BACKGROUND

The order and magnitude of pathologic processes in Alzheimer’s disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer’s disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease.

METHODS

In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant’s age at baseline assessment and the parent’s age at the onset of symptoms of Alzheimer’s disease to calculate the estimated years from expected symptom onset (age of the participant minus parent’s age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes.

RESULTS

Concentrations of amyloid-beta (Aβ)42 in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini–Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset.

CONCLUSIONS

We found that autosomal dominant Alzheimer’s disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer’s disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer’s disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.)

Whole brain extraction is an important pre-processing step in neuro-image 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 sub-optimal 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 T1-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-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.

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.

Neuronal intermediate filament inclusion disease and atypical frontotemporal lobar degeneration are rare diseases characterized by ubiquitin-positive inclusions lacking transactive response DNA-binding protein-43 and tau. Recently, mutations in the fused in sarcoma gene have been shown to cause familial amyotrophic lateral sclerosis and fused in sarcoma-positive neuronal inclusions have subsequently been demonstrated in neuronal intermediate filament inclusion disease and atypical frontotemporal lobar degeneration with ubiquitinated inclusions. Here we provide clinical, imaging, morphological findings, as well as genetic and biochemical data in 14 fused in sarcoma proteinopathy cases. In this cohort, the age of onset was variable but included cases of young-onset disease. Patients with atypical frontotemporal lobar degeneration with ubiquitinated inclusions all presented with behavioural variant frontotemporal dementia, while the clinical presentation in neuronal intermediate filament inclusion disease was more heterogeneous, including cases with motor neuron disease and extrapyramidal syndromes. Neuroimaging revealed atrophy of the frontal and anterior temporal lobes as well as the caudate in the cases with atypical frontotemporal lobar degeneration with ubiquitinated inclusions, but was more heterogeneous in the cases with neuronal intermediate filament inclusion disease, often being normal to visual inspection early on in the disease. The distribution and severity of fused in sarcoma-positive neuronal cytoplasmic inclusions, neuronal intranuclear inclusions and neurites were recorded and fused in sarcoma was biochemically analysed in both subgroups. Fused in sarcoma-positive neuronal cytoplasmic and intranuclear inclusions were found in the hippocampal granule cell layer in variable numbers. Cortical fused in sarcoma-positive neuronal cytoplasmic inclusions were often ‘Pick body-like’ in neuronal intermediate filament inclusion disease, and annular and crescent-shaped inclusions were seen in both conditions. Motor neurons contained variable numbers of compact, granular or skein-like cytoplasmic inclusions in all fused in sarcoma-positive cases in which brainstem and spinal cord motor neurons were available for study (five and four cases, respectively). No fused in sarcoma mutations were found in any cases. Biochemically, two major fused in sarcoma species were found and shown to be more insoluble in the atypical frontotemporal lobar degeneration with ubiquitinated inclusions subgroup compared with neuronal intermediate filament inclusion disease. There is considerable overlap and also significant differences in fused in sarcoma-positive pathology between the two subgroups, suggesting they may represent a spectrum of the same disease. The co-existence of fused in sarcoma-positive inclusions in both motor neurons and extramotor cerebral structures is a characteristic finding in sporadic fused in sarcoma proteinopathies, indicating a multisystem disorder.

Background

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

Methods and Results

The authors of this position paper propose a path toward this goal. The steps include: 1) Establish and empower an oversight board to manage and assess the effort, 2) Adopt the standardized definition of anatomic hippocampal boundaries on MRI arising from the EADC-ADNI hippocampal harmonization effort as a Reference Standard, 3) Establish a scientifically appropriate, publicly available Reference Standard Dataset based on manual delineation of the hippocampus in an appropriate sample of subjects (ADNI), and 4) Define minimum technical and prognostic performance metrics for validation of new measurement techniques using the Reference Standard Dataset as a benchmark.

Conclusions

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

Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD, and in prodromal and even presymptomatic states that can help rule-in the AD pathophysiological process. No one imaging modality can serve all purposes as each have unique strengths and weaknesses. These modalities and their particular utilities are discussed in this article. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies.

Various neuroimaging modalities (e.g., MRI and PET) have shown characteristic changes in the brains of patients with Alzheimer disease. Identifying imaging biomarkers will facilitate diagnosis, disease staging, and drug development.

The National Institute on Aging and the Alzheimer’s Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer’s disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings.

An expanded hexanucleotide repeat in the C9ORF72 gene has recently been identified as a major cause of familial frontotemporal lobar degeneration and motor neuron disease, including cases previously identified as linked to chromosome 9. Here we present a detailed retrospective clinical, neuroimaging and histopathological analysis of a C9ORF72 mutation case series in relation to other forms of genetically determined frontotemporal lobar degeneration ascertained at a specialist centre. Eighteen probands (19 cases in total) were identified, representing 35% of frontotemporal lobar degeneration cases with identified mutations, 36% of cases with clinical evidence of motor neuron disease and 7% of the entire cohort. Thirty-three per cent of these C9ORF72 cases had no identified relevant family history. Families showed wide variation in clinical onset (43–68 years) and duration (1.7–22 years). The most common presenting syndrome (comprising a half of cases) was behavioural variant frontotemporal dementia, however, there was substantial clinical heterogeneity across the C9ORF72 mutation cohort. Sixty per cent of cases developed clinical features consistent with motor neuron disease during the period of follow-up. Anxiety and agitation and memory impairment were prominent features (between a half to two-thirds of cases), and dominant parietal dysfunction was also frequent. Affected individuals showed variable magnetic resonance imaging findings; however, relative to healthy controls, the group as a whole showed extensive thinning of frontal, temporal and parietal cortices, subcortical grey matter atrophy including thalamus and cerebellum and involvement of long intrahemispheric, commissural and corticospinal tracts. The neuroimaging profile of the C9ORF72 expansion was significantly more symmetrical than progranulin mutations with significantly less temporal lobe involvement than microtubule-associated protein tau mutations. Neuropathological examination in six cases with C9ORF72 mutation from the frontotemporal lobar degeneration series identified histomorphological features consistent with either type A or B TAR DNA-binding protein-43 deposition; however, p62-positive (in excess of TAR DNA-binding protein-43 positive) neuronal cytoplasmic inclusions in hippocampus and cerebellum were a consistent feature of these cases, in contrast to the similar frequency of p62 and TAR DNA-binding protein-43 deposition in 53 control cases with frontotemporal lobar degeneration–TAR DNA-binding protein. These findings corroborate the clinical importance of the C9ORF72 mutation in frontotemporal lobar degeneration, delineate phenotypic and neuropathological features that could help to guide genetic testing, and suggest hypotheses for elucidating the neurobiology of a culprit subcortical network.

Short echo time localized proton magnetic resonance spectroscopy provides quantification of brain metabolites, including N-acetyl-aspartate, myo-inositol, creatine/phosphocreatine and choline-containing compounds, which may be useful biomarkers for monitoring Alzheimer's disease. We aimed to quantify the rate of metabolite change in Alzheimer's disease, to assess factors influencing changes and to investigate the potential for serial magnetic resonance spectroscopy as an Alzheimer's disease trial biomarker. A total of 42 patients and 22 controls each had up to six magnetic resonance spectroscopy examinations over a 2-year period, using a midline posterior cingulate single-voxel point resolved spectroscopy sequence (1.5 T; time to repetition = 2000 ms; echo time = 30 ms; 192 averages). Metabolite ratios N-acetyl-aspartate:creatine/phosphocreatine, choline-containing compounds:creatine/phosphocreatine, and myo-inositol:creatine/phosphocreatine were measured using online software (PROBE-Q) and the N-acetyl-aspartate:myo-inositol ratio was derived. Baseline ratios were compared between patients and controls. A linear mixed model was used to quantify longitudinal changes and extended to assess the effect of age, disease severity and baseline use of acetylcholinesterase inhibitors. Patients and controls were matched for age (patients: 68.9 ± 7.2 years; controls: 69.1 ± 6.7 years); 71% of the patients were on acetylcholinesterase inhibitors at baseline; mean Mini-Mental State Examination for patients was 19.4 ± 4.1. A total of 307 spectra were acquired. In cross-sectional analyses, patients were significantly different from controls for N-acetyl-aspartate:creatine/phosphocreatine (11% lower, P < 0.001), N-acetyl-aspartate:myo-inositol (24% lower, P < 0.001), and myo-inositol:creatine/phosphocreatine (17% higher, P < 0.001). After adjustment for N-acetyl-aspartate:myo-inositol, none of the other variables differed significantly. In patients there was significant decline in N-acetyl-aspartate:creatine/phosphocreatine (mean: 2.2%/year; 95% confidence interval: 0.9–3.5) and N-acetyl-aspartate:myo-inositol (mean: 3.7%/year; 95% confidence interval: 1.7–5.7), with no evidence for influence by age, disease severity or acetylcholinesterase inhibitor use. There was significant excess decline in patients compared with controls only in N-acetyl-aspartate:myo-inositol (mean: 3.6%/year; 95% confidence interval: 0.8–6.4; P = 0.014). Between-subject standard deviation for N-acetyl-aspartate:myo-inositol was 0% for controls and 3.5%/year for patients; within-subject standard deviation for a 1 year, two-time-point study was 9.2%/year for both patients and controls. These results confirm that magnetic resonance spectroscopy can be used to quantify excess metabolite decline in Alzheimer's disease, which may provide a useful measure of disease progression. We found no evidence that age, disease severity or acetylcholinesterase inhibitor use influenced rate of decline, although numbers were small. The substantial variability in longitudinal measurements that drives sample size requirements is principally within-subject and technique related: technical developments to reduce this variability may make serial magnetic resonance spectroscopy a viable biomarker in clinical trials for Alzheimer's disease.

Relating clinical symptoms to neuroanatomical profiles of brain damage and ultimately to tissue pathology is a key challenge in the field of neurodegenerative disease and particularly relevant to the heterogeneous disorders that comprise the frontotemporal lobar degeneration spectrum. Here we present a retrospective analysis of clinical, neuropsychological and neuroimaging (volumetric and voxel-based morphometric) features in a pathologically ascertained cohort of 95 cases of frontotemporal lobar degeneration classified according to contemporary neuropathological criteria. Forty-eight cases (51%) had TDP-43 pathology, 42 (44%) had tau pathology and five (5%) had fused-in-sarcoma pathology. Certain relatively specific clinicopathological associations were identified. Semantic dementia was predominantly associated with TDP-43 type C pathology; frontotemporal dementia and motoneuron disease with TDP-43 type B pathology; young-onset behavioural variant frontotemporal dementia with FUS pathology; and the progressive supranuclear palsy syndrome with progressive supranuclear palsy pathology. Progressive non-fluent aphasia was most commonly associated with tau pathology. However, the most common clinical syndrome (behavioural variant frontotemporal dementia) was pathologically heterogeneous; while pathologically proven Pick's disease and corticobasal degeneration were clinically heterogeneous, and TDP-43 type A pathology was associated with similar clinical features in cases with and without progranulin mutations. Volumetric magnetic resonance imaging, voxel-based morphometry and cluster analyses of the pathological groups here suggested a neuroanatomical framework underpinning this clinical and pathological diversity. Frontotemporal lobar degeneration-associated pathologies segregated based on their cerebral atrophy profiles, according to the following scheme: asymmetric, relatively localized (predominantly temporal lobe) atrophy (TDP-43 type C); relatively symmetric, relatively localized (predominantly temporal lobe) atrophy (microtubule-associated protein tau mutations); strongly asymmetric, distributed atrophy (Pick's disease); relatively symmetric, predominantly extratemporal atrophy (corticobasal degeneration, fused-in-sarcoma pathology). TDP-43 type A pathology was associated with substantial individual variation; however, within this group progranulin mutations were associated with strongly asymmetric, distributed hemispheric atrophy. We interpret the findings in terms of emerging network models of neurodegenerative disease: the neuroanatomical specificity of particular frontotemporal lobar degeneration pathologies may depend on an interaction of disease-specific and network-specific factors.

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 12 months (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 SNT (Surgical Navigation Technologies) 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≤0.03). MAPS-HBSI required the lowest sample sizes (68 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.

Autosomal-dominant Alzheimer's disease has provided significant understanding of the pathophysiology of Alzheimer's disease. The present review summarizes clinical, pathological, imaging, biochemical, and molecular studies of autosomal-dominant Alzheimer's disease, highlighting the similarities and differences between the dominantly inherited form of Alzheimer's disease and the more common sporadic form of Alzheimer's disease. Current developments in autosomal-dominant Alzheimer's disease are presented, including the international Dominantly Inherited Alzheimer Network and this network's initiative for clinical trials. Clinical trials in autosomal-dominant Alzheimer's disease may test the amyloid hypothesis, determine the timing of treatment, and lead the way to Alzheimer's disease prevention.

Functions of the ADNI MRI core fall into three categories: (1) those of the central MRI core lab at Mayo Clinic, Rochester, Minnesota, needed to generate high quality MRI data in all subjects at each time point; (2) those of the funded ADNI MRI core imaging analysis groups responsible for analyzing the MRI data, and (3) the joint function of the entire MRI core in designing and problem solving MR image acquisition, pre-processing and analyses methods. The primary objective of ADNI was and continues to be improving methods for clinical trials in Alzheimer's disease. Our approach to the present (“ADNI-GO”) and future (“ADNI-2”, if funded) MRI protocol will be to maintain MRI methodological consistency in previously enrolled “ADNI-1” subjects who are followed longitudinally in ADNI-GO and ADNI-2. We will modernize and expand the MRI protocol for all newly enrolled ADNI-GO and ADNI-2 subjects. All newly enrolled subjects will be scanned at 3T with a core set of three sequence types: 3D T1-weighted volume, FLAIR, and a long TE gradient echo volumetric acquisition for micro hemorrhage detection. In addition to this core ADNI-GO and ADNI-2 protocol, we will perform vendor specific pilot sub-studies of arterial spin labeling perfusion, resting state functional connectivity and diffusion tensor imaging. One each of these sequences will be added to the core protocol on systems from each MRI vendor. These experimental sub-studies are designed to demonstrate the feasibility of acquiring useful data in a multi-center (but single vendor) setting for these three emerging MRI applications.

Introduction

Tinnitus and hyperacusis are common symptoms of excessive auditory perception in the general population; however, their anatomical substrates and disease associations continue to be defined. Patients with semantic dementia (SemD) frequently report tinnitus and hyperacusis but the significance and basis for these symptoms have not been elucidated.

Methods

43 patients with a diagnosis of SemD attending a specialist cognitive disorders clinic were retrospectively studied. 14 patients (32% of the cohort) reported at least moderately severe chronic auditory symptoms: seven had tinnitus and a further seven had hyperacusis, and all had brain MRI while symptomatic. MRI data from SemD patients with and without auditory symptoms were compared using voxel based morphometry in order to identify neuroanatomical associations of tinnitus and hyperacusis.

Results

Compared with SemD patients with no history of auditory symptoms, patients with tinnitus or hyperacusis had relative preservation of grey matter in the posterior superior temporal lobe and reduced grey matter in the orbitofrontal cortex and medial geniculate nucleus.

Conclusions

Tinnitus and hyperacusis may be a significant issue in SemD. Neuroanatomical evidence in SemD supports previous work implicating a distributed cortico-subcortical auditory and limbic network in the pathogenesis of these abnormal auditory percepts.

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.

Structural imaging based on magnetic resonance is an integral part of the clinical assessment of patients with suspected Alzheimer dementia. Prospective data on the natural history of change in structural markers from preclinical to overt stages of Alzheimer disease are radically changing how the disease is conceptualized, and will influence its future diagnosis and treatment. Atrophy of medial temporal structures is now considered to be a valid diagnostic marker at the mild cognitive impairment stage. Structural imaging is also included in diagnostic criteria for the most prevalent non-Alzheimer dementias, reflecting its value in differential diagnosis. In addition, rates of whole-brain and hippocampal atrophy are sensitive markers of neurodegeneration, and are increasingly used as outcome measures in trials of potentially disease-modifying therapies. Large multicenter studies are currently investigating the value of other imaging and nonimaging markers as adjuncts to clinical assessment in diagnosis and monitoring of progression. The utility of structural imaging and other markers will be increased by standardization of acquisition and analysis methods, and by development of robust algorithms for automated assessment.

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.

Hippocampal atrophy rates are useful in both diagnosing and tracking Alzheimer’s disease (AD). However, cohorts and methods used to determine such rates are heterogeneous, leading to differences in reported annualised rates. We performed a meta-analysis of hippocampal atrophy rates in AD patients and matched controls from studies reported in the peer-reviewed literature. Studies reporting longitudinal volume change in hippocampi in AD subjects together with controls were systematically identified and appraised. All authors were contacted either to confirm the results or to provide missing data. Meta-analysis and meta-regression were then performed on this data. Nine studies were included from seven centres, with data from a total of 595 AD and 212 matched controls. Mean (95% CIs) annualised hippocampal atrophy rates were found to be 4.66% (95% CI 3.92, 5.40) for AD subjects and 1.41% (0.52, 2.30) for controls. The difference between AD and control subject in this rate was 3.33% (1.73, 4.94).

A diagnosis of dementia is devastating at any age but diagnosis in younger patients presents a particular challenge. The differential diagnosis is broad as late presentation of metabolic disease is common and the burden of inherited dementia is higher in these patients than in patients with late-onset dementia. The presentation of the common degenerative diseases of late life, such as Alzheimer's disease, can be different when presenting in the fifth or sixth decade. Moreover, many of the young-onset dementias are treatable. The identification of causative genes for many of the inherited degenerative dementias has led to an understanding of the molecular pathology, which is also applicable to later-onset sporadic disease. This understanding offers the potential for future treatments to be tailored to a specific diagnosis of both young-onset and late-onset dementia.

Background

Recently, two large genome wide association studies in Alzheimer disease (AD) have identified variants in three different genes (CLU, PICALM and CR1) as being associated with the risk of developing AD. The strongest association was reported for an intronic single nucleotide polymorphism (SNP) in CLU.

Methodology/Principal Findings

To further characterize this association we have sequenced the coding region of this gene in a total of 495 AD cases and 330 healthy controls. A total of twenty-four variants were found in both cases and controls. For the changes found in more than one individual, the genotypic frequencies were compared between cases and controls. Coding variants were found in both groups (including a nonsense mutation in a healthy subject), indicating that the pathogenicity of variants found in this gene must be carefully evaluated. We found no common coding variant associated with disease. In order to determine if common variants at the CLU locus effect expression of nearby (cis) mRNA transcripts, an expression quantitative loci (eQTL) analysis was performed. No significant eQTL associations were observed for the SNPs previously associated with AD.

Conclusions/Significance

We conclude that common coding variability at this locus does not explain the association, and that there is no large effect of common genetic variability on expression in brain tissue. We surmise that the most likely mechanism underpinning the association is either small effects of genetic variability on resting gene expression, or effects on damage induced expression of the protein.

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.

We assessed the significance and nature of delusions in frontotemporal lobar degeneration (FTLD), an important cause of young-onset dementia with prominent neuropsychiatric features that remain incompletely characterised. The case notes of all patients meeting diagnostic criteria for FTLD attending a tertiary level cognitive disorders clinic over a three year period were retrospectively reviewed and eight patients with a history of delusions were identified. All patients underwent detailed clinical and neuropsychological evaluation and brain MRI. The diagnosis was confirmed pathologically in two cases. The estimated prevalence of delusions was 14 %. Delusions were an early, prominent and persistent feature. They were phenomenologically diverse; however paranoid and somatic delusions were prominent. Behavioural variant FTLD was the most frequently associated clinical subtype and cerebral atrophy was bilateral or predominantly right-sided in most cases. We conclude that delusions may be a clinical issue in FTLD, and this should be explored further in future work.

We investigated progression of atrophy in vivo, in Alzheimer’s disease (AD), and mild cognitive impairment (MCI). We included 64 patients with AD, 44 with MCI and 34 controls with serial MRI examinations (interval 1.8 ± 0.7 years). A nonlinear registration algorithm (fluid) was used to calculate atrophy rates in six regions: frontal, medial temporal, temporal (extramedial), parietal, occipital lobes and insular cortex. In MCI, the highest atrophy rate was observed in the medial temporal lobe, comparable with AD. AD patients showed even higher atrophy rates in the extramedial temporal lobe. Additionally, atrophy rates in frontal, parietal and occipital lobes were increased. Cox proportional hazard models showed that all regional atrophy rates predicted conversion to AD. Hazard ratios varied between 2.6 (95% confidence interval (CI) = 1.1–6.2) for occipital atrophy and 15.8 (95% CI = 3.5–71.8) for medial temporal lobe atrophy. In conclusion, atrophy spreads through the brain with development of AD. MCI is marked by temporal lobe atrophy. In AD, atrophy rate in the extramedial temporal lobe was even higher. Moreover, atrophy rates also accelerated in parietal, frontal, insular and occipital lobes. Finally, in nondemented elderly, medial temporal lobe atrophy was most predictive of progression to AD, demonstrating the involvement of this region in the development of AD.

There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.

We assessed the significance and nature of delusions in frontotemporal lobar degeneration (FTLD), an important cause of young-onset dementia with prominent neuropsychiatric features that remain incompletely characterised. The case notes of all patients meeting diagnostic criteria for FTLD attending a tertiary level cognitive disorders clinic over a three year period were retrospectively reviewed and eight patients with a history of delusions were identified. All patients underwent detailed clinical and neuropsychological evaluation and brain MRI. The diagnosis was confirmed pathologically in two cases. The estimated prevalence of delusions was 14 %. Delusions were an early, prominent and persistent feature. They were phenomenologically diverse; however paranoid and somatic delusions were prominent. Behavioural variant FTLD was the most frequently associated clinical subtype and cerebral atrophy was bilateral or predominantly right-sided in most cases. We conclude that delusions may be a clinical issue in FTLD, and this should be explored further in future work.