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Dive into the research topics where Nick C. Fox is active.

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Featured researches published by Nick C. Fox.


Nature Genetics | 2009

Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease.

Denise Harold; Richard Abraham; Paul Hollingworth; Rebecca Sims; Amy Gerrish; Marian Lindsay Hamshere; Jaspreet Singh Pahwa; Valentina Moskvina; Kimberley Dowzell; Amy Williams; Nicola L. Jones; Charlene Thomas; Alexandra Stretton; Angharad R. Morgan; Simon Lovestone; John Powell; Petroula Proitsi; Michelle K. Lupton; Carol Brayne; David C. Rubinsztein; Michael Gill; Brian A. Lawlor; Aoibhinn Lynch; Kevin Morgan; Kristelle Brown; Peter Passmore; David Craig; Bernadette McGuinness; Stephen Todd; Clive Holmes

We undertook a two-stage genome-wide association study (GWAS) of Alzheimers disease (AD) involving over 16,000 individuals, the most powerful AD GWAS to date. In stage 1 (3,941 cases and 7,848 controls), we replicated the established association with the apolipoprotein E (APOE) locus (most significant SNP, rs2075650, P = 1.8 × 10−157) and observed genome-wide significant association with SNPs at two loci not previously associated with the disease: at the CLU (also known as APOJ) gene (rs11136000, P = 1.4 × 10−9) and 5′ to the PICALM gene (rs3851179, P = 1.9 × 10−8). These associations were replicated in stage 2 (2,023 cases and 2,340 controls), producing compelling evidence for association with Alzheimers disease in the combined dataset (rs11136000, P = 8.5 × 10−10, odds ratio = 0.86; rs3851179, P = 1.3 × 10−9, odds ratio = 0.86).


Journal of Magnetic Resonance Imaging | 2008

The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods

Clifford R. Jack; Matt A. Bernstein; Nick C. Fox; Paul M. Thompson; Gene E. Alexander; Danielle Harvey; Bret Borowski; Paula J. Britson; Jennifer L. Whitwell; Chadwick P. Ward; Anders M. Dale; Joel P. Felmlee; Jeffrey L. Gunter; Derek L. G. Hill; Ronald J. Killiany; Norbert Schuff; Sabrina Fox-Bosetti; Chen Lin; Colin Studholme; Charles DeCarli; Gunnar Krueger; Heidi A. Ward; Gregory J. Metzger; Katherine T. Scott; Richard Philip Mallozzi; Daniel James Blezek; Joshua R. Levy; Josef Phillip Debbins; Adam S. Fleisher; Marilyn S. Albert

The Alzheimers Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimers disease. Magnetic resonance imaging (MRI), (18F)‐fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquiredat multiple time points. All data will be cross‐linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications thatguided protocol development. A major effort was devoted toevaluating 3D T1‐weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced‐scale clinical trial. The protocol selected for the ADNI study includes: back‐to‐back 3D magnetization prepared rapid gradient echo (MP‐RAGE) scans; B1‐calibration scans when applicable; and an axial proton density‐T2 dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom‐based monitoring of all scanners could be used as a model for other multisite trials. J. Magn. Reson. Imaging 2008.


The New England Journal of Medicine | 2012

Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease

Randall J. Bateman; Chengjie Xiong; Anne M. Fagan; Alison Goate; Nick C. Fox; Daniel S. Marcus; Nigel J. Cairns; Xianyun Xie; Tyler Blazey; David M. Holtzman; Anna Santacruz; Virginia Buckles; Angela Oliver; Krista L. Moulder; Paul S. Aisen; Bernardino Ghetti; William E. Klunk; Eric McDade; Ralph N. Martins; Colin L. Masters; Richard Mayeux; John M. Ringman; Peter R. Schofield; Reisa A. Sperling; Stephen Salloway; John C. Morris

BACKGROUND The order and magnitude of pathologic processes in Alzheimers disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimers 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 participants age at baseline assessment and the parents age at the onset of symptoms of Alzheimers disease to calculate the estimated years from expected symptom onset (age of the participant minus parents 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 Alzheimers disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimers 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 Alzheimers disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.).


Lancet Neurology | 2010

Revising the definition of Alzheimer's disease: a new lexicon

Bruno Dubois; Howard Feldman; Claudia Jacova; Jeffrey L. Cummings; Steven T. DeKosky; Pascale Barberger-Gateau; André Delacourte; Giovanni B. Frisoni; Nick C. Fox; Douglas Galasko; Serge Gauthier; Harald Hampel; Gregory A. Jicha; Kenichi Meguro; John T. O'Brien; Florence Pasquier; Philippe Robert; Steven Salloway; Marie Sarazin; Leonardo Cruz de Souza; Yaakov Stern; Pieter J. Visser; Philip Scheltens

Alzheimers disease (AD) is classically defined as a dual clinicopathological entity. The recent advances in use of reliable biomarkers of AD that provide in-vivo evidence of the disease has stimulated the development of new research criteria that reconceptualise the diagnosis around both a specific pattern of cognitive changes and structural/biological evidence of Alzheimers pathology. This new diagnostic framework has stimulated debate about the definition of AD and related conditions. The potential for drugs to intercede in the pathogenic cascade of the disease adds some urgency to this debate. This paper by the International Working Group for New Research Criteria for the Diagnosis of AD aims to advance the scientific discussion by providing broader diagnostic coverage of the AD clinical spectrum and by proposing a common lexicon as a point of reference for the clinical and research communities. The cornerstone of this lexicon is to consider AD solely as a clinical and symptomatic entity that encompasses both predementia and dementia phases.


Archive | 2009

Letter abstract - Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's Disease

Denise Harold; Richard Abraham; Paul Hollingworth; Rebecca Sims; Amy Gerrish; Marian Lindsay Hamshere; Jaspreet Sing Pahwa; Valentina Moskvina; Kimberley Dowzell; Amy Williams; Nicola L. Jones; Charlene Thomas; Alexandra Stretton; Angharad R. Morgan; Simon Lovestone; John Powell; Petroula Proitsi; Michelle K. Lupton; Carol Brayne; David C. Rubinsztein; Michael Gill; Brian A. Lawlor; Aoibhinn Lynch; Kevin Morgan; Kristelle Brown; Peter Passmore; David Craig; Bernadette McGuinness; Stephen Todd; Clive Holmes

We undertook a two-stage genome-wide association study (GWAS) of Alzheimers disease (AD) involving over 16,000 individuals, the most powerful AD GWAS to date. In stage 1 (3,941 cases and 7,848 controls), we replicated the established association with the apolipoprotein E (APOE) locus (most significant SNP, rs2075650, P = 1.8 × 10−157) and observed genome-wide significant association with SNPs at two loci not previously associated with the disease: at the CLU (also known as APOJ) gene (rs11136000, P = 1.4 × 10−9) and 5′ to the PICALM gene (rs3851179, P = 1.9 × 10−8). These associations were replicated in stage 2 (2,023 cases and 2,340 controls), producing compelling evidence for association with Alzheimers disease in the combined dataset (rs11136000, P = 8.5 × 10−10, odds ratio = 0.86; rs3851179, P = 1.3 × 10−9, odds ratio = 0.86).


Lancet Neurology | 2013

Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration

Joanna M. Wardlaw; Eric E. Smith; Geert Jan Biessels; Charlotte Cordonnier; Franz Fazekas; Richard Frayne; Richard Lindley; John T. O'Brien; Frederik Barkhof; Oscar Benavente; Sandra E. Black; Carol Brayne; Monique M.B. Breteler; Hugues Chabriat; Charles DeCarli; Frank Erik De Leeuw; Fergus N. Doubal; Marco Duering; Nick C. Fox; Steven M. Greenberg; Vladimir Hachinski; Ingo Kilimann; Vincent Mok; Robert J. van Oostenbrugge; Leonardo Pantoni; Oliver Speck; Blossom C. M. Stephan; Stefan J. Teipel; Anand Viswanathan; David J. Werring

Summary Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).


Lancet Neurology | 2014

Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria

Bruno Dubois; Howard Feldman; Claudia Jacova; Harald Hampel; José Luis Molinuevo; Kaj Blennow; Steven T. DeKosky; Serge Gauthier; Dennis J. Selkoe; Randall J. Bateman; Stefano F. Cappa; Sebastian J. Crutch; Sebastiaan Engelborghs; Giovanni B. Frisoni; Nick C. Fox; Douglas Galasko; Marie Odile Habert; Gregory A. Jicha; Agneta Nordberg; Florence Pasquier; Gil D. Rabinovici; Philippe Robert; Christopher C. Rowe; Stephen Salloway; Marie Sarazin; Stéphane Epelbaum; Leonardo Cruz de Souza; Bruno Vellas; Pieter J. Visser; Lon S. Schneider

In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimers Association have contributed criteria for the diagnosis of Alzheimers disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimers pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD.


The New England Journal of Medicine | 2014

Two Phase 3 Trials of Bapineuzumab in Mild-to-Moderate Alzheimer's Disease

Stephen Salloway; Reisa A. Sperling; Nick C. Fox; Kaj Blennow; William E. Klunk; Murray A. Raskind; Marwan N. Sabbagh; Lawrence S. Honig; Anton P. Porsteinsson; Steven H. Ferris; Marcel Reichert; Nzeera Ketter; Bijan Nejadnik; Volkmar Guenzler; Maja Miloslavsky; Daniel Wang; Yuan Lu; Julia Lull; Iulia Cristina Tudor; Enchi Liu; Michael Grundman; Eric Yuen; Ronald S. Black; H. Robert Brashear

BACKGROUND Bapineuzumab, a humanized anti-amyloid-beta monoclonal antibody, is in clinical development for the treatment of Alzheimers disease. METHODS We conducted two double-blind, randomized, placebo-controlled, phase 3 trials involving patients with mild-to-moderate Alzheimers disease--one involving 1121 carriers of the apolipoprotein E (APOE) ε4 allele and the other involving 1331 noncarriers. Bapineuzumab or placebo, with doses varying by study, was administered by intravenous infusion every 13 weeks for 78 weeks. The primary outcome measures were scores on the 11-item cognitive subscale of the Alzheimers Disease Assessment Scale (ADAS-cog11, with scores ranging from 0 to 70 and higher scores indicating greater impairment) and the Disability Assessment for Dementia (DAD, with scores ranging from 0 to 100 and higher scores indicating less impairment). A total of 1090 carriers and 1114 noncarriers were included in the efficacy analysis. Secondary outcome measures included findings on positron-emission tomographic amyloid imaging with the use of Pittsburgh compound B (PIB-PET) and cerebrospinal fluid phosphorylated tau (phospho-tau) concentrations. RESULTS There were no significant between-group differences in the primary outcomes. At week 78, the between-group differences in the change from baseline in the ADAS-cog11 and DAD scores (bapineuzumab group minus placebo group) were -0.2 (P=0.80) and -1.2 (P=0.34), respectively, in the carrier study; the corresponding differences in the noncarrier study were -0.3 (P=0.64) and 2.8 (P=0.07) with the 0.5-mg-per-kilogram dose of bapineuzumab and 0.4 (P=0.62) and 0.9 (P=0.55) with the 1.0-mg-per-kilogram dose. The major safety finding was amyloid-related imaging abnormalities with edema among patients receiving bapineuzumab, which increased with bapineuzumab dose and APOE ε4 allele number and which led to discontinuation of the 2.0-mg-per-kilogram dose. Between-group differences were observed with respect to PIB-PET and cerebrospinal fluid phospho-tau concentrations in APOE ε4 allele carriers but not in noncarriers. CONCLUSIONS Bapineuzumab did not improve clinical outcomes in patients with Alzheimers disease, despite treatment differences in biomarkers observed in APOE ε4 carriers. (Funded by Janssen Alzheimer Immunotherapy and Pfizer; Bapineuzumab 301 and 302 ClinicalTrials.gov numbers, NCT00575055 and NCT00574132, and EudraCT number, 2009-012748-17.).


Nature Reviews Neurology | 2010

The clinical use of structural MRI in Alzheimer disease.

Giovanni B. Frisoni; Nick C. Fox; Clifford R. Jack; Philip Scheltens; Paul M. Thompson

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.


Brain | 2008

Automatic classification of MR scans in Alzheimer's disease

Stefan Klöppel; Cynthia M. Stonnington; Carlton Chu; Bogdan Draganski; Ri Scahill; Jonathan D. Rohrer; Nick C. Fox; Clifford R. Jack; John Ashburner; Richard S. J. Frackowiak

To be diagnostically useful, structural MRI must reliably distinguish Alzheimers disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

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Jason D. Warren

UCL Institute of Neurology

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Natalie S. Ryan

University College London

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David M. Cash

University College London

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Marc Modat

University College London

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