title: MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas.
abstract: BACKGROUND
  Brain-wide mRNA mappings offer a great potential for neuroscience research as they can provide information about system proteomics. In a previous work we have correlated mRNA maps with the binding patterns of radioligands targeting specific molecular systems and imaged with positron emission tomography (PET) in unrelated control groups. This approach is potentially applicable to any imaging modality as long as an efficient procedure of imaging-genomic matching is provided. In the original work we considered mRNA brain maps of the whole human genome derived from the Allen human brain database (ABA) and we performed the analysis with a specific region-based segmentation with a resolution that was limited by the PET data parcellation. There we identified the need for a platform for imaging-genomic integration that should be usable with any imaging modalities and fully exploit the high resolution mapping of ABA dataset.
  
  
  OBJECTIVE
  In this work we present MENGA (Multimodal Environment for Neuroimaging and Genomic Analysis), a software platform that allows the investigation of the correlation patterns between neuroimaging data of any sort (both functional and structural) with mRNA gene expression profiles derived from the ABA database at high resolution.
  
  
  RESULTS
  We applied MENGA to six different imaging datasets from three modalities (PET, single photon emission tomography and magnetic resonance imaging) targeting the dopamine and serotonin receptor systems and the myelin molecular structure. We further investigated imaging-genomic correlations in the case of mismatch between selected proteins and imaging targets.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Gaia
authors.initials: G
authors.lastname: Rizzo
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
authors.forename: Mattia
authors.initials: M
authors.lastname: Veronese
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Expert
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
authors.forename: Federico E
authors.initials: FE
authors.lastname: Turkheimer
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Bertoldo
mesh_terms: D001931:Brain Mapping / Q000379:methods; D030541:Databases, Genetic; D004298:Dopamine / Q000235:genetics / Q000378:metabolism; D023281:Genomics; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging / Q000379:methods*; D049268:Positron-Emission Tomography; D012333:RNA, Messenger / Q000235:genetics; D012701:Serotonin / Q000235:genetics / Q000378:metabolism; D012984:Software; D059467:Transcriptome / Q000235:genetics*
chemical_list: D012333:RNA, Messenger; D012701:Serotonin; D004298:Dopamine