2011-2012 - The Italian Academy - Columbia University

MDD subjects. The risk of developing MDD for relatives of depressive
subjects is significantly greater than it is for relatives of healthy
controls. Identifying the population at highest risk could help target
preventive intervention for adolescents and young adults. Researchers,
however, do not currently have objective tools to classify
people at risk and select personalized treatment plans based on
each individual’s likelihood of remission.
Depression is a heterogeneous disorder whose treatment
prediction likely requires information derived from multiple brain
imaging modalities, such as PET, magnetic resonance imaging, and
electroencephalography, which can each assess different brain
structures and functions, and have already provided some predictors
of treatment response for MDD. I am developing a technique
that jointly analyzes data from multiple modalities for early MDD
preventive intervention by investigating whether advanced machine
learning—which can enhance image-derived measures and
elucidate function-structure networks—can automatically identify
young subjects at risk for MDD, and predict each individual’s likelihood
of remission to specific treatments.
The second topic focuses specifically on PET and its need for
extensive sampling of the subject’s arterial blood to determine
the plasmatic concentration of the injected radioligand—the input
function—and be quantitatively reliable. This invasive and costly
procedure, which requires arterial catheter insertion and analysis
of several blood samples, heavily hampers the use of PET in brain
studies. The so-called non-invasive alternative approaches proposed
so far only rarely translate into truly non-invasive procedures,
mostly because they still require multiple arterial samples for scaling
and/or correcting the recovered input function, a problem that
is radioligand-specific.
The simultaneous estimation (SIME) of the input function
whose application to PET I have co-authored (Ogden et al., 2010, J
Cereb Blood Flow Metab 30(4): 816-26), is promising for multiple
radioligands and accounts for metabolite correction, but it still
needs one blood sample for scaling the estimated input function. As
reported in a grant currently under submission with the National
Institutes of Health (1 R01 EB015516-01—“Non-Invasive Fully Quan-
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