Mixed‐effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies

We report that regions‐of‐interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed‐effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real‐world dataset for the neuronal response to nicotine use was acquired using a custom‐made MRI‐compatible apparatus for the smoking of electronic cigarettes (e‐cigarettes). Nineteen participants smoked e‐cigarettes in an MRI scanner using the apparatus with two experimental conditions: e‐cigarettes with nicotine (ECIG) and sham e‐cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2‐test of the MEMA but not from the t‐test, and the corresponding activations were significantly associated with the similarity scores (r = −.52, p = .041, confidence interval [CI] = [−0.78, −0.17]) and the urge‐to‐smoke scores (r = .73, p SCIG), the right orbitofrontal cortex was identified from the χ2‐tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = −.58, p = .01, CI = [−0.84, −0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project. We report that regions‐of‐interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed‐effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was evaluated using the fMRI and behavioral data in a small dataset from our nicotine craving experiment and a large dataset from the Human Connectome Project.