PSYSCAN - Newsletter September 2016


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Introduction by Prof. Mc Guire

Publications, The road to PSYSCAN

PSYSCAN in short: : WP2 and WP5

Knowing the Work Packages, IXICO

A day in the life of a young PSYSCAN researcher, Margot Slot

The team

PSYSCAN contact

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Introduction,
by Prof. Mc Guire


Prof. Mc Guire portrait

Dear Reader

Welcome to the first PSYSCAN Newsletter!

PSYSCAN is a large scale multi-centre study funded by the European Commission. The PSYSCAN consortium includes around 20 leading research centres in Europe,
Australia, Canada, South America and Asia, along with our industrial partners, IXICO, CamCog, Roche and Lilly.

The main aim of the programme is to develop tools that can be used in a clinical setting to help predict outcomes in the early phase of psychosis. The study will focus on two clinical populations: people at ultra high risk for psychosis, and patients who are experiencing their first episode of psychosis.

In the high risk population, we are interested in facilitating prediction of whether people will go on to develop psychosis, will have persistent symptoms without developing psychosis, or will recover. In the first episode population we plan to facilitate prediction of the response to treatment, and of the long term course of illness.

In both types of subject, we will collect clinical, cognitive and neuroimaging data, as well as blood samples that will allow us to examine genetic, inflammatory, immunological, proteomic and metabolic markers. These data will be acquired at presentation and at follow up points.

Since starting the programme, we have been building a central database to store the data, and developing new software that will be used to integrate and analyse the data. An excellent website has been produced, and this will be a key source of information about the project, as well as training materials. We have also been collating data that were previously collected by members of the PYSCAN consortium in earlier studies. These legacy data will be pooled to form larger datasets, and analysed using the novel methods developed in the project. The participating sites are now in the final stages of preparation for the initiation of subject recruitment, which will start in the next few weeks.

We are lucky to have a fantastic consortium, and it has been a pleasure to work with colleagues with such a wide diversity of skills and expertise. This newsletter will provide you with regular updates on the progress of the study from now on. We look forward to bringing you more news from PSYSCAN as the project moves forward.

Best wishes

Philip McGuire

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Publications: The road to PSYSCAN

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A.

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.

Neurosci Biobehav Rev. 2012 Apr;36(4):1140-52. doi: 10.1016/j.neubiorev.2012.01.004. Epub 2012 Jan 28.


Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers.

Koutsouleris N, Riecher-Rössler A, Meisenzahl EM, Smieskova R, Studerus E, Kambeitz-Ilankovic L, von Saldern S, Cabral C, Reiser M, Falkai P,Borgwardt S.

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.

Schizophr Bull. 2015 Mar;41(2):471-82. doi: 10.1093/schbul/sbu078. Epub 2014 Jun 9.



Using structural neuroimaging to make quantitative predictions of symptom progression in individuals at ultra-high risk for psychosis.

Tognin S, Pettersson-Yeo W, Valli I, Hutton C, Woolley J, Allen P, McGuire P, Mechelli A.

Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy. These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.


Front Psychiatry. 2014 Jan 29;4:187. doi: 10.3389/fpsyt.2013.00187. eCollection 2013.



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PSYSCAN in short: WP2 and WP5

WP2
Dr. Andrea Mechelli portait

Work package 2 (WP2) involves the re-analysis of previously acquired data. So far, 8 research groups from the PSYSCAN consortium have contributed data to this work package, resulting in a total sample of 1,968 subjects including 887 patients and 1081 healthy controls. All datasets include demographic, clinical and structural MRI data, and some of them include cognitive, functional MRI and DTI information. These datasets provide a fantastic opportunity to implement new analytical approaches, test novel hypotheses and carry out mega-analyses. We will shortly circulate a full list of available datasets amongst those research teams that are participating in WP2, and provide instructions on how to request access.

For more information about the work we are doing on Work package 2, please contact Dr. Andrea Mechelli, WP2 coordinator.

WP5 Dr. Stefania Tognin Dr. Erika van Hell

Work package 5 (WP5) involves the recruitment and assessment of participants with a first episode of psychosis (FEP), participants at ultra-high risk of developing psychosis (UHR) and healthy controls (HC) and the collection of new clinical, biological, cognitive and neuroimaging data. 20 research sites across Europe, North America, Asia and Australia will participate in the project. 17 research sites will participate in the recruitment of FEP participants, 13 research sites in the recruitment of UHR participants and, among those, 8 sites will also recruit HC participants. Some of the research sites will recruit and assess only one cohort while others will recruit and assess two or three cohorts. The FEP protocol, led by UMC Utrecht and the UHR protocol, led by King’s College London were finalised and sent to the sites in September 2015. All sites have applied to obtain ethical approval in their respective countries. Sites that have obtained ethical approval are currently being initiated by the respective project managers and they are about to start local recruitment.

For more information about the work we are doing on Work package 5, please contact Dr. Stefania Tognin at KCL, WP5 UHR protocol project manager and/or Dr. Erika van Hell , WP5 FEP protocol project manager.

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Knowing the Work Packages.
IXICO Logo

In this issue we look at IXICO, a London-based company who support clinical studies through technology solutions for running trials, and data management. Work package 4 will lead on the development of standardized protocols for the acquisition of the different types of NI data that will be processed by the tool. Kate McLeish is VP of technology at IXICO and has an extensive background in medical imaging and product development in clinical trials and healthcare.

Kate, which made you become interested in NI in psychosis?

I have, with IXICO and my experience as a researcher, been involved in several NI projects around healthy aging and neuro degenerative disorders such as dementia and Huntington’s Disease. As part of this work we have developed many tools for assessing these diseases and the drugs used to treat them. It is very rewarding to be able to translate these tools to the area of psychosis to support work in this area. We have previously been involved in the Optimise study also working in this area but this is the first time we are involved scientifically in this group.

Could you give us a brief summary of the characteristics of the core informatics platform development of PSYSCAN-WP4? Which are their objectives?

The core platform is at the moment focused on collecting data as part of a prospective research study. The data includes imaging, demographic and clinical tests amongst others and needs to collect data from the multiple hospital sites involved in the study. The platform has different interfaces for collecting different types of data and will also be used for the analysis of the data. Data will be analysed using existing and new techniques.
Ultimately the platform will develop into one that clinicians can use as part of routine patient management, but that is some time off yet.

Which are the main difficulties when building this kind of tool?

It is hard to provide a seamless, easy to use platform when there are such large amounts of complex data to be collected. We are working closely with Cambridge Cognition to provide the initial platform but the real challenge will be when we come to make this a platform for use in routine patient management, as the user experience of the clinicians will be even more important.

Most of us are psychiatrists and psychologists who know little about computers. Could you give us a brief explanation of what machine learning is and how does it work?

Once all the data has been collected in the prospective research study, scientists will be working on algorithms that take several sources of data from large groups of patients to look for patterns in the data. This may be a combination of image analysis, demographics and behaviour for example to say “is there something in the structure or connectivity in the brain in certain individuals that indicates some type of behavioural outcome?” The key to this work is having large amounts of patient data and the data being collected as consistently as possible across the group.

Could you tell us something about the team involved in the development and organization of WP3?

We are working closely with Cambridge Cognition to deliver a platform that can be used for data collection. They have lots of experience in developing iPad based applications for use in the clinic.

Which, do you think, are the main positive aspects of the PSYCAN project?

The large scale EU projects are a great way of accessing large numbers of patients, they give access to the main KOL in the space and in this project a really good way of capturing requirements for an innovative product that can hopefully have a large impact on patients with psychosis.

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A day in the life of a young PSYSCAN researcher
Margot Slot, from the UMC Utrecht
Margot Slot

I started working at the University Medical Center Utrecht when I had just finished my master’s program in Neuropsychology. As part of this, I completed a clinical internship at a neurology department. I realized that volunteering as a research assistant at the Psychiatry department of the UMC would be quite different from the clinical work during my internship, but I was very excited to gain some experience in this field.

Initially, I was mainly responsible for the practical realization of various clinical studies in the area of psychosis, assisting with recruitment, assessments, interviews and MRI scans. It gave me the opportunity to learn a lot about the patient group and let me realize even more how important it is to develop new treatment options for psychotic disorders. It’s interesting to collaborate with professionals from other disciplines and to work for various projects focusing on the pathophysiology, illness course and treatment of psychotic disorders. In order to recruit the target number of patients for our projects, I maintain close contacts with other healthcare institutions. It can be a challenge to motivate the external staff to cooperate in one of our studies, but these collaborations often appear to be very fruitful in the end.

Over the course of time, I also got involved in larger multi-center international trials such as PSYSCAN. The PSYSCAN start-up meeting in August was a great opportunity to meet everybody involved in the project and to get familiar with the study procedures. Since then, I have started the preparatory work for the initiation of PSYSCAN at the UMC Utrecht; think about discussing our plans with the laboratory and radiology department, setting out a study manual and arranging test materials. Currently, our MRI scanner is replaced by a new 3 Tesla scanner. Unfortunately, this means that the UMC Utrecht will not be able to start immediately after approval from the Medical Ethical Review Board. However, we expect the new scanner to be up and running soon. Up till then, we will give our best shot to promote PSYSCAN at the Psychiatry department and to motivate everyone to contribute in this study. Recruiting patients will provide some challenge, as we have many ongoing clinical trials in the field of psychosis. Nevertheless, we look forward to recruiting our first patient in 2016!

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The team

map FEP Cohort

map UHR Cohort
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Contact details
Coordinator
Philip McGuire
Institute of Psychiatry, Psychology, and Neuroscience
King’s College, London
16 De Crespigny Park
London SE5 8AF
United Kingdom
Phone: +44 207 848 0355
Email: philip.mcguire@kcl.ac.uk
Research Programme Coordinator
Currently vacant
Project Finance and Legal Manager
Katherine Bellenie
Institute of Psychiatry, Psychology, and Neuroscience
King’s College, London
16 De Crespigny Park
London SE5 8AF
United Kingdom
Phone: +44 (0)207 848 0927
Email: katherine.bellenie@kcl.ac.uk
Project Manager FEP
Erika van Hell
Brain Center Rudolf Magnus
Department of Psychiatry, A01.126
University Medical Center Utrecht
PO Box 85500
3504 GA Utrecht
The Netherlands
Phone: +31 (0)88 7556369
Email: H.H.Vanhell-2@umcutrecht.nl
Project Manager UHR
Stefania Tognin
Institute of Psychiatry, Psychology, and Neuroscience
King’s College, London
16 De Crespigny Park
London SE5 8AF
United Kingdom
Email: stefania.tognin@kcl.ac.uk
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