and may be useful in the discrimination of AD from other dementias.

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.

The factors driving clinical heterogeneity in Alzheimer's disease are not well understood. This study assessed the relationship between amyloid deposition, glucose metabolism and clinical phenotype in Alzheimer's disease, and investigated how these relate to the involvement of functional networks. The study included 17 patients with early-onset Alzheimer's disease (age at onset <65 years), 12 patients with logopenic variant primary progressive aphasia and 13 patients with posterior cortical atrophy [whole Alzheimer's disease group: age = 61.5 years (standard deviation 6.5 years), 55% male]. Thirty healthy control subjects [age = 70.8 (3.3) years, 47% male] were also included. Subjects underwent positron emission tomography with (11)C-labelled Pittsburgh compound B and (18)F-labelled fluorodeoxyglucose. All patients met National Institute on Ageing-Alzheimer's Association criteria for probable Alzheimer's disease and showed evidence of amyloid deposition on (11)C-labelled Pittsburgh compound B positron emission tomography. We hypothesized that hypometabolism patterns would differ across variants, reflecting involvement of specific functional networks, whereas amyloid patterns would be diffuse and similar across variants. We tested these hypotheses using three complimentary approaches: (i) mass-univariate voxel-wise group comparison of (18)F-labelled fluorodeoxyglucose and (11)C-labelled Pittsburgh compound B; (ii) generation of covariance maps across all subjects with Alzheimer's disease from seed regions of interest specifically atrophied in each variant, and comparison of these maps to functional network templates; and (iii) extraction of (11)C-labelled Pittsburgh compound B and (18)F-labelled fluorodeoxyglucose values from functional network templates. Alzheimer's disease clinical groups showed syndrome-specific (18)F-labelled fluorodeoxyglucose patterns, with greater parieto-occipital involvement in posterior cortical atrophy, and asymmetric involvement of left temporoparietal regions in logopenic variant primary progressive aphasia. In contrast, all Alzheimer's disease variants showed diffuse patterns of (11)C-labelled Pittsburgh compound B binding, with posterior cortical atrophy additionally showing elevated uptake in occipital cortex compared with early-onset Alzheimer's disease. The seed region of interest covariance analysis revealed distinct (18)F-labelled fluorodeoxyglucose correlation patterns that greatly overlapped with the right executive-control network for the early-onset Alzheimer's disease region of interest, the left language network for the logopenic variant primary progressive aphasia region of interest, and the higher visual network for the posterior cortical atrophy region of interest. In contrast, (11)C-labelled Pittsburgh compound B covariance maps for each region of interest were diffuse. Finally, (18)F-labelled fluorodeoxyglucose was similarly reduced in all Alzheimer's disease variants in the dorsal and left ventral default mode network, whereas significant differences were found in t...