Imperial College London

Professor Paul M. Matthews

Faculty of MedicineDepartment of Medicine

Edmond and Lily Safra Chair and Head of Brain Sciences
 
 
 
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Contact

 

+44 (0)20 7594 2855p.matthews

 
 
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Assistant

 

Mr Noel Caliste +44 (0)20 7594 2855

 
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Location

 

E515Burlington DanesHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

536 results found

Matthews PM, Datta G, Colasanti A, KALK N, Owen D, Scott G, Rabiner E, Gunn R, Lingford-Hughes A, Malik O, Ciccarelli O, Nicholas R, Nei L, Battaglini M, Stefano Net al., [(11)C]PBR28 or [(18)F]PBR111 detect white matter inflammatory heterogeneity in multiple sclerosis., Journal of Nuclear Medicine, ISSN: 1535-5667

JOURNAL ARTICLE

Bai W, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, Glocker B, King A, Matthews PM, Rueckert Det al., 2017, Semi-supervised learning for network-based cardiac MR image segmentation, Pages: 253-260, ISSN: 0302-9743

© Springer International Publishing AG 2017. Training a fully convolutional network for pixel-wise (or voxel-wise) image segmentation normally requires a large number of training images with corresponding ground truth label maps. However, it is a challenge to obtain such a large training set in the medical imaging domain, where expert annotations are time-consuming and difficult to obtain. In this paper, we propose a semi-supervised learning approach, in which a segmentation network is trained from both labelled and unlabelled data. The network parameters and the segmentations for the unlabelled data are alternately updated. We evaluate the method for short-axis cardiac MR image segmentation and it has demonstrated a high performance, outperforming a baseline supervised method. The mean Dice overlap metric is 0.92 for the left ventricular cavity, 0.85 for the myocardium and 0.89 for the right ventricular cavity. It also outperforms a state-of-the-art multi-atlas segmentation method by a large margin and the speed is substantially faster.

CONFERENCE PAPER

Bishop CA, Newbould RD, Lee JSZ, Honeyfield L, Quest R, Colasanti A, Ali R, Mattoscio M, Cortese A, Nicholas R, Matthews PM, Muraro PA, Waldman ADet al., 2017, Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions, NEUROIMAGE-CLINICAL, Vol: 13, Pages: 9-15, ISSN: 2213-1582

JOURNAL ARTICLE

Coffey S, Lewandowski AJ, Garratt S, Meijer R, Lynum S, Bedi R, Paterson J, Yaqub M, Noble JA, Neubauer S, Petersen SE, Allen N, Sudlow C, Collins R, Matthews PM, Leeson Pet al., 2017, Protocol and quality assurance for carotid imaging in 100,000 participants of UK Biobank: development and assessment., Eur J Prev Cardiol

Background Ultrasound imaging is able to quantify carotid arterial wall structure for the assessment of cerebral and cardiovascular disease risks. We describe a protocol and quality assurance process to enable carotid imaging at large scale that has been developed for the UK Biobank Imaging Enhancement Study of 100,000 individuals. Design An imaging protocol was developed to allow measurement of carotid intima-media thickness from the far wall of both common carotid arteries. Six quality assurance criteria were defined and a web-based interface (Intelligent Ultrasound) was developed to facilitate rapid assessment of images against each criterion. Results and conclusions Excellent inter and intra-observer agreements were obtained for image quality evaluations on a test dataset from 100 individuals. The image quality criteria then were applied in the UK Biobank Imaging Enhancement Study. Data from 2560 participants were evaluated. Feedback of results to the imaging team led to improvement in quality assurance, with quality assurance failures falling from 16.2% in the first two-month period examined to 6.4% in the last. Eighty per cent had all carotid intima-media thickness images graded as of acceptable quality, with at least one image acceptable for 98% of participants. Carotid intima-media thickness measures showed expected associations with increasing age and gender. Carotid imaging can be performed consistently, with semi-automated quality assurance of all scans, in a limited timeframe within a large scale multimodality imaging assessment. Routine feedback of quality control metrics to operators can improve the quality of the data collection.

JOURNAL ARTICLE

Datta G, Colasanti A, Kalk N, Owen D, Scott G, Rabiner EA, Gunn RN, Lingford-Hughes A, Malik O, Ciccarelli O, Nicholas R, Nei L, Battaglini M, Stefano ND, Matthews PMet al., 2017, C-11-PBR28 and F-18-PBR111 Detect White Matter Inflammatory Heterogeneity in Multiple Sclerosis, JOURNAL OF NUCLEAR MEDICINE, Vol: 58, Pages: 1477-1482, ISSN: 0161-5505

JOURNAL ARTICLE

Datta G, Violante IR, Scott G, Zimmerman K, Santos-Ribeiro A, Rabiner EA, Gunn RN, Malik O, Ciccarelli O, Nicholas R, Matthews PMet al., 2017, Translocator positron-emission tomography and magnetic resonance spectroscopic imaging of brain glial cell activation in multiple sclerosis, MULTIPLE SCLEROSIS JOURNAL, Vol: 23, Pages: 1469-1478, ISSN: 1352-4585

JOURNAL ARTICLE

Dong H, Supratak A, Pan W, Wu C, Matthews PM, Guo Yet al., 2017, Mixed Neural Network Approach for Temporal Sleep Stage Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, ISSN: 1534-4320

IEEE This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory (LSTM) network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electrooculogram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

JOURNAL ARTICLE

Gafson A, Craner MJ, Matthews PM, 2017, Personalised medicine for multiple sclerosis care, MULTIPLE SCLEROSIS JOURNAL, Vol: 23, Pages: 362-369, ISSN: 1352-4585

JOURNAL ARTICLE

He S, Yong M, Matthews PM, Guo Yet al., 2017, tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles, BIOINFORMATICS, Vol: 33, Pages: 787-788, ISSN: 1367-4803

JOURNAL ARTICLE

LaRocca NG, Hudson LD, Rudick R, Amtmann D, Balcer L, Benedict R, Bermel R, Chang I, Chiaravalloti ND, Chin P, Cohen JA, Cutter GR, Davis MD, DeLuca J, Feys P, Francis G, Goldman MD, Hartley E, Kapoor R, Lublin F, Lundstrom G, Matthews PM, Mayo N, Meibach R, Miller DM, Motl RW, Mowry EM, Naismith R, Neville J, Panagoulias J, Panzara M, Phillips G, Robbins A, Sidovar MF, Smith KE, Sperling B, Uitdehaag BM, Weaver J, Multiple Sclerosis Outcome Assessments Consortium MSOACet al., 2017, The MSOAC approach to developing performance outcomes to measure and monitor multiple sclerosis disability., Mult Scler

BACKGROUND: The Multiple Sclerosis Outcome Assessments Consortium (MSOAC) was formed by the National MS Society to develop improved measures of multiple sclerosis (MS)-related disability. OBJECTIVES: (1) To assess the current literature and available data on functional performance outcome measures (PerfOs) and (2) to determine suitability of using PerfOs to quantify MS disability in MS clinical trials. METHODS: (1) Identify disability dimensions common in MS; (2) conduct a comprehensive literature review of measures for those dimensions; (3) develop an MS Clinical Data Interchange Standards Consortium (CDISC) data standard; (4) create a database of standardized, pooled clinical trial data; (5) analyze the pooled data to assess psychometric properties of candidate measures; and (6) work with regulatory agencies to use the measures as primary or secondary outcomes in MS clinical trials. CONCLUSION: Considerable data exist supporting measures of the functional domains ambulation, manual dexterity, vision, and cognition. A CDISC standard for MS ( http://www.cdisc.org/therapeutic#MS ) was published, allowing pooling of clinical trial data. MSOAC member organizations contributed clinical data from 16 trials, including 14,370 subjects. Data from placebo-arm subjects are available to qualified researchers. This integrated, standardized dataset is being analyzed to support qualification of disability endpoints by regulatory agencies.

JOURNAL ARTICLE

Lema A, Bishop C, Malik O, Mattoscio M, Ali R, Nicholas R, Muraro PA, Matthews PM, Waldman AD, Newbould RDet al., 2017, A Comparison of Magnetization Transfer Methods to Assess Brain and Cervical Cord Microstructure in Multiple Sclerosis, JOURNAL OF NEUROIMAGING, Vol: 27, Pages: 221-226, ISSN: 1051-2284

JOURNAL ARTICLE

Matthews PM, 2017, Advanced MRI measures like DTI or fMRI should be outcome measures in future clinical trials - NO., Mult Scler, Vol: 23, Pages: 1456-1458

JOURNAL ARTICLE

Nie L, Yang X, Matthews PM, Xu ZW, Guo YKet al., 2017, Inferring functional connectivity in fMRI using minimum partial correlation, International Journal of Automation and Computing, Vol: 14, Pages: 371-385, ISSN: 1476-8186

© 2017, The Author(s). Functional connectivity has emerged as a promising approach to study the functional organisation of the brain and to define features for prediction of brain state. The most widely used method for inferring functional connectivity is Pearson-s correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by computing the partial correlation between two regions controlling all other regions, but this method suffers from Berkson-s paradox. Some advanced methods, such as regularised inverse covariance, have been applied. However, these methods usually depend on some parameters. Here we propose use of minimum partial correlation as a parameter-free measure for the skeleton of functional connectivity in functional magnetic resonance imaging (fMRI). The minimum partial correlation between two regions is the minimum of absolute values of partial correlations by controlling all possible subsets of other regions. Theoretically, there is a direct effect between two regions if and only if their minimum partial correlation is non-zero under faithfulness and Gaussian assumptions. The elastic PC-algorithm is designed to efficiently approximate minimum partial correlation within a computational time budget. The simulation study shows that the proposed method outperforms o thers in most cases and its application is illustrated using a resting-state fMRI dataset from the human connectome project.

JOURNAL ARTICLE

Owen DR, Narayan N, Wells L, Healy L, Smyth E, Rabiner EA, Galloway D, Williams JB, Lehr J, Mandhair H, Peferoen LAN, Taylor PC, Amor S, Antel JP, Matthews PM, Moore CSet al., 2017, Pro-inflammatory activation of primary microglia and macrophages increases 18 kDa translocator protein expression in rodents but not humans, JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 37, Pages: 2679-2690, ISSN: 0271-678X

JOURNAL ARTICLE

Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafo MR, Nichols TE, Poline J-B, Vul E, Yarkoni Tet al., 2017, Scanning the horizon: towards transparent and reproducible neuroimaging research, NATURE REVIEWS NEUROSCIENCE, Vol: 18, Pages: 115-126, ISSN: 1471-003X

JOURNAL ARTICLE

Robinson R, Valindria VV, Bai W, Suzuki H, Matthews PM, Page C, Rueckert D, Glocker Bet al., 2017, Automatic quality control of cardiac MRI segmentation in large-scale population imaging, Pages: 720-727, ISSN: 0302-9743

© 2017, Springer International Publishing AG. The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.

CONFERENCE PAPER

Shenkin SD, Pernet C, Nichols TE, Poline J-B, Matthews PM, van der Lugt A, Mackay C, Lanyon L, Mazoyer B, Boardman JP, Thompson PM, Fox N, Marcus DS, Sheikh A, Cox SR, Anblagan D, Job DE, Dickie DA, Rodriguez D, Wardlaw JMet al., 2017, Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group, NEUROIMAGE, Vol: 153, Pages: 399-409, ISSN: 1053-8119

JOURNAL ARTICLE

Wilman HR, Kelly M, Garratt S, Matthews PM, Milanesi M, Herlihy A, Gyngell M, Neubauer S, Bell JD, Banerjee R, Thomas ELet al., 2017, Characterisation of liver fat in the UK Biobank cohort, PLOS ONE, Vol: 12, ISSN: 1932-6203

JOURNAL ARTICLE

Colasanti A, Guo Q, Giannetti P, Wall MB, Newbould RD, Bishop C, Onega M, Nicholas R, Ciccarelli O, Muraro PA, Malik O, Owen DR, Young AH, Gunn RN, Piccini P, Matthews PM, Rabiner EAet al., 2016, Hippocampal Neuroinflammation, Functional Connectivity, and Depressive Symptoms in Multiple Sclerosis, BIOLOGICAL PSYCHIATRY, Vol: 80, Pages: 62-72, ISSN: 0006-3223

JOURNAL ARTICLE

Comninos AN, Anastasovska J, Sahuri-Arisoylu M, Li X, Li S, Hu M, Jayasena CN, Ghatei MA, Bloom SR, Matthews PM, O'Byrne KT, Bell JD, Dhillo WSet al., 2016, Kisspeptin signaling in the amygdala modulates reproductive hormone secretion, BRAIN STRUCTURE & FUNCTION, Vol: 221, Pages: 2035-2047, ISSN: 1863-2653

JOURNAL ARTICLE

Datta G, Colasanti A, Kalk NJ, Owen DR, Scott G, Rabiner EA, Gunn RN, Lingford-Hughes AR, Malik O, Ciccarelli O, Nicholas R, Battaglini M, Stefano ND, Matthews PMet al., 2016, In vivo translocator protein positron emission tomography imaging detects a heterogeneity of lesion inflammatory activity in multiple sclerosis not evident by MRI., 32nd Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), Publisher: SAGE PUBLICATIONS LTD, Pages: 36-37, ISSN: 1352-4585

CONFERENCE PAPER

De Guio F, Jouvent E, Biessels GJ, Black SE, Brayne C, Chen C, Cordonnier C, De Leeuw F-E, Dichgans M, Doubal F, Duering M, Dufouil C, Duzel E, Fazekas F, Hachinski V, Ikram MA, Linn J, Matthews PM, Mazoyer B, Mok V, Norrving B, O'Brien JT, Pantoni L, Ropele S, Sachdev P, Schmidt R, Seshadri S, Smith EE, Sposato LA, Stephan B, Swartz RH, Tzourio C, van Buchem M, van der Lugt A, van Oostenbrugge R, Vernooij MW, Viswanathan A, Werring D, Wollenweber F, Wardlaw JM, Chabriat Het al., 2016, Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease, JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 36, Pages: 1319-1337, ISSN: 0271-678X

JOURNAL ARTICLE

Dong H, Matthews PM, Guo Y, 2016, A New Soft Material Based In-the-Ear EEG Recording Technique, 38th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5709-5712, ISSN: 1557-170X

CONFERENCE PAPER

Gafson AR, Nicholas R, Giovannoni G, Matthews PMet al., 2016, Plasma cytokine concentration changes in multiple sclerosis patients after treatment with dimethyl fumarate, 32nd Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), Publisher: SAGE PUBLICATIONS LTD, Pages: 670-671, ISSN: 1352-4585

CONFERENCE PAPER

James A, Joyce E, Lunn D, Hough M, Kenny L, Ghataorhe P, Fernandes HM, Matthews PM, Zarei Met al., 2016, Abnormal frontostriatal connectivity in adolescent-onset schizophrenia and its relationship to cognitive functioning (vol 35C, pg 32, 2016), EUROPEAN PSYCHIATRY, Vol: 38, Pages: 22-22, ISSN: 0924-9338

JOURNAL ARTICLE

James A, Joyce E, Lunn D, Hough M, Kenny L, Ghataorhe P, Fernandez H, Matthews PM, Zarei Met al., 2016, Abnormal frontostriatal connectivity in adolescent-onset schizophrenia and its relationship to cognitive functioning, EUROPEAN PSYCHIATRY, Vol: 35, Pages: 32-38, ISSN: 0924-9338

JOURNAL ARTICLE

Khamis RY, Woollard KJ, Hyde GD, Boyle JJ, Bicknell C, Chang S-H, Malik TH, Hara T, Mauskapf A, Granger DW, Johnson JL, Ntziachristos V, Matthews PM, Jaffer FA, Haskard DOet al., 2016, Near Infrared Fluorescence (NIRF) Molecular Imaging of Oxidized LDL with an Autoantibody in Experimental Atherosclerosis, SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322

JOURNAL ARTICLE

Lovestone S, Rossor M, Gallacher J, Ritchie C, Burn D, Hyslop PSG, Mackay C, Matthews PM, Ballard C, Georges Jet al., 2016, Better together for better dementia research and care, LANCET PSYCHIATRY, Vol: 3, Pages: 503-504, ISSN: 2215-0374

JOURNAL ARTICLE

Maron E, Near J, Wallis G, Stokes M, Matthews PM, Nutt DJet al., 2016, A pilot study of the effect of short-term escitalopram treatment on brain metabolites and gamma-oscillations in healthy subjects, JOURNAL OF PSYCHOPHARMACOLOGY, Vol: 30, Pages: 579-580, ISSN: 0269-8811

JOURNAL ARTICLE

Matthews PM, Hampshire A, 2016, Clinical Concepts Emerging from fMRI Functional Connectomics, NEURON, Vol: 91, Pages: 511-528, ISSN: 0896-6273

JOURNAL ARTICLE

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