Diagnostic & Biomarker Development
MINDSET represents the intellectual property associated with biomarker development at the Mind Research Network. MINDSET offers diagnostic evaluations of imaging data beyond conventional evaluations of radiology, or mere visual inspection. These capabilities include density analysis and functional connectivity analysis.
An example of MINDSET’s capability is a determination whether an individual is ill or healthy based solely on the neuroimaging data. MINDSET is also able to differentiate between different types of psychosis, schizophrenia or bipolar disorder or psychotic depression. Other examples of our work in this area include electrophysiological evaluation using MEG and EEG which can provide for differential diagnosis of mild TBI versus PTSD. And imaging data can be extremely helpful in understanding drug effects, event at low does. For example, we have found that MEG shows that different medications induce different patterns of background brain activity, even at dosages that are significantly below those which cause significant behavioral or cognitive effects. Contact us for more information about our publications and projects in this area (citations to selected publications are listed below).
MINDEST offers unique data analysis services to clinical trial and pharmaceutical companies that are critical to the success of a investigational drug such as identifying homogenous subcohorts of patients with different illness to prospective prediction of future outcomes. To do this we use metabolomics and a combination of structural, functional, diffusion tensor imaging, and MRS, MEG and EEG as warranted. This multi-modal approach provides an unprecendented undertanding of the mechanisms underlying drug action.
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Arribas JI, Calhoun VD, Adali T. Automatic Bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from FMRI data. IEEE Trans Biomed Eng. 2010 Dec;57(12):2850-60. Epub 2010 Sep 27.
Calhoun VD, Maciejewski PK, Pearlson GD, Kiehl KA. Temporal lobe and "default" hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp. 2008 Nov;29(11):1265-75.
Calhoun VD, Sui J, Kiehl K, Turner J, Allen E, Pearlson G. Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder. Front Psychiatry. 2011;2:75. Epub 2012 Jan 10.
Celone KA, Calhoun VD, Dickerson B, Atri A, Chua E, Miller S, DePeau K, Rentz D, Selkoe D, Albert MS, and Sperling RA, Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis, Journal of Neuroscience, vol. 26, pp. 10222-10231, 2006.
Meda SA, Gill A, Stevens MC, Lorenzoni RP, Glahn DC, Calhoun VD, Sweeney JA, Tamminga CA, Keshavan MS, Thaker G, Pearlson GD. Differences in resting-state functional magnetic resonance imaging functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives. Biol Psychiatry. 2012 May 15;71(10):881-9.
Meda S, Narayanan B, Liu J, Perrone-Bizzozero N, Stevens M, Calhoun VD, Glahn G, Shen, L, Risacher L, Sayking A, and Pearlson GD, A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's Disease in the ADNI cohort, NeuroImage, vol. 60, pp. 1608-1621, 2012.
Sui J, Pearlson G, Caprihan A, Adali T, Kiehl KA, Liu J, Yamamoto J, Calhoun VD. Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model. Neuroimage. 2011 Aug 1;57(3):839-55. Epub 2011 May 27.
Sorg C, Riedl V, Muhlau M, Calhoun VD, Drzezga L, Forstl H, Kurz A, Zimmer C, and Wohlschlager A. Selective changes of resting-state networks in patients at high risk for Alzheimer’s disease – an example for profiling functional brain disorders, Proc Natl Acad Sci U S A, vol. 104, pp. 18760-18765, 2007.