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Description: This study will assess differences in resting state functional magnetic resonance imaging (fMRI) functional connectivity between cocaine dependent (CD) subjects and healthy control (HC) subjects. Functional connectivity within- and between-brain networks estimated by independent component analysis (ICA) will be performed. Networks to be examined include the default mode network (DMN), salience network (SN), left executive control network (LECN), and right executive control network (RECN). The subject sample will include resting state fMRI data from 28 CD subjects with DSM-IV-diagnosed CD and 28 HC subjects compiled from three studies. All subjects in both groups met Parkes et al. (2018) stringent criteria for head motion and were right-handed. Given that the two groups differ statistically in age and years of education attained, we will also perform a second supplementary analysis after matching the two groups more closely for age and years of education. This supplementary analysis will include 22 CD and 22 HC subjects. Furthermore, if there is a statistically significant relationship between age and functional connectivity and/or years of education attained and functional connectivity, we will perform an ANCOVA with age and/or years of education attained as covariates, per the recommendation of a standard statistics textbook (Kutner et al., 2005, pp 347, 919, 940). In order to address the effects of recent cocaine and cannabis use within the CD group, we will also include whether the CD subject’s urine drug screen the day of their MRI scan was positive for cocaine or cannabis as factors in a general linear model. The fMRI acquisition parameters were: repetition time 1500 ms, echo time 30 ms, flip angle 68°, field of view 240 mm (anterior-to-posterior) × 240 mm (left-to-right) × 143.67 mm (foot-to-head), in-plane resolution 3.75 mm × 3.75 mm, 32 axial slices, slice thickness 3.75 mm, interslice gap 0.76 mm, 375 repetitions per run after 12 dummy acquisitions, and total duration 9 minutes. Similar to Woisard et al. (2021), initial removal of signal outliers, slice timing correction, spatial smoothing, and registration to a T1-weighted anatomical scan were performed. Quality control for head motion was performed by eliminating fMRI runs which did not meet the Parkes et al. (2018) stringent criteria. Head motion re-alignment and signal correction was performed using the FSL MCFLIRT motion-correction program (Jenkinson et al., 2002; Woolrich et al., 2004) and ICA-AROMA (Pruim et al., 2015), respectively. Further denoising was performed using the aCompCor procedure implemented in CONN software (Behzadi et al., 2007; www.nitrc.org/projects/conn, RRID:SCR_009550), and ICA components with possible motion-related structured noise are regressed out during the FSL dual regression procedure (Bijsterbosch et al., 2017, pp. 64-65). The T1-weighted anatomical scan was transformed into MNI space using the FSL non-linear transformation module FNIRT, after which these parameters were applied to the denoised fMRI timeseries for transformation into MNI space. High pass filtering (cutoff=125 s), but not low pass filtering, was performed. ICA will be performed with FSL Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC; Beckmann & Smith, 2004; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC) with a dimensionality of 30, in order to match our previously conducted study in opioid use disorder (Woisard et al., 2021). The output will be inspected visually to identify the DMN, SN, LECN, and RECN, based on previous published studies (Menon, 2011; Shirer et al., 2012; Sridharan et al., 2008; Woisard et al., 2021). The dual regression procedure (Nickerson et al., 2017) implemented in FSL will then be used to generate a subject-specific timecourse and subject-specific spatial map for each of the DMN, SN, LECN, and RECN network components. The subject-specific spatial maps will be compared between groups to assess differences in within-network functional connectivity. The subject-specific timecourses will be input into the FSLnets program (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets) to estimate connectivity between networks using partial correlation coefficients. The use of partial correlation coefficients allows for more direct estimation of connectivity between network pairs by regressing out the timeseries of all other networks. These resulting partial correlation coefficients will be Fisher’s r-to-Z transformed, and the subject-specific Z-values will be compared between groups to assess between-network functional connectivity. Further regression analyses will be performed to assess the effect of impulsivity measured by a delayed discounting task on within- and between-network functional connectivity. We hypothesize that CD subjects will have weaker within-network LECN functional connectivity relative to HC, based on previous findings in regular cocaine users (Reese et al., 2019), opioid use disorder (Woisard et al., 2021), and alcohol dependent (Zhu et al., 2017) subjects. We also hypothesize that CD subjects will have stronger within-network DMN functional connectivity relative to HC, based on previous findings in regular cocaine users (Reese et al., 2019) and alcohol dependent subjects (Zhu et al., 2017). We hypothesize that impulsivity measured by a delayed discounting task will negatively correlate with within-network LECN functional connectivity. We also hypothesize that CD subjects will have stronger functional connectivity between the LECN and SN based on previous findings in regular cocaine users (Reese et al., 2019) and alcohol dependent (Zhu et al., 2017) subjects, weaker functional connectivity between the RECN and SN based on previous findings in regular cocaine users and other substance use disorder subjects (Reese et al., 2019; Zhang & Volkow, 2019), and stronger functional connectivity between the DMN and SN relative to HC based on previous research in substance use disorder subjects (Sutherland et al., 2012; Zhang & Volkow, 2019). We will also examine the within-network functional connectivity of the RECN and SN as well as the functional connectivity between the ECN and DMN and between the LECN and RECN in exploratory fashion. 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