Participants

Fourteen right-handed, current smokers (6 males, mean age 25.21 years) participated in this study. Individuals who reported smoking a minimum of 10 cigarettes a day on average for at least 1 year were included. Exclusion criteria were history of physical or mental illness, use of psychotropic medication, history of drug or alcohol abuse, and contraindications for the MRI scanner. Participants received financial compensation for their participation and travel costs. The study was approved by the Ethical Review Committee Psychology and Neuroscience of Maastricht University, and written informed consent was obtained from each participant.

Procedure

Participants were scanned on two occasions. On one occasion, they were instructed to refrain from smoking for at least 10 h before the experiment. On the other occasion, participants were allowed to smoke ad libitum and were specifically asked to smoke a cigarette just before entering the lab in order to achieve maximal satiation. The order of the sessions was randomized across subjects. Upon entering the lab, smoking status was verified by exhaled carbon monoxide measurement. In addition, participants reported at what time they had smoked their most recent cigarette. Furthermore, they filled out the Fagerström Test for Nicotine Dependence (FTND) (Heatherton et al. 1991), the Minnesota Nicotine Withdrawal Scale (MNWS) (Cappelleri et al. 2005) and an MRI eligibility check.

Localizer run

An independent functional localizer run preceded the experiment to localize object-sensitive regions of the brain. Participants were presented with 12 blocks of 10-Gy-scale pictures of common objects and 12 blocks of scrambled versions of the same pictures, all equalized for luminance. All pictures were presented for 1200 ms with an inter-stimulus interval of 600 ms. Stimulus blocks were presented in random order and interleaved with fixation blocks of 12,600 ms.

Experimental run

Stimuli for this run consisted of coloured pictures of cigarettes, pencils and chairs in similar colours, presented centrally on a white background. Participants were presented with three runs, consisting of eight blocks of eight pictures of either cigarettes or pencils or chairs. Pictures were presented for 1500 ms with a 50-ms inter-stimulus interval. Stimulus blocks were presented in random order and interchanged with intervals of 10, 12 or 14 s of rest (see Fig. 1). These intervals allowed the blood oxygenated level-dependent (BOLD) response elicited by the previous stimulus block to return back to baseline before the onset of the adjacent block. To ensure attention to the pictures, participants had to respond to catch trials in which presentation of the preceding picture was repeated.

Fig. 1 Experimental task paradigm. Participants were presented with eight stimulus blocks consisting of eight pictures of either cigarettes or pencils or chairs. The stimulus blocks were presented in random order and interchanged with intervals of 10, 12 or 14 s of rest Full size image

Imaging data acquisition

Functional and anatomical images were acquired in a 3-T Siemens Prisma Scanner (Siemens Medical Solutions, Germany) using a 64-channel head coil. For the localizer run, functional images were obtained for 33 contiguous axial slices (voxel size = 3 × 3 × 3 mm) using a standard gradient echo planar imaging (EPI) sequence (repetition time [TR] = 1800 ms, echo time [TE] = 30 ms, matrix size = 72 × 72, flip angle [FA] = 77°). Functional images for the experimental runs were also acquired with an EPI sequence (TR = 2000 ms, TE = 30 ms, matrix size = 72 × 72, FA = 77°), but for 36 contiguous axial slices (voxel size = 3 × 3 × 3 mm). Both functional measurements covered as much as possible the entire cortical volume. Whole brain structural images were acquired using a 1 × 1 × 1 mm resolution T1-weighted MPRAGE sequence ([TR] = 2250 ms, [TE] = 2.21 ms, [FA] = 9°). The first two volumes of all functional runs were discarded because of possible T1 saturation.

Participants were placed in supine position on the scanner bed, and their heads were fixated with foam pads. Stimuli were projected on a screen at the back of the scanner bore, and were visible to the participants through a mirror attached to the head coil. Responses were made with a hand-held button box. Stimulus presentation was accomplished with Presentation software (Neurobehavioral Systems Inc., Albany, CA) for the localizer run and E-Prime® (Psychology Software Tools, Inc. Sharpsburg, PA) for the experimental runs and was synchronized to the MR data acquisition.

Analyses

Imaging data was pre-processed and analysed using BrainVoyager QX version 2.8 (Brain Innovation, Maastricht, The Netherlands) and Matlab R2014b (MathWorks Inc., Natick, MA). First, functional images were corrected for possible susceptibility-induced EPI distortions by using B0 fieldmaps and the BrainVoyager QX plugin anatabacus (Breman et al. 2009). The functional data were then corrected for slice scan-time differences and 3D head motion (six parameters). In order to enhance the subsequent alignment of the functional images to the anatomical volume, the first and third runs were corrected with the second run as intra-session reference, as the acquisition of this run was temporally adjacent to the anatomical scan. Subsequently, linear trends and low-frequency temporal drifts were removed from the data using a high-pass filter, removing temporal frequencies below 6 cycles per run. The resulting functional data were co-registered to the anatomical volume and transformed to Talairach space. To further reduce the effects of motion-related variability (‘spikes’), a custom Matlab algorithm was applied that for each volume computed the percentage of voxels exceeded the voxel mean intensity by more than 4 standard deviations. Volumes with more than 2% extreme voxels were labelled as affected by ‘excessive’ motion. These volumes were replaced by new values derived from a voxel-wise spline interpolation between the volumes that temporally bordered the removed volumes. In case the excessive volumes were at the beginning or end of the run, the volumes were simply removed.

A standard whole brain univariate random effects GLM analysis was performed to localize bilateral LOC clusters for each subject. The locations of the object selective regions of interest (ROIs) were determined based on a conjunction contrast and anatomical criteria. We selected voxels within the bilateral ventral occipito-temporal cortex that showed a significant response (Bonferroni corrected) in the conjunction analysis between an object-responsive contrast (objects > scrambled objects) and an object-positive contrast (objects > baseline).

The experimental runs were analysed through a pattern classification approach (Cox and Savoy 2003) using custom written code in Matlab. The pseudocode of this analysis is provided as a supplement. This analysis was confined to the resulting voxels from the analysis of the localizer run. First, for each voxel within the region of interest, individual responses to the experimental blocks were estimated by fitting a double-gamma haemodynamic response function (HRF) to the voxel’s time course, using the resulting beta as block estimate. For each ROI and each condition, this resulted in a matrix with dimensions V × B, where V represents the number of voxels, and B stands for the number of blocks. These matrices capture the fine-grained neural information about the conditions that may be apparent in the response patterns. The blocks were labelled according to their corresponding condition (cigarette, pencil, chair) and normalized (z-scored) so as to obtain a mean response of 0 and standard deviation of 1 across all voxels within our region of interest.

The analysis was run using a leave-1-run-out cross-validation. This entails that the classification algorithm was trained on two out of three runs, and its generalization performance tested on the run that was left out. This procedure is repeated three times, ensuring that each run is once used for testing, after which the three prediction accuracies are averaged. First, for the cigarette and pencil conditions, the dataset was split into a training set consisting of two runs (i.e. 16 examples) per condition and a test set consisting of one run (i.e. 8 examples) per condition. The labelled training trials were submitted to a linear support vector machine classifier (C = 1) (Cortes and Vapnik 1995; Mourão-Miranda et al. 2005; Formisano et al. 2008), which performs binary classification on a dataset by placing all cases in a multidimensional space. Each individual block (or example) is expressed as a vector of N features (voxels) in the N-dimensional space. This creates two ‘clouds’ of block vectors in the multidimensional space, one for each condition tested. The algorithm then attempts to define an optimal separation boundary, or hyperplane, between the two classes, given the training data. The generalizability of the trained classifier is then subsequently assessed by feeding the independent and unlabelled test blocks from the left-out run to the algorithm. The accuracy at which the classifier is able to determine the correct labels from these trials given only the response patterns is an indication of successful learning of the algorithm. This, in turn, reflects a meaningful difference in spatial patterns of neural activity elicited by the two conditions.

In order to statistically quantify the prediction results, we empirically estimated the distribution of prediction accuracies under the null hypothesis by a bootstrapping approach: instead of training the classifier on voxel patterns and corresponding class labels, we pseudo randomly assigned the labels to the patterns (randomization of labels per run). The classifier then learned the arbitrary relationship between patterns and classes. By feeding the test trials to the model, we obtained a prediction accuracy under the null hypothesis. Repeating this 1000 times yields the distribution of prediction accuracies under the null hypothesis. This entire analysis was repeated for the cigarette and chair, and pencil and chair classes. Finally, a non-parametric Wilcoxon signed-rank test (Wilcoxon 1945) was performed to statistically test the mean prediction accuracies against the permutations, and to test the difference between the deprivation and satiation sessions. Resulting p values were corrected for multiple comparisons by computing the false discovery rate (FDR) using q = 0.05.

In order to test the variables for possible interaction effects, we computed the two-way F-statistic using a repeated-measures ANOVA with the factors deprivation state (2) × stimulus type (3). Then, we permuted the 2 × 3 condition labels across participants and recomputed the F-statistic in 10,000 iterations, creating the distribution of F under the null hypothesis (Suckling and Bullmore 2004). The probability of finding the true interaction F-statistic under the assumption that the H0 is true was subsequently derived from the permuted F-distribution. A priori planned comparisons between the satiated and deprived conditions were made for each stimulus category, regardless of the outcome of the corresponding overall interaction test. This is a legitimate procedure if the comparisons are suggested by the theoretical basis of the experiment (Winer 1971). Access to the Matlab codes used in this study will be provided upon individual request.