SfN, Nov 6, 2018
*D. SINGLA1, J. KAUR2, A. DHAWAN3, V. MAHAJAN4, R. GARG2;
1Electrical Engin., 2Computer Sci. & Engin., Indian Inst. of Technology, Delhi, New Delhi, India; 3All India Inst. of Med. Sci., Delhi, India; 4Mahajan Imaging Pvt. Ltd., Delhi, India
Cue reactivity tasks have been widely employed in fMRI studies. Due to ease of use and compatibility with General Linear Model (GLM), visual cues are predominantly adopted despite their limitation in terms of replicating real life scenario. We propose using Intersubject Correlation Analysis (ISC) to analyse multi sensory paradigms over GLM based analysis and demonstrate advantages of ISC in a multi sensory paradigm using a case study of craving for alcohol in subjects with heavy alcohol use.
Four male young adults (mean age of 24) with heavy alcohol use whose score on Alcohol Use Disorder Identification Test (AUDIT) was greater than 8, were scanned using a 3T GE MRI Scanner while undergoing a multi sensory craving paradigm. The paradigm included 20 blocks with short videos with fixation cross after every block. Ten videos contained alcohol which were matched with neutral videos based on colour, background, presence of faces, emotions, etc. The order of blocks was randomized once and then kept same across all subjects.
Preprocessing of fMRI data included BET extraction, slice timing correction (ascending interleaved), spatial smoothing (FWHM of 5mm) and temporal filtering of 0.01Hz using FSL. Contrast between alcohol cues and fixation was computed using GLM analysis and compared with statistical maps obtained using ISC analysis. Both the statistical maps were corrected for multiple comparisons using False Discovery Rate (FDR) of 0.05.
With GLM analysis, both visual and auditory regions were observed to be activated along with thalamus. With ISC analysis, regions previously known to be involved in craving such as insula, amygdala, hippocampus, caudate, putamen, anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), orbitofrontal cortex (OFC) were also activated. Refer to the attached figure for the two statistical maps and the activated areas.
We hypothesize that craving is nonlinear in nature. Linear Time Invariant (LTI) assumption of GLM makes it harder to capture craving regions when applied to multi-sensory cues. ISC analysis is a better option in this case.
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