Description of work performed and main results of the first 18 month:


WP1 has set up IMAGEMEND servers for data storage and automated procedures for harmonization of demographic, clinical and other meta-data are in place. Extensive cataloguing of information has already been completed for currently available baseline data on 8,452 subjects. Expansion of environmental risk data through re-contacting of IMAGEMEND subjects is ongoing. WP1 is ensuring that all analyses are performed to the highest scientific standards.

 

WP2 has performed studies to identify markers in the disorders of interest and several publications and presentations of the data at scientific congresses have resulted from this work. Now, we will address differential diagnosis aspects, based on the IMAGEMEND database. WP2 has identified genetic markers for subcortical brain volumes, and the effects of genetic markers from case-control analyses on brain phenotypes have been evaluated. Multi-modality analyses are being used in the context of case-control studies within and beyond the imaging domain to define classifiers. Trans-diagnostic markers are being defined based on clinical, cognitive and imaging phenotypes.

 

WP3 has compiled a consensus report on clinical criteria, protocols for statistical analysis of outcome predictors and has drafted a proposal of imaging features to be used as markers for prediction and stratification analyses. WP3 has further identified a number of candidate genetic markers to perform patient stratification, which will be tested in the near future, and has compiled a list of criteria and statistical protocols for the analysis of data concerning treatment outcome.

 

WP4 has assessed sub-clinical psychotic symptoms in IMAGEN using the ‘CAPE’ self-report questionnaire. Some evidence suggests that the genetic basis of full blown schizophrenia (SZ) may be shared with subjects scoring highly on CAPE. WP4 has been collaborating closely with BI on predicting attention deficit hyperactivity disorder (ADHD) remission/persistence at age 19 based on data taken at age 14. This has involved the use of functional and structural MRI and environmental risk variables. As a result, biomarkers that are associated with ADHD remission and persistence have been identified.

 

WP5 has developed a software pipeline for machine learning analysis of structural MRI data and for feature extraction and classification based on DTI and behavioural data. During a more advanced stage of the application, a more formal evaluation of the different requirements will be performed. WP5 will evaluate the ability to classify the phenotypes of interest with sufficient performance. WP5 will then proceed to automatize parts of the workflow, reporting and visualization of results. A simple, easy to use, real-time fMRI analysis software is available for all partners and has been distributed to CIMH. This software will be the basis for the development of a dedicated prototype. Specific tools for real-time functional connectivity analysis have been developed and are tested at BI and CIMH.

 

WP6 has scrutinized the informed consents of all clinical IMAGEMEND partners to ensure that they meet all ethical and legal requirements for performing the outlined research. Additionally, WP6 has identified various ethical problem areas related to genetic testing and case-vignettes illustrating these were developed. WP6 has made significant progress in discussions on informed consent procedures and ethical, legal, and regulatory issues related to sequencing in clinical practice.

 

WP7 is concerned with dissemination of the IMAGEMEND project and results and has implemented a website, a project flyer, and a poster, which has been used for presentation at various conferences. Furthermore, several scientific papers have been published on IMAGEMEND work.

 

WP8 is ensuring that IMAGEMEND achieves its objectives, supports the consortium regarding legal and financial matters, has established a communication strategy and has been organizing successful meetings for the consortium.

 

Authors

 

TitleReferenceDate
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Papmeyer et al.

Cortical Thickness in Individuals at High Familial Risk of Mood Disorders as They Develop Major Depressive Disorder Biol Psychiatry. 2014 Oct 30. pii: S0006-3223(14)00802-6. doi: 10.1016/j.biopsych.2014.10.018. [Epub ahead of print] 2014
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Wolfers et al. Lower white matter microstructure in the superior longitudinal fasciculus is associated with increased response time variability in adults with attention- deficit / hyperactivity disorder  J. Psychiatry Neurosci. 40, 1–8. doi:10.1503/jpn.140154 2015
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Wolfers, et al. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics Neurosci Biobehav Rev. 2015 Aug 4. pii: S0149-7634(15)00201-8. doi: 10.1016/j.neubiorev.2015.08.001. [Epub ahead of print] 2015
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Luksys et al. Computational dissection of human episodic memory reveals mental process-specific genetic profiles Proc Natl Acad Sci U S A. 2015 Sep 1;112(35):E4939-48. doi: 10.1073/pnas.1500860112. Epub 2015 Aug 10. 2015
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 2015
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Noirhomme et al.

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients

Neuroimage. 2015 Dec 12. pii: S1053-8119(15)01119-2. doi: 10.1016/j.neuroimage.2015.12.006. [Epub ahead of print]  2015
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