@article{10.1371/journal.pone.0148744,

title = {MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas},

author = {Rizzo Gaia, Veronese Mattia, Expert Paul, Turkheimer Federico E., Alessandra Bertoldo },

url = {http://dx.doi.org/10.1371%2Fjournal.pone.0148744},

doi = {10.1371/journal.pone.0148744},

year  = {2016},

date = {2016-01-01},

journal = {PLoS ONE},

volume = {11},

number = {2},

pages = {e0148744},

publisher = {Public Library of Science},

abstract = {<sec id="sec001"> <title>Introduction</title> <p>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.</p> </sec> <sec id="sec002"> <title>Aim</title> <p>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.</p> </sec> <sec id="sec003"> <title>Results</title> <p>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.</p> </sec>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1176/appi.ajp.2015.14101358,

title = {Microglial Activity in People at Ultra High Risk of Psychosis and in Schizophrenia: An [11C]PBR28 PET Brain Imaging Study},

author = {Peter S. Bloomfield and Sudhakar Selvaraj and Mattia Veronese and Gaia Rizzo and Alessandra Bertoldo and David R. Owen and Michael A.P. Bloomfield and Ilaria Bonoldi and Nicola Kalk and Federico Turkheimer and Philip McGuire and Vincenzo de Paola and Oliver D. Howes},

url = {http://dx.doi.org/10.1176/appi.ajp.2015.14101358},

doi = {10.1176/appi.ajp.2015.14101358},

year  = {2016},

date = {2016-01-01},

journal = {American Journal of Psychiatry},

volume = {173},

number = {1},

pages = {44-52},

note = {PMID: 26472628},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Veronese20161,

title = {Measuring specific receptor binding of a PET radioligand in human brain without pharmacological blockade: The genomic plot},

author = {Mattia Veronese and Paolo Zanotti-Fregonara and Gaia Rizzo and Alessandra Bertoldo and Robert B. Innis and Federico E. Turkheimer},

url = {http://www.sciencedirect.com/science/article/pii/S1053811916000732},

doi = {http://dx.doi.org/10.1016/j.neuroimage.2016.01.058},

issn = {1053-8119},

year  = {2016},

date = {2016-01-01},

journal = {NeuroImage},

volume = {130},

pages = {1 - 12},

abstract = {Abstract PET studies allow in vivo imaging of the density of brain receptor species. The PET signal, however, is the sum of the fraction of radioligand that is specifically bound to the target receptor and the non-displaceable fraction (i.e. the non-specifically bound radioligand plus the free ligand in tissue). Therefore, measuring the non-displaceable fraction, which is generally assumed to be constant across the brain, is a necessary step to obtain regional estimates of the specific fractions. The nondisplaceable binding can be directly measured if a reference region, i.e. a region devoid of any specific binding, is available. Many receptors are however widely expressed across the brain, and a true reference region is rarely available. In these cases, the nonspecific binding can be obtained after competitive pharmacological blockade, which is often contraindicated in humans. In this work we introduce the genomic plot for estimating the nondisplaceable fraction using baseline scans only. The genomic plot is a transformation of the Lassen graphical method in which the brain maps of mRNA transcripts of the target receptor obtained from the Allen brain atlas are used as a surrogate measure of the specific binding. Thus, the genomic plot allows the calculation of the specific and nondisplaceable components of radioligand uptake without the need of pharmacological blockade. We first assessed the statistical properties of the method with computer simulations. Then we sought ground-truth validation using human PET datasets of seven different neuroreceptor radioligands, where nonspecific fractions were either obtained separately using drug displacement or available from a true reference region. The population nondisplaceable fractions estimated by the genomic plot were very close to those measured by actual human blocking studies (mean relative difference between 2% and 7%). However, these estimates were valid only when mRNA expressions were predictive of protein levels (i.e. there were no significant post-transcriptional changes). This condition can be readily established a priori by assessing the correlation between PET and mRNA expression.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}