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Gray matter characterization

Gray matter is the tissue in the brain that contains most neuronal cell bodies. Since gray matter is crucial for brain function, there is an interest in extracting quantitative information about its structure, like for example surfaces, thickness, volume, folding patterns, shape, etc.. Such metrics can be used to characterize different subject populations. Gray matter information can also be used in the context of functional MRI, for example to have regions of interest defined anatomically or to use surfaces for data co-registration and analysis. Anatomical MRI information can be also used with other functional neuroimaging techniques that give limited or no anatomical information while offering complementary advantages (e.g., MEG, EEG, TMS, PET, NIRS). 

To obtain accurate quantitative information from gray matter the first step is to have high-resolution anatomical images with optimal image contrast between gray matter and from other tissues (like white matter or cerebral spinal fluid). We test and investigate MRI sequence and postprocessing strategies to improve the contrast-to-noise ratio between gray matter and surrounding tissues. We evaluate automated segmentation tools and their test-restest reproducibility. We implement and evaluate harmonized multi-site MRI acquisition and analysis protocols for longitudinal brain morphometry studies.  

​Selected work:

  • Marizzoni M., Antelmi L, Bosch B., Bartrés-Faz D., Müller B.W. , Wiltfang J., Fiedler U., Roccatagliata L., Picco A., Nobili F., Blin O., Bombois S., Lopes R., Sein J., Ranjeva J.P., Didic M., Gros-Dagnac H., Payoux P., Zoccatelli G., Alessandrini F., Beltramello A., Bargalló, N., Ferretti A., Caulo M., Aiello M., Cavaliere C., Soricelli A., Salvadori N., Parnetti L., Tarducci R., Floridi P., Tsolaki M., Constantinidis M., Drevelegas A., Rossini P.M., Marra C., Hoffmann K.T., Hensch T., Schönknecht P., Kuijer J.P., Visser P.J., Barkhof F., Bordet R., Frisoni G.B.*, Jovicich J.*, The PharmaCog Consortium. Longitudinal reproducibility of automatically segmented hippocampal subfields: a multisite European 3T study on healthy elderly. Human Brain Mapping (2015, in press).
     
  • Jovicich J*, Marizzoni M*, Sala-Llonch R, Bosch B, Bartrés-Faz D, Arnold J,Benninghoff J, Wiltfang J, Roccatagliata L, Nobili F, Hensch T, Tränkner A, Schönknecht P, Leroy M, Lopes R, Bordet R, Chanoine V, Ranjeva JP, Didic M, Gros-Dagnac H, Payoux P, Zoccatelli G, Alessandrini F, Beltramello A, Bargalló N, Blin O, Frisoni GB; PharmaCog Consortium. Brain morphometry reproducibility in multi-center 3T MRI studies: A comparison of cross-sectional and longitudinal segmentations. Neuroimage 2013, 83C:472-484. (*equally contributing authors) (PubMed)
     
  • Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, Pacheco J, Albert M, Killiany R, Blacker D, Maguire P, Rosas D, Makris N, Gollub R, Dale A, Dickerson BC, Fischl B.. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths, NeuroImage, 2009;46(1):177-92, (PubMed)
     
  • Miller, M.I., Priebe, C., Qiu, A., Kolasny, A., Brown, T., Park, Y., Ratnanather, J.T., Busa, E., Jovicich. J., Yu, P., Dickerson, B., Buckner, R.L. and Morphometry BIRN. Collaborative Computational Anatomy: An MRI Morphometry Study of the Human Brain via Diffeomorphic Metric Mapping, Human Brain Mapping 2009, 30(7):2132-41. (PubMed)
     
  • Han X., Jovicich J, Salat D, van der Kouwe A, Quinn B.T., Czanner S., Busa E., Pacheco J., Albert M., Killiany R., Maguire P, Rosas D., Makris N., Dale A., Dickerson B, Fischl B., Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer, NeuroImage, 2006, 32(1):180-95.  (PubMed)
     
  • Jovicich J, Czanner S, Greve D, Haley E, Kouwe A, Gollub R, Kennedy D, Schmitt F, Brown G, MacFall J, Fischl B, Dale A. Reliability in Multi-Site Structural MRI Studies: Effects of Gradient Non-linearity Correction on Phantom and Human Data. NeuroImage, 2006, 30(2): 436-443. (PubMed)