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

Sungman Jo, Hyun Chul Kim, Niv Lustig, Gang Chen, Jong Hwan Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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 <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > 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.

Original languageEnglish
Pages (from-to)5374-5396
Number of pages23
JournalHuman Brain Mapping
Issue number16
Publication statusPublished - 2021 Nov

Bibliographical note

Funding Information:
Electronics and Telecommunications Research Institute (ETRI), Grant/Award Number: 21ZS1100; National Research Council of Science & Technology (NST), Grant/Award Number: CAP‐18‐01‐KIST; National Research Foundation (NRF), Grant/Award Numbers: 2017R1E1A1A01077288, 2016M3C7A1914450, 2015R1A2A2A03004462 Funding information

Funding Information:
Authors thank Da‐Woon Heo for her logistic supports during data collection. This work was supported by the National Research Foundation (NRF) grant, MSIP of Korea (2015R1A2A2A03004462, 2016M3C7A1914450, and 2017R1E1A1A01077288), in part by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT; No. CAP‐18‐01‐KIST), and in part by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. (21ZS1100, Core Technology Research for Self‐Improving Integrated Artificial Intelligence System).

Publisher Copyright:
© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.


  • Human Connectome Project
  • canonical correlation analysis
  • electronic cigarette
  • functional MRI
  • mixed-effects multilevel analysis
  • nicotine craving

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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