In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus. Participants who drank a high amount had overall increases in resting state functional connectivity, and specific increases in reward-related systems including the medial orbitofrontal cortex and the cingulate cortex. Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex. The main findings were cross-validated in an independent longitudinal dataset with 1176 participants, IMAGEN. Further, the functional connectivities in 14-year-old non-smokers  were related to who would smoke or drink at age 19. An implication is that these differences in brain functional connectivities play a role in smoking and drinking, together with other factors. To understand why people become addicted to alcohol or smoking, it is important to look at how the brains of people who use these substances may be different than those who abstain. Many studies show that substance use activates the reward systems in the brain via a chemical called dopamine. Changes or differences in parts of the brain that control decision-making and restraint also have been implicated in substance use.Functional magnetic resonance imaging (fMRI) is one tool scientists can use to explore such differences. It can measure how well different parts of the brain are communicating with each other by measuring their activity when a person is at rest. The patterns of activity reveal which parts of the brain are working closely together or have high functional connectivity and which parts are less well connected, or have low functional connectivity.Cheng, Rolls et al. measured the functional connectivity between different parts of the brain in people who smoke and people who drink alcohol. Smokers had a low overall functional connectivity between brain regions. Specifically, they had weaker connections involving two brain regions that help people change or stop a behavior, the lateral orbitofrontal cortex and inferior frontal gyrus. These differences may make people more impulsive and less able to resist smoking. The stimulating effects of nicotine may enhance communication between different parts of the brain, so people also may use it to overcome some underlying communication deficits. Those who drink alcohol had high overall functional connectivity. Reward-related systems, including the medial orbitofrontal cortex and the cingulate cortex, were especially strongly connected. This may make them more sensitive to the rewarding aspects of drinking, or more impulsive.To confirm their results, Cheng, Rolls et al. analyzed fMRI data from another study. These showed that the characteristic differences in brain connectivity were already present in 14-year olds who would go on to drink or smoke at age 19. This suggests that these functional connectivity differences in the brain make people more likely to smoke or drink. Recent statistics have shown not only the widespread use of cigarettes and alcohol, but also the co-occurrence of these high risk behaviors. Although dopamine mechanisms in the basal ganglia have been implicated in addiction there is also considerable evidence for cortical, especially prefrontal cortical, involvement . For exaThe high levels of comorbidity in using cigarettes and alcohol demonstrated by a wealth of epidemiological and genetic data  makes itMost studies have used relatively small sample sizes and focused on brain regions rather than brain circuits identified with functional connectivity. Further, common and distinct connectivity features underlying the use of different substances such as nicotine and alcohol have not been fully explored with large numbers of participants . In thisIn the HCP participants, 273 links had significantly lower resting state functional connectivity in the smoking group compared to the non-smoking group (after FDR correction p<0.005). In the HCP participants, the top 30 significantly different FC links between the low drinking and high drinking group are shown in −10) (−10). This finding indicates a complementarity between the functional connectivities in drinkers and smokers. That is, if links were high in the smoking group, they tended to be low in the drinking group. This provides evidence that a difference in one direction (lower) may relate to smoking, and in an opposite direction (higher) to drinking.For the HCP participants, −10)  and thisWe also performed a direct comparison between drinkers and smokers, to ensure that the results were not subject simply to possible baseline differences in the different control groups. In this contrast, we found, as predicted, that links involving the lateral orbitofrontal cortex  were significantly (with FDR correction) positive, indicating that the lateral orbitofrontal cortex links are lower in smokers than drinkers. Further, also as predicted, medial orbitofrontal cortex and related areas (including OFCpost and Olf) were significantly more positive, indicating that the medial orbitofrontal cortex links are greater in drinkers than smokers, consistent with the other analyses in this investigation. Details are provided in the source data of To analyse functional connectivity in those who both smoke and drink, the following comparison between four groups was made, with a common baseline group who neither smoked nor drank. Functional connectivity was compared over four groups  using a common baseline comparison group (a no smoking and low drinking group n = 198) from the HCP dataset. For Thus the group that both smokes and drinks shows a combination of the links found to be different in the smoking group, and of those found to be different in the drinking group. However, it was noticeable that in the combined smoking and drinking group the difference in functional connectivity from the controls for the increases and decreases  found were smaller than in the only drinking or only smoking group .In summary, participants who both smoked and drank had a combination of the specifically different links in those who only smoked or only drank, and these links had on average lower functional connectivity. An implication is that the differences in functional connectivity in smokers and drinkers described in this paper can, when combined, be related to, and potentially contribute to, both smoking and drinking in the same individual.Figure 5C, D shows that for the drinking group, there was a positive correlation between the mean strength of FCs for the links for the whole brain  and a trend also for significantly different links  and the amount of drinking across individuals (drinks per drinking day in the past 12 months). (This is consistent with the finding that the FCs are higher in drinkers).Further, we found that this pattern of changes with the amount of use also applied to most of the significant links for both smoking  and drin−4) and DDisc_AUC_40K . There was also a negative correlation between the amount of drinking and both the delay discounting scores, DDisc_AUC_200 . The findings are consistent with decreases of functional connectivity in areas such as the lateral orbitofrontal cortex and the adjacent inferior frontal cortex being associated with greater impulsivity in the smoking group as measured with the delay discounting score.For the drinking group, the significant increases in functional connectivity in the medial orbitofrontal cortex found in this group  were assWe further found that these measures of impulsivity had relatively high correlations, with respect to the 68 behavior measures in the HCP, with the smoking and drinking, and their related functional connectivities.Using a separate dataset (IMAGEN ) we cros−10, one sample t-test). With respect to cross-validation of the specific links identified in the HCP dataset, −10).The results of this cross-validation for smoking are shown in −10, one sample t-test). −10). As described in the Supplementary Material, the comparison group was the low-drinkers.For the drinking, the results of the cross-validation are also shown in −10, −10, To investigate whether the functional connectivity at 14 might be related to whether a participant became a smoker by age 19, a longitudinal analysis with the same IMAGEN participants was performed. The 19 years olds were split into two approximately equal size groups, of smokers vs non-smokers. For each link, its value in the 14 years olds was compared between those who were smokers and non-smokers at age 19 with a two sample t test. For the overall connectivity, this provided 4371 t values. A one-sample t-test was then performed for whether the mean of these t-values was lower than 0 in those who became smokers at 19. That test was significant  in fema−4) and the amount of drinking  at age 19. The results for impulsivity at age 14 are shown in −4). The 14 year olds who will drink at age 19 (n = 527) have higher impulsivity than those who will not drink at 19 (n = 298) . Thus higher impulsivity at 14 is associated with who will smoke, or drink, at age 19.The analyses of impulsivity with the IMAGEN dataset are considered next. First, the impulsivity is significantly positively correlated with the frequency of smoking  which is involved in reward processing, and in related structures including the anterior cingulate cortex, parahippocampal cortex, supramarginal gyrus, insula, and superior temporal gyrus. In addition, the mean functional connectivity across the whole brain was lower in smokers than non-smokers. In contrast, mean functional connectivity across the whole brain was higher in high drinkers than low drinkers. The differences in both the overall and the identified links were correlated with the amount of smoking or drinking. For both the smoking and drinking, the differences in overall functional connectivity were correlated with increased impulsivity. These findings with the HCP dataset were cross-validated with the IMAGEN dataset. In addition, analysis of the IMAGEN dataset showed that for the significantly different links between smokers and non-smokers identified in the HCP dataset, those links were lower at age 14 in the IMAGEN dataset in those who became smokers at 19  which is implicated in stopping and changing behavior, including when reward is not received , and wheThe increased functional connectivity in drinkers relative to non-drinkers involved the medial orbitofrontal cortex which is involved in reward processing  and struIn the short term just after administration, alcohol increases impulsivity and decreases behavioral inhibition  and inc. The mecTo check the main findings of this investigation, and to control for possible differences in the comparison groups, we directly compared functional connectivity in drinkers and smokers. The hypotheses were that functional connectivity involving the lateral orbitofrontal cortex would be smaller in smokers than drinkers; that functional connectivity involving the medial orbitofrontal cortex would be greater in drinkers than in smokers; and that the mean functional connectivity would be higher in drinkers than non-smokers. All these hypotheses were confirmed by the direct comparison of drinkers – smokers in the Results section see .Using a separate dataset (IMAGEN ) we crosThe low functional connectivity that we describe here in smokers in frontal and associated areas may be reflected in the hypofrontality that has previously been described in smokers and has been associated in genome-wide association studies with single-nucleotide polymorphisms (SNPs) in the human CHRNA5 gene, encoding the α5 nAcetylcholine receptor subunit, that increase the risks for both smoking and schizophrenia ; SchizopFor female drinkers there was also an association between high functional connectivity at age 14 and who would be in the high drinking group at age 19. In this case a possible account for the drinking is that it is related to increased functional connectivity or sensitivity of medial orbitofrontal cortex and related reward circuitry, which makes the alcohol more rewarding, and which may also because of the increased sensitivity to reward produce some increase in reward-related impulsivity . The acuWe note that although there were overall higher in FC in drinkers, and lower in smokers, to place this in context, FCs were also smaller in males than females in this dataset in smokers and drinkers . AccordiThe differences in functional connectivity described in this investigation were not due to any obvious possible confounding factors such as gender, ethnicity, level of education, marijuana use, or basic brain structure (volume of gray and white matter), which were regressed out of the analysis.The use of resting state fMRI is useful for this type of investigation as it does not require task performance that might differ between participants, and such studies can for example predict behavior  and taskPossible limitations of this investigation are that linear analyses were used. It is also important to stress that all participants were healthy adults, and that the levels of smoking and drinking exhibited were not directly associated with diagnoses of substance dependence, although they may be relevant to these disorders. Thus the effect size of this study involving the normal use of alcohol and cigarettes could be lower than in a case/control study. But the design of this study, use of individuals in the general population, enable this investigation to have impact on public health, since the use of substances for most of the smokers and drinkers was within a normal range, and we could identify these changes by the use of two large-scale datasets. Although the current study was based on the general population, the findings are in general consistent with previous studies based on clinical populations. For example, chronic nicotine exposure associated with lower functional connectivity was reported in a number of studies . In addiAs shown in In conclusion, we have related for the first time reduced functional connectivity involving the lateral orbitofrontal cortex and related inferior frontal gyrus to smoking. We interpret the smoking as being related to the increased impulsivity and decreased behavioral inhibition. In addition, smokers have globally lower functional connectivity. A possible interpretation is that smokers may self-administer nicotine in order to increase brain functional connectivity and thereby attention and alertness. We also describe for the first time that drinkers have increased functional connectivity of the medial orbitofrontal cortex, a reward area, and globally increased functional connectivity as well as impulsive behavior. An interpretation is that there is a role in drinking of increased reward sensitivity implemented in the medial orbitofrontal cortex. The increased impulsivity in drinkers may also be related to increased reward processing in the medial orbitofrontal cortex and its connected areas. These differences in functional connectivity may play a causal role, as shown by analyses showing that there was an association between smoking or drinking at age 19 and the functional connectivities at age 14 of individuals who at 14 are not smoking or drinking. In both smokers and drinkers, the increased impulsivity may exacerbate substance use. These findings open the way for further investigations of the extent to which these differences contribute to the substance use, or are caused by it. In any case, we note that there may be many factors that influence smoking and drinking, though we have revealed some of the possible underlying factors and brain mechanisms in this investigation.https://www.humanconnectome.org/). It should be noted that the HCP dataset was not specifically designed for the study of substance use, but it is useful for investigating the impact of alcohol and cigarette use in a community sample. Two resting state fMRI acquisitions on different days were used. The four resting-state runs of approximately 15 min each were acquired in separate sessions on two different days, with eyes open with relaxed fixation on a projected bright cross-hair on a dark background. The WU-Minn HCP Consortium obtained full informed consent from all participants, and research procedures and ethical guidelines were followed in accordance with the Washington University Institutional Review Boards . The demographic characteristics of participants are summarized in http://www.humanconnectome.org/).The dataset used for this investigation was selected from the November 2015 public data release from the Human Connectome Project , WU-Minn Consortium. Our sample includes 831 subjects  scanned on a 3 T Siemens connectome-Skyra scanner. The HCP consortium is a public shared large-scale neuroimaging dataset which aims to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults  and whicWe obtained minimally preprocessed R-fMRI data conducted using the HCP Functional Pipeline v2.0  involvinlization . In thislization  and AFNIlization . Brieflylization  with altlization .After preprocessing, the whole brain (gray matter) was parcellated into 94 regions of interest (ROI) according to the automated anatomical labeling (AAL2 ) atlas  47 regi based onThe following factors were considered in choosing the measures to define the groups. The main purpose of the current study was to investigate whether there were differences in functional connectivity that were related to smoking or drinking. Accordingly, in the initial analysis, we categorized the participants for the smoking analysis into two groups, one which used significant amounts of cigarettes, and the other using no or low amounts of cigarettes. The same was performed for the division into two groups with respect to the amount of alcohol drunk. In making the cutoff, we ensured that there were reasonable numbers of participants in the high and the low amount groups. After the initial analysis, we wished to investigate the effect of the different amounts of use of cigarettes or alcohol, and therefore used measures of the different amounts of drinking of each participant, and the number of times each participant had smoked, as described in the results section  and 6.Accordingly, to investigate smoking-associated functional connectivity, we divided all participants into three groups according to their smoking history (SSAGA_TB_Smoking_History), that is non-smokers (417 participants), occasional smokers (161 participants) and regular smokers (203 participants). The non-smoker group contained participants who had never smoked during their lifetime. The occasional smoker group contained participants who experimented with cigarettes at least once in their lifetime but never became a regular smoker. The regular smoker group included those who smoked at least 100 cigarettes in his or her lifetime and smoked at least one day per week when they were smoking regularly.  In the main analyses described in this paper to investigate whether there was an effect of smoking, two of these groups were used, the non-smoker and regular smoker groups. The third group was helpful in further analyses to investigate effects of the amount of smoking .−10). In order to compare the results for smoking and drinking, only participants with information about both drinking and smoking behavior were included. The demographic characteristics of the participants in each group are summarized in To investigate drinking-associated functional connectivities, we divided all participants into two groups. A Low Amount (LA) group with 311 participants drank one or fewer drinks per drinking day in the past 12 months. A High Amount (HA) group with 470 participants drank two or more drinks per drinking day in the past 12 months.  It should be noted that the ‘amount’ of drinking in the current study refers to the amount of drinks per drinking day . The rationale for the use of this measure is provided in the Discussion section, and a complementary analysis showed that there was a good correlation between the association pattern of the functional connectivities when the measures were the 'total drinks' and the 'drinks per drinking day'  and smoking, two sample two-tailed t tests were used to test whether smoking was associated with functional connectivity after removing the confounding effects of age, gender, years of education, race, handedness, head motion , BMI, blood pressure (diastolic and systolic), total gray matter volume, total white matter volume, drinking, and marijuana use. Considering the purpose of this study described above and the sample size in each sub-group, we used a two sample t-test rather than a 2 × 2 ANOVA. Then a Storey's FDR procedure was used to correct for multiple comparisons . Storey’We explored the relationship between the functional connectivity links identified above as different in substance use groups with behavioral measures (i.e. drinks per drinking day in the past 12 months and the amount of smoking). Specifically, a partial correlation analysis was used to measure the correlation between the identified functional connectivity and these behavioral measures with removal of the confounding variables of age, gender, years of education, race, handedness, head motion (mean FD), BMI, blood pressure (diastolic and systolic), total gray matter volume, total white matter volume and use of the other substance.We also investigated the relationship between the functional connectivity links identified above as different in substance use groups and behavioral scores related to self-regulation and impulsivity. We performed a partial correlation of the delay discounting score with the amount of drinking per drinking day and the amount of smoking respectively. The score of self-regulation and impulsivity was based on the delay discounting task which measures the undervaluing of rewards delayed in time. We used an area-under-the-curve discounting measure (AUC) that provides a valid and reliable index of how steeply an individual discounts delayed rewards . SpecifiWe used another separate large longitudinal fMRI dataset (the IMAGEN dataset ) which iIMAGEN adopted a longitudinal design to collect genetic, neuroimaging, environmental, and behavioural data in the UK and Europe, starting at age 14 years old with a sample 2087 healthy adolescents, and followed at ages 16 and 19 years. The neuroimaging data were acquired at ages 14 and 19 years . The demIMAGEN 19: For the dataset at age 19, by using a similar criterion to define the sub-groups as in the HCP database, a Low Amount (LA) drinking group with 378 participants who drank two or fewer drinks per drinking day, and a High Amount (HA) drinking group with 566 participants who drank three or more drinks per drinking day, were identified.The small difference in the different definitions for the drinking groups for the HCP and IMAGEN samples is because they used different questionnaires. We used the European School Survey Project on Alcohol and Other Drugs (ESPAD) to assess the lifetime drinking and smoking in the IMAGEN dataset. The measure of drinking in the IMAGEN dataset is as follows:“How many drinks containing alcohol do you have on a typical day when you are drinking?1: 1 or 22: 3 or 43: 5 or 64: 7 to 95: 10 or more’For comparison, the measure of drinking in the HCP dataset is:‘Drinks consumed per drinking day in past 12 months: 0, 1, 2, 3, 4, 5–6 = 5, 7+ = 6.’In fact, the control group for the HCP and IMAGEN datasets were similar, in that they had not drunk alcohol.For the smoking dataset, a smoking group (regular smoker) was defined as those who smoked at least 100 cigarettes in his or her lifetime and smoked at least one day per week when they were smoking regularly (180 participants), and a non-smoking group .IMAGEN 14: Since almost all participants did not take any cigarettes and alcohol at age 14, it was not suitable to assess the difference between smoking and non-smoking group (or low drinking and high drinking group). However, this longitudinal data enabled us to investigate possible causal associations between substance use behaviors and functional connectivities. Therefore, for the dataset at age 14, to investigate possible causal associations between drinking and functional connectivities, we focused only on the subjects who drank two or fewer drinks per drinking day. (This was the great majority of participants.) We also divided all participants into two groups according to their drinking behavior at age 14 and 19. Specifically, a Future Low Amount (FLA) group with 86 participants drank with two or fewer drinks per drinking day at both age 14 and 19. A Future High Amount (FHA) group with 91 participants drank with three or more drinks per drinking day at age 19. It should be noted that both of these groups drank two or fewer drinks per drinking day at age 14.Similarly to the above, for the analysis of smoking, we still focused on the participants who never smoked cigarettes at age 14: specifically, on a future non-smoking group with 56 participants who never smoked at both age 14 and 19; and a future regular smoking group with 19 participants who experienced smoking at age 19.Then a two sample two-tailed t-test was used to assess the difference between the (F)LA group and (F)HA group and the smoking and non-smoking groups in the datasets at both the ages of 14 and 19 after removing the effect of gender, BMI, head motion (mean FD) and the use of the another substances use.We also investigate the impulsivity of participants at age 14 by using the IMAGEN dataset. The impulsivity measure is based on the Substance Use Risk Profile Scale  by usingI often don't think things through before I speak.I often involve myself in situations that I later regret being involved in.I usually act without stopping to think.Generally, I am an impulsive person.I feel I have to be manipulative to get what I want.We used the European School Survey Project on Alcohol and Other Drugs (ESPAD) at ages 14 and 19 years to assess life time drinking and smoking. The primary question of interest was regarding lifetime alcohol and cigarette use and the questionnaire were as follows :1. On how many occasions during your lifetime have you smoked cigarettes?0: 01: 1–22: 3–53: 6–94: 10–195: 20–396: 40 or mor2. How frequently have you smoked cigarettes during the last 30 days?6: more than 20 cigarettes per day999: non smoker0: not at all1: less than one cigarette per week2: less than one cigarette per day3: 1–5 cigarettes per day4: 6–10 cigarettes per day5: 11–20 cigarettes per day3. How many drinks containing alcohol do you have on a typical day when you are drinking?1: 1 or 22: 3 or 43: 5 or 64: 7 to 95: 10 or more4. On how many occasions over the last 12 months have you had any alcoholic beverage to drink?0: 01: 1–22: 3–53: 6–94: 10–195: 20–396: 40 or moreData preprocessing was performed using DPARSF  The complete HCP pipeline.Data pre-processing was carried out using FSL (FMRIB Software Library), FreeSurfer, and the Connectome Workbench software. All the data preprocessing procedures were performed by the human connectome project (HCP) as described in . Briefly2)The HCP minimal preprocessing pipeline + other standard preprocessing procedures but without global signal removal.The HCP minimal preprocessing pipeline was used for the HCP data set. This pipeline includes artifact removal, motion correction and registration to standard space. Then standard preprocessing procedures were applied to the fMRI data: first the linear trend and quadratic term were removed from these functional images; several nuisance signals were regressed from the time course of each voxel using multiple linear regression, including cerebrospinal fluid, white matter and head motion parameters; finally, temporal band-pass filtering (0.09–0.1 Hz) was performed to reduce the influence of low-frequency drift and the high-frequency physiological noise. This pipeline has been widely used in previous studies based on the HCP dataset  and our 3)The HCP minimal preprocessing pipeline + other standard preprocessing procedures and with global signal removal.Same as pipeline two but with the global signal removal. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, including Heidi Johansen-Berg as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard Ivry as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Dieter Meyerhoff (Reviewer #2).Thank you for submitting your article "Decreased brain connectivity in smoking contrasts with increased connectivity in drinking" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The authors describe a large sample size, well controlled and cross validated study of similarities and differences between smokers and drinkers. They find a general reduction in brain-wide functional connectivity in smokers and a general increase in brain-wide FC in drinkers. Further, the degree of these changes correlate with a measure of impulsivity. The study seeks to quantify several methodological issues important to neuroimaging  and presents a compelling and novel set of findings of contrasting whole brain connectivity patterns associated with exposure to difference substances of abuse.Essential revisions:1) Minimization of comorbid results: The minimization of the comorbid results is a missed opportunity. At the least, the smokers who drink should be further characterized and I would encourage the inclusion and interpretation of Supplementary Figure 4 in the main text. As poly drug use and comorbid disorders are the rule not the exception for real world substance use disorder, these results are worthy of more attention. This is relevant in light of the contrasting findings when exposure to each drug is interrogated individually. What is the nature of the interaction in smokers who drink?2) Methodological concerns: The section "correlation between functional connectivity and the amount of smoking and drinking" appears to suffer from circular logic: Based on smoking group assignment, links that are shown to be different are then correlated with the frequency of smoking? This does not appear to be a valid statistic test as the initial identification of nodes is based on smoking behavior which is then correlated with the results. Can the authors comment?3) The motivation for the 14 year old to 19 year old IMAGEN data could be more fully articulated. The cross validation with IMAGEN data makes sense, but the authors do not properly introduce a developmental hypothesis. Similarly, the gender specific analysis described in the subsection “Association between the functional connectivity at age 14 and the smoking and drinking at age 19”, are not motivated in the Introduction. Are these post hoc tests?4) With large sample sizes even small effects can be statistically significant. It is therefore particularly important to comment on effect sizes as well as statistical significance. Some of the effects, particularly the correlations, are very small (r<0.1).5) The drinking groups are categorised based on number of drinks per drinking day. Although this measure is commonly used in studies of regular drinkers it is less commonly used in community samples that presumably include infrequent drinkers. The authors should better justify their choice and should report whether relationships exist with measures of average/total drinks per week/month. It would also be important to report on what the relationships are between drinks per drinking day and e.g., number of drinking days/total drinks etc.6) The authors report interesting relationships to behavioural measures of impulsivity. I would like to know how specific these relationships are to impulsivity as opposed other behavioural characteristics. This is particularly important given that these effect sizes are fairly low, despite being statistically significant.7) Resting FC can fluctuate over time with various factors such as tiredness, time of day, caffeine, mood etc. To what extent have those factors been taken into account? Given the focus of the study it would be particularly relevant to report on when the most recent cigarette/drink was consumed. If smokers/drinkers had recently consumed then that might affect the signal. On the other hand, if the study protocols required abstinence for a period prior to the scan then is it possible that some of effects seen relate to withdrawal/wanting/anxiety? If information about most recent drink/cigarette is not available then it should be commented on as a limitation.8) There is very little description/characterization of the study participants. How were they recruited? How were they assessed and tested for potential pathologies that could affect the outcome measures? How was comorbid substance use assessed/diagnosed? Any selection based on study targets (drinking/smoking)? This information is necessary when making comparisons to other studies.9) The manuscript Introduction would greatly benefit from being set in the context of previous work on FC in smokers and drinkers. While the authors cite several smaller studies, they do not really summarize the current knowledge on the topic. And in the Discussion, reference should be made to how this new data fits in with the findings in this previous work.10) Please clarify why 2 simple t-tests were used with corresponding covariate corrections rather than a 2x2 ANOVA.11) When discussing 'complimentarity' in the subsection “Comparisons between drinking and smoking” (is that even a word?), the meaning of it and the findings remain rather elusive. Are the authors referring to the possibility that those who are vulnerable to FC alterations associated with drinking may be equally vulnerable to FC alterations  associated with smoking? Essentially reflecting some vulnerability/resilience to the effects of substance use in general on FC?12) Although quite catchy in its simplicity, the title of the manuscript should include a hint to the nature of the study participants – these are not individuals diagnosed with a substance use disorder. Some of the findings may be specific to this control population and may be different in pathological cases with full blown substance use disorder diagnoses. This is properly dealt with as a limitation in the Discussion.13) The Abstract should make reference to the impulsivity measures as well.14) In the subsection “Statistical Analysis”, give the n for all subgroups or give them in the corresponding Figures 4 and 5.15) Figure 5A-D could be interpreted to reflect a threshold effect on impulsivity in smokers (only regular smokers show effects). Similarly, among those who drink, any drinking (even 1 drink per day) is associated with greater impulsivity . This brings up the question what happens when you compare no drinking to any drinking? Are the results as strong as low vs. high or will you be power-limited?eLife. Your revised article has been favorably evaluated by Richard Ivry (Senior Editor), Heidi Johansen-Berg as Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "Decreased brain connectivity in smoking contrasts with increased connectivity in drinking" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Essential revisions:1) Brain effects in a community sample of light smokers and/or drinkers may not extrapolate to clinical samples studied previously. This point needs to be more clearly acknowledged in the Discussion. Currently the Discussion mentions in the limitations section that levels of smoking and drinking were within the normal range and so effect sizes may be lower than clinical studies. However, the possibility that different patterns of brain variation may be found within the normal range versus in clinical populations is not mentioned.2) Regarding original point 15 – we agree with the authors that comparing the small sample of non-drinkers to the much larger sample of those who consumed any alcohol is not a good idea. Given this imbalance, the authors are simply not able to address the issue of a potential threshold effect. This should be acknowledged in the Discussion.in the same circuits, why does the combination of smoking and drinking lead to decreases? They don't address this issue in their edited manuscript.3) The data for individuals who both smoke and drink is given almost no interpretation in the Discussion. The edits to include these results  in the main manuscript are great. However, the authors should provide some interpretation (however speculative) about why these dual use individuals have a functional connectivity profile that appears to look like that of smokers only. This appears to argue against their idea of "complimentarity." That is, if smoking reduces FC and drinking increases FC  Essential revisions:1) Minimization of comorbid results: The minimization of the comorbid results is a missed opportunity. At the least, the smokers who drink should be further characterized and I would encourage the inclusion and interpretation of Supplementary Figure 4 in the main text. As poly drug use and comorbid disorders are the rule not the exception for real world substance use disorder, these results are worthy of more attention. This is relevant in light of the contrasting findings when exposure to each drug is interrogated individually. What is the nature of the interaction in smokers who drink?Thank you for the suggestion, which we have followed: Supplementary Figure 4 has been moved to the main text, and is now Figure 4. To help interpret the results shown in Figure 4, we added a new section “Comparison between smokers, drinkers, and both smokers and drinkers”.2) Methodological concerns: The section "correlation between functional connectivity and the amount of smoking and drinking" appears to suffer from circular logic: Based on smoking group assignment, links that are shown to be different are then correlated with the frequency of smoking? This does not appear to be a valid statistic test as the initial identification of nodes is based on smoking behavior which is then correlated with the results. Can the authors comment?First, for the correlation between the mean strength of whole brain functional connectivity and the amount of smoking and drinking, we believe that there is no problem of circular logic. This is because the whole brain functional connectivities are used in this analysis, rather than the links identified in the association analysis.However, to address the point raised, for the correlation between the mean strength of the identified brain functional connectivity and the amount of smoking and drinking, we have reanalyzed the data without the control group. For smoking, we calculated the partial correlation between the mean strength of the different functional connectivities and the frequency of smoking in the group without non-smokers. We found that the correlation was also significant  after removing the non-smoking group. In addition, we also performed the same analysis for the individual links by focusing on the group with a history of smoking. The results showed that the correlation value for 269 out of the 273 identified links were negative; and further that 54 out of the 273 identified links were correlated with the number of years smoked (uncorrected p<0.05).For drinking, we also calculated the partial correlation between the mean strength of the different functional connectivities and the drinks per drinking day in the high drinking group. We found that the correlation tended towards significance  after removing the low-drinking group.In addition, we also performed the same analysis for the individual links by focusing on the high drinking group. The results showed that the correlation value of 177 out of 214 identified links were positive, and 38 out of the 214 identified links were correlated with the drinks per drinking day (uncorrected p<0.05).To address this issue, the following changes have been made to the Results section of the main text of the paper."Correlation between the functional connectivity and the amount of smoking and drinkingFigure 5A, B shows that for the smoking group, there was a negative correlation between the mean strength of functional connectivity for both the significantly different links  and the links for the whole brain  with the frequency of smoking across individuals. […] In addition, 177 out of 214 links associated with drinking showed a positive correlation with the drinks per drinking day, and 38 of these 214 identified links were significant (p<0.05 uncorrected)."3) The motivation for the 14 year old to 19 year old IMAGEN data could be more fully articulated. The cross validation with IMAGEN data makes sense, but the authors do not properly introduce a developmental hypothesis. Similarly, the gender specific analysis described in the subsection “Association between the functional connectivity at age 14 and the smoking and drinking at age 19”, are not motivated in the Introduction. Are these post hoc tests?We have followed this advice, and added to the Materials and methods section, to make these points more explicit."Cross-validation with the IMAGEN datasetWe used another separate large longitudinal fMRI dataset ) which included 1176 participants to test whether the findings based on the HCP dataset could be cross-validated with an independent dataset, which would considerably strengthen the findings. […] The IMAGEN dataset also enabled investigation of gender differences in the behaviors that may be predictable from brain functional connectivity in this important period of development."4) With large sample sizes even small effects can be statistically significant. It is therefore particularly important to comment on effect sizes as well as statistical significance. Some of the effects, particularly the correlations, are very small (r<0.1).First, the HCP dataset was not specifically designed for the study of substance use. It contains numerous behavior measurements, and the use of alcohol and cigarettes are two of them. We cannot expect a high effect size in a sample drawn from the general community, as contrasted with for example a study that selects people with for example heavy smoking and compares then to controls. In more detail, we show that many functional connectivity links are related to drinking and smoking, though each has a relatively low effect size .Although the effect size for smoking and drinking is relatively low in the HCP dataset, the main findings, such as increased functional connectivity in high drinking group and decreased functional connectivity in smoking group, are robust, in that they were cross-validated in an independent dataset, IMAGEN. Moreover, we do demonstrate with the IMAGEN dataset that the effect size is sufficient to enable prediction from the brain functional connectivities that we identify at age 14 of who will smoke at age 19. This is a good demonstration that the effects described in this paper are meaningful, in that predictions can be made from them about what will happen five years later.We have followed the advice, and have included the following in the Discussion section of the revised paper to address this point:"It is also important to stress that all participants were healthy adults, and that the levels of smoking and drinking exhibited were not directly associated with diagnoses of substance dependence, although they may be relevant to these disorders. […] This is good evidence that the effects described in this paper are meaningful, in that the functional connectivity at age 14 can be related to what will happen in terms of smoking and drinking five years later."5) The drinking groups are categorised based on number of drinks per drinking day. Although this measure is commonly used in studies of regular drinkers it is less commonly used in community samples that presumably include infrequent drinkers. The authors should better justify their choice and should report whether relationships exist with measures of average/total drinks per week/month. It would also be important to report on what the relationships are between drinks per drinking day and e.g., number of drinking days/total drinks etc.The rationale for the categorization of participants is described in the Materials and methods section, and is considered further in the Discussion as follows: “The amount of alcohol consumed on a drinking day, the ‘amount’ measure, may well be related to how rewarding the consumption of the alcohol is. […] That in turn is consistent with the higher functional connectivity of the medial orbitofrontal cortex found in the High Amount drinkers in this investigation, and with the evidence that the medial orbitofrontal cortex contains a representation of reward value .”-10). To make sure that this point was covered clearly in the paper, we added the following to the Materials and methods section:To further address this issue, we also analysed the dataset by using the measure of 'total drinks'. The 'total drinks' is defined as the drinks per drinking day (SSAGA_Alc_12_Drinks_Per_Day) times the frequency of any alcohol use (SSAGA_Alc_12_Frq). We then calculated the partial correlation between the total drinks and functional connectivity after removing the same covariates as in the previous analysis. The results show that the association pattern of the total drinks and the drinks per drinking day are quite similar, as shown by the correlation coefficient .""… the amount of drinks per drinking day . The rationale for the use of this measure is provided in the Discussion section, and a complementary analysis showed that there was a good correlation between of association pattern of the functional conenctivities when the measures were the 'total drinks' and the 'drinks per drinking day'  The authors report interesting relationships to behavioural measures of impulsivity. I would like to know how specific these relationships are to impulsivity as opposed other behavioural characteristics. This is particularly important given that these effect sizes are fairly low, despite being statistically significant.To test whether the impulsivity is specifically correlated with the amount of substance use, we calculated the correlation between the amount of substance use and numerous available behavioral measures in the HCP dataset , such as emotion, cognition, motor, etc. The details for each item are available in the HCP websitehttps://wiki.humanconnectome.org/display/PublicData/HCP+Data+Dictionary+Public-+Updated+for+the+1200+Subject+Release. The results are shown in the following table. For the correlation between behaviour assessments and smoking, the top 12 behaviour assessments were all related to impulsivity (Delay Discounting). For the correlation between behaviour assessments and drinking, 6 out of the top 13 behaviour assessments were related to impulsivity. For the canonical correlation between behaviour assessments and FCs associated with smoking, the impulsive measurement (DDisc_AUC_200) ranks 13th. For the canonical correlation between behaviour assessments and FCs associated with drinking, the impulsive measurements  rank 11th and 18th respectively. In general, impulsivity is significantly correlated with both the substance use and the identified links compared with other behaviour assessments.We have addressed the point briefly in the new text added to the end of this part of the Results, as follows:"We further found that these measures of impulsivity had relatively high correlations, with respect to the 68 behavior measures in the HCP, with the smoking and drinking, and their related functional connectivities."7) Resting FC can fluctuate over time with various factors such as tiredness, time of day, caffeine, mood etc. To what extent have those factors been taken into account? Given the focus of the study it would be particularly relevant to report on when the most recent cigarette/drink was consumed. If smokers/drinkers had recently consumed then that might affect the signal. On the other hand, if the study protocols required abstinence for a period prior to the scan then is it possible that some of effects seen relate to withdrawal/wanting/anxiety? If information about most recent drink/cigarette is not available then it should be commented on as a limitation.To minimize any effects of confounding factors in the analyses, we included 13 covariates, including age, gender, years of education, race, handedness, head motion, BMI, blood pressure (diastolic and systolic), etc. The HCP dataset does not include information about tiredness, time of day, caffeine, and mood when the participants came to perform the MRI scanning. But the dataset does contain information about the total drinks and total times smoked in the past 7 days. To confirm that our findings are robust in respect to the most recent drink/cigarette use, we re-analyzed the data by adding the total drinks and total times smoked in the past 7 days as two covariates. For smoking, the association pattern of functional connectivity with these two covariates is very consistent with the results described in the main paper . For drinking, the result with these two covariates also is very consistent with the results described in the main paper . So the association pattern between functional connectivity and the substance use are almost exactly the same in the cases with and without the covariates of the most recent drink/cigarette use. The following changes were made in the Materials and methods section to address this issue."The effects of any use of the other substance were always removed by regression when we performed the analysis on the use of one of the substances. […] This provides evidence that it is whether one is a smoker or drinker that is relevant to the results described in this paper, and not just the most recent drug use.”8) There is very little description/characterization of the study participants. How were they recruited? How were they assessed and tested for potential pathologies that could affect the outcome measures? How was comorbid substance use assessed/diagnosed? Any selection based on study targets (drinking/smoking)? This information is necessary when making comparisons to other studies.The Human Connectome Project consortium is a public shared large-scale neuroimaging dataset which aims to map macroscopic human brain circuits and their relationship to behavior in a population of 1200 healthy adults. This dataset has been widely used in neuroimaging studies, and more than 500 papers have been published based on this valuable data. The full details on the subject recruitment, visits, behavioral testing and the parameters of the scanner have been provided in the HCP website (https://www.humanconnectome.org/) and the papers published by the HCP team.It should be noted that the HCP dataset was not specifically designed for the study of substance use. All participants are healthy adults. The following sentences were added in the subsection “Participants and data preprocessing” to make this clear, and to address the point:"…Our sample includes 831 subjects  scanned on a 3-T Siemens connectome-Skyra scanner. [...] It should be noted that the HCP dataset was not specifically designed for the study of substance use, but it is useful for investigating the impact of alcohol and cigarette use in a community sample."9) The manuscript Introduction would greatly benefit from being set in the context of previous work on FC in smokers and drinkers. While the authors cite several smaller studies, they do not really summarize the current knowledge on the topic. And in the Discussion, reference should be made to how this new data fits in with the findings in this previous work.To address this point, we have added the following to the Introduction:"Resting state functional connectivity differences involving the prefrontal cortex and insula have also been described in smokers ."We have not returned to these reviews and investigations in the Discussion, because the sample size used in this investigation, and the cross-validation with an independent sample in the IMAGEN dataset, are more powerful than previous investigations.10) Please clarify why 2 simple t-tests were used with corresponding covariate corrections rather than a 2x2 ANOVA.First, as described in the Materials and methods, the main purpose of the current study was to investigate whether there are differences in functional connectivity that are related to smoking or drinking. So, the first step of this study was identifying the functional connectivities associated with smoking (and the same for drinking) and making sure that the association result was not affected by use of the other two substances (drinking and marijuana use). However, the 2×2 ANOVA cannot control for the effect of the use of another substance very well since the control variables are binary, . In addition, the 2×2 ANOVA was used to test whether there are any differences between any two sub-groups . But what we interested in is the drinking-related effect and the smoking-related effect, rather than all possible comparisons shown in a 2×2 ANOVA model. If a 2×2 ANOVA had been used as primary test in this study, then post hoc test would need to be performed to identify the effects in which we are interested, and that may increase the burden of correction for multiple comparisons.Second, only non-smokers and regular smokers were considered in the smoking analysis . So, if the ANOVA had been used in the association analysis, only 620 subjects would have been left for the final analysis. This would have significantly reduced the sample size available for the drinking analysis.Finally, the sample size of each group for an ANOVA is not balanced. The sample size of the no-smoking and low drinking group is 198; the sample size of the no-smoking and high drinking group is 219; and the sample size of the smoking and high drinking group is 143. But the sample size of the smoking and low drinking group is 60, which is significantly smaller than other groups.To make this clear, the following sentence was added in the main paper:"Considering the purpose of this study described above and the balance of sample size in each sub-group, we used a two sample t-test rather than the 2×2 ANOVA. "11) When discussing 'complimentarity' in the subsection “Comparisons between drinking and smoking” (is that even a word?), the meaning of it and the findings remain rather elusive. Are the authors referring to the possibility that those who are vulnerable to FC alterations associated with drinking may be equally vulnerable to FC alterations  associated with smoking? Essentially reflecting some vulnerability/resilience to the effects of substance use in general on FC?The word being used was 'complementarity' (a noun), meaning that two processes are 'complementary' (an adjective). We have added the following to the Results section to make the point more explicit:"This finding indicates a complementarity between the functional connectivities in drinkers and smokers. […] This provides evidence that a difference in one direction (increase) may relate to smoking, and in an opposite direction (decrease) to drinking."12) Although quite catchy in its simplicity, the title of the manuscript should include a hint to the nature of the study participants – these are not individuals diagnosed with a substance use disorder. Some of the findings may be specific to this control population and may be different in pathological cases with full blown substance use disorder diagnoses. This is properly dealt with as a limitation in the Discussion.We have addressed this point by making it very clear in the first sentence of the Abstract that this is an investigation in a general population, as follows:"In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus."We also discuss it in the Discussion as follows:"Possible limitations of this investigation are that linear analyses were used. […] This is good evidence that the effects described in this paper are meaningful, in that the functional connectivity at age 14 can be related to what will happen in terms of smoking and drinking five years later."13) The Abstract should make reference to the impulsivity measures as well.We have addressed this by adding the following to the Abstract, as suggested. It was not present previously in order to keep the Abstract short."Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex."14) In the subsection “Statistical Analysis”, give the n for all subgroups or give them in the corresponding Figures 4 and 5.The number of subjects for all subgroups has been added to the main text as suggested.15) Figure 5A-D could be interpreted to reflect a threshold effect on impulsivity in smokers (only regular smokers show effects). Similarly, among those who drink, any drinking (even 1 drink per day) is associated with greater impulsivity . This brings up the question what happens when you compare no drinking to any drinking? Are the results as strong as low vs. high or will you be power-limited?The rationale for the categorization of participants is described in the Materials and methods. For the smoking analysis, only non-smokers and regular smokers were considered in our analysis. But for drinking, it is difficult just to divide all the partcipants into a no-drinking and a drinking group. This was because only 55 participants did not drink any alcohol in past 12 months. If we categorized participants in this way, the sample size is very unbalanced (55:727). Because of the power limitation, we did not investigate that question.Essential revisions:1) Brain effects in a community sample of light smokers and/or drinkers may not extrapolate to clinical samples studied previously. This point needs to be more clearly acknowledged in the Discussion. Currently the Discussion mentions in the limitations section that levels of smoking and drinking were within the normal range and so effect sizes may be lower than clinical studies. However, the possibility that different patterns of brain variation may be found within the normal range versus in clinical populations is not mentioned.We have carefully addressed what has been reported in clinical populations, and have addressed the point that the results described here may not generalize to all populations, by the following new text added to the Discussion:"Although the current study was based on the general population, the findings are in general consistent with previous studies based on clinical populations. […] However, the results described here were obtained on populations from the general community, and do not necessarily apply to heavy smokers or drinkers."2) Regarding original point 15 – we agree with the authors that comparing the small sample of non-drinkers to the much larger sample of those who consumed any alcohol is not a good idea. Given this imbalance, the authors are simply not able to address the issue of a potential threshold effect. This should be acknowledged in the Discussion.We have added the following t the Discussion, to follow this suggestion:"Another possible limitation of the drinking analysis is that the control group was a low drinking group rather than a non-drinker group. The reason for this choice was that only 55 participants did not drink any alcohol in the past 12 months, which would have resulted in too small a sample size."3) The data for individuals who both smoke and drink is given almost no interpretation in the Discussion. The edits to include these results  in the main manuscript are great. However, the authors should provide some interpretation (however speculative) about why these dual use individuals have a functional connectivity profile that appears to look like that of smokers only. This appears to argue against their idea of "complimentarity." That is, if smoking reduces FC and drinking increases FC in the same circuits, why does the combination of smoking and drinking lead to decreases? They don't address this issue in their edited manuscript.The complementarity between those who either smoke or drink is left, as it describes the findings. To address that finding about what occurs in those who both smoke and drink we have added the following to the Discussion. Thank you for asking this question."As shown in Figure 4, most of the links are decreased in the group that both smokes and drinks. One explanation is that the effect size for the comparison between non-smokers and regular smokers is higher than the effect size for the comparison between low-drinkers and high drinkers."


However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at  T cells form an important part of the adaptive immune system as they can recognize potentially pathogen-derived or aberrant peptides (epitopes), which are presented by major histocompatibility complex (MHC) molecules on the cell surface of nucleated host cells or professional antigen-presenting cells, and induce an immune response. The T cell receptor (TCR) molecule is responsible for the recognition of the MHC presented epitope. Each TCR protein is encoded by a genomic region that undergoes non-homologous recombination during T cell maturation, a process termed VDJ recombination. The randomness of the recombination process ensures the production of many different TCR proteins, with each T cell clone expressing a particular TCR, and allows the recognition of different epitopes. Epitope binding by the TCR is a critical step for the activation of targeted immune responses.As high-throughput sequencing of TCR repertoires becomes more common, the need for more advanced analysis methods is increasing. A major challenge involves the functional identification of activated T cells from TCR repertoire data. To date, considerable efforts have been made to address this problem either by the detection of proliferated T cell clones, through the comparison of the abundance of TCR clonotypes between different time points , 2, or bHere, we present a new strategy to study T cell responses based on the direct identification of epitope-specific TCRs from TCR repertoire data. To determine the epitope specificity of TCRs, machine learning models have been developed to analyze TCR sequences and predict the probability that they recognize and bind specific epitopes. These methods are based on the principle that similar TCR sequences often target the same epitope  and thatIn this paper, we specifically address two issues that currently limit the use of prediction methods on full TCR repertoires. The first is the uncertainty on where to place the cut-off for a correct prediction. In other words, how confident must the prediction be before we accept it as the truth. If the cut-off is chosen too strictly, we may miss many epitope-specific T cells. Conversely, if it is chosen too low, we increase the number of TCRs that are mistakenly identified as epitope-binding. To address this first issue, the strategy proposed here includes the estimation of a model-specific baseline prediction rate, which can be used to set the prediction cut-off in an informed manner. The second issue is that the presence of epitope-specific TCRs is not restricted to TCR repertoires that have been stimulated by an epitope, since they can also occur in small numbers in naïve, healthy TCR repertoires. Therefore, the identification of single epitope-specific TCR sequences by itself does not provide sufficient information on the capacity of the full TCR repertoire to target specific epitopes. Current prediction strategies do not evaluate whether a TCR repertoire as a whole contains more TCRs against a specific epitope than expected in a healthy TCR repertoire that is not enriched for these TCRs. To address this second issue, we introduce an enrichment analysis strategy to identify those specific epitopes that are being targeted within a given TCR repertoire.tcrex.biodatamining.be.We built upon the epitope-specific TCR classifier described in De Neuter et al.  and deveA positive training dataset was constructed containing human epitope-specific TCR beta chain sequences collected from the manually curated catalog of pathology-associated T cell receptor sequences [McPAS-TCR ] 11 3399] 17 79, 16–18. The control training dataset was designed to contain non-binding TCRs for each epitope. As one epitope can be presented by more than one MHC-molecule, and one TCR sequence might interact with different MHC molecules , we did +CD8+CD14− CD19−CD38+HLA-DR+ Ag-experienced, activated effector T cells”  vaccination studies , 2 and oT cells” , we gathFor each epitope present in the collected training dataset, we trained a random forest model to identify epitope-binding TCRs in a TCR repertoire dataset. These machine learning models have the ability to identify TCRs from multiple epitope-specific clusters . Traininc can be calculated so that:The class probability score for a TCR beta chain sequence is specific for each epitope model and does not take false positive predictions into account. To control the number of false positives, we provide each TCR sequence with a baseline prediction rate (BPR) value along with its class probability score. This BPR value represents the fraction of epitope-specific TCRs that can be detected in a background dataset  at a certain confidence cut-off. Since background datasets are expected to contain only a limited number of epitope-specific TCRs, this BPR can also be used as an estimate for the false positive rate (FPR). For every TCR sequence, its class probability score is compared to the class probability scores of the 100 000 unique background TCR beta chain sequences. A BPR value is then calculated for each TCR sequence as the fraction of randomly selected background sequences with a class probability score greater than or equal to the score of the TCR sequence. Thus, for a desired BPR threshold, a confidence cut-off p is the prediction function and B is the background dataset. This allows us to filter the prediction results with a custom threshold across different epitope models, by using a calculated cut-off c that is specific for each model. Furthermore, this allows a direct interpretation of the cut-off with regards to the expected number of false positive hits for a typical TCR sequence dataset.Where X ~ B and thus accept the alternative hypothesis that the abundance of TCRs specific for the epitope in the studied TCR repertoire exceeds the chosen enrichment threshold. Here, X is the number of unique identified TCRs specific for the studied epitope at a chosen enrichment threshold, n the size of the TCR repertoire and p the chosen enrichment threshold. The latter represents the percentage of epitope-specific TCRs that can be identified in a hypothetical background dataset at a specific BPR threshold. Since one false positive hit  is possible at every BPR threshold, we restrict the statistical analysis to those epitopes for which at least two epitope-specific TCR sequences are found.In addition to the detection of epitope-specific TCR beta chain sequences, we implemented a method to identify those epitopes for which we find a significantly higher number of unique TCR sequences in a target repertoire than expected in a representative control TCR repertoire. This analysis is performed by comparing the abundance of the epitope-specific TCR sequences in the TCR repertoire to a hypothetical dataset of randomly selected TCRs, i.e., the background dataset. TCRex does not use any information on the frequency of the TCR clonotypes in this comparison: only the number of unique predicted epitope-specific TCRs are considered. This enrichment analysis strategy uses a BPR-based enrichment threshold that represents the percentage of unique epitope-specific TCRs identified in the background repertoire at the chosen BPR threshold. For each epitope of interest, a one-sided exact binomial test is used to compare the number of TCR sequences with a BPR score smaller or equal to the specified enrichment threshold. More specifically, we test whether we can reject the null hypothesis that the observed number of identified TCRs follows the binomial distribution tcrex.biodatamining.be. This web tool was built using the Django web framework  value and the average precision. These are shown in To evaluate how our epitope-specific models handle data from new studies, we designed a validation approach wherein all data of one study was left out during the training of the models and served as a holdout validation set. We selected viral epitopes with a large set of epitope-specific training data  from various studies. For each of these epitopes, one study was removed from the training dataset . We ensup = 0.03711 for  were carried out to assess whether the identified expanded T cell clones for one of the volunteers (S1) were vaccine-associated   . The samIn the previous section, we showed how an enrichment in epitope-specificity in TCR repertoires can be detected by comparing the abundances of epitope-specific TCRs to a general background dataset with a predefined enrichment threshold. Here, we demonstrate how the selection of a representative background dataset can improve these enrichment analyses. For each of the volunteers from the two YFV studies , 2, we c+ and CMV− volunteers and set up a statistical test to identify public TCRs that were enriched in the healthy CMV+ volunteers. In total, 164 TCRs were found to be associated with a positive cytomegalovirus serostatus of which three were matched with a CMV epitope through an additional validation experiment. Here, we use our enrichment analysis strategy to identify the CMV epitopes that are targeted by the TCRs in this dataset. In short, we searched for epitopes for which an elevated level of specific TCRs are present in this dataset, i.e., more than expected in a background TCR repertoire. To see whether we only find an enrichment for CMV-specific TCR sequences, and not for non-CMV epitopes, we performed binding predictions for all epitopes present in TCRex with a strict BPR threshold of 0.01%. In total, 8 out of 164 different TCRs were predicted to bind to one of the CMV epitopes  and TPRVTGGGAM , compared to a TCR background dataset where we would expect to identify 0.01% specific TCRs for each epitope with our selected BPR threshold. Additionally, we also found one YFV-specific TCR in the list of 164 CMV associated TCR sequences. This might be a false positive identification, where the LLWNGPMAV-specific model wrongly classified the TCR as epitope-binding. However, this TCR sequence shared an identical combination of a CDR3 beta sequence and J gene with TCR sequences linked with the YFV epitope in public databases and thus the model training set. It is therefore possible that this TCR is cross-reactive and recognizes both the immunodominant YFV epitope and a CMV epitope. It could also be that the identified TCR is not CMV-reactive as the dataset reported by the original study represents TCR sequences that were statistically associated with the CMV seropositive volunteers which does not directly entail CMV-reactivity. In summary, this analysis indicates the relevance of using prediction models to identify epitope-specific T cells and search for enriched T cell epitopes in a TCR beta dataset.Previously, Emerson et al. introduced a new statistical method to discover disease-associated TCRs from TCR repertoire data . Brieflyepitopes . Two epiConsidering each epitope-specific model is based on a positive and negative training dataset, with, respectively, a very constrained and broad MHC background, the performance of the prediction models could be hampered by an inherent MHC bias. More specifically, the models are at risk of being MHC-specific, and thus predict the MHC binding property of a TCR sequence in addition to its epitope specificity. This could obstruct the identification of epitope-specific TCRs that bind MHC proteins other than those included in the training dataset. To assess the MHC-specificity of our prediction models, we studied the influence of the MHC bias on the prediction results. To this end, we collected epitope-specific TCR sequences from epitopes that are presented by more than one MHCI molecule. Out of the 49 epitopes available in TCRex, 8 were shown to be presented by different MHCI proteins . We seleTo support this result, we performed an additional analysis where the HLA-B*42:01 restricted models for the TPGPGVRYPL and TPQDLNTML epitopes were used to predict the specificity of TCRs with the same HLA-B*42:01 MHC background but different epitope specificity. In total, TCRex contained 4 epitopes associated with HLA-B*42:01: LPPIVAKEI (62 TCRs), IIKDYGKQM (54 TCRs), HPKVSSEVHI (75 TCRs), and FPRPWLHGL (120 TCRs). Using the HLA-B*42:01 restricted models (0.01% BPR), none of the TCRs associated with these 4 epitopes were predicted to bind TPGPGVRYPL or TPQDLNTML, which shows that the MHC binding property of a TCR was not learned by these prediction models. Taken together, the results of this study suggest that the MHC-bias in the prediction models does not necessarily have a major impact on the prediction results.Traditional methods to identify epitope-specific T cells require targeted experiments, which are known to fail to identify a considerable amount of reactive T cells , 7. ApprThis novel approach was validated on several independent datasets to address different research questions. Firstly, we examined whether we could detect a difference in the presence of epitope-specific TCRs in pre- and post-vaccination repertoires. Considering that vaccines are expected to activate T cells upon binding with vaccine-associated epitopes, leading to a targeted proliferation of these T cells, we expected to find more unique epitope-specific T cells after vaccination. The analysis of the two YFV studies , 2 seemsOne limitation of our method is the restriction on the number of epitopes that can be studied as the models rely on the presence of sufficient epitope-specific TCR data from targeted experiments. Consequently, it is only possible to inspect the specificity of TCR repertoires for epitopes which have already been studied. However, considering our analysis of the two YFV studies was restricted to one epitope, we achieved a relatively high identification rate in comparison with the method of Pogorelyy et al. . This miFinally, we demonstrated how our approach enables the search for enriched epitope specificity in TCR repertoire datasets using our built-in enrichment analysis test. In short, we used an enrichment test to check whether a TCR repertoire contains significantly more TCRs recognizing a specific epitope than a random background TCR dataset. As a proof of concept, we compared the elevated level of LLWNGPMAV-specific TCRs in pre- and post-vaccination TCR repertoires and analyzed a dataset enriched in CMV-associated TCRs. The former analysis shows the importance of the selection of the right background dataset when performing enrichment analyses, which we discuss in more detail in the next section. For the latter, our enrichment analysis method easily detected a significant increase in epitope-specific TCRs for two CMV epitopes. No enrichment was found for other viral epitopes, which illustrates the robustness of our approach.While this novel approach allows a full repertoire exploration, several potential sources of bias may still impact the results. A first problem lies in the diversity of epitope-specific repertoires: various groups of T cells can be found in these repertoires, each carrying a specific pattern , 13, 14.Another major concern was the effect of the MHC bias in the training data on the prediction models. This bias could lead to an underestimation of the epitope specificity of TCR repertoires from individuals whose HLA types do not match the MHC background of the prediction models. In addition, it could cause an increase of false positive predictions for individuals with matching HLAs. Our analysis on the impact of the MHC bias demonstrated that the MHC background of epitope-specific TCRs is not likely to hamper the prediction results. This indicates that the epitope-specific TCR patterns might transcend the MHC background of epitopes, which is in line with . HoweverStill, even if the epitope-specific models are not MHC-agnostic, potential biases in the enrichment results can be avoided by comparing the abundance of epitope-specific TCRs in a TCR repertoire to a background dataset with a matching MHC background. In general, we recommend the use of a representative background dataset  when available as any other biases between the TCR repertoire and the background dataset might affect the enrichment results. This is demonstrated in the enrichment analyses of the YFV studies, where the replacement of the standard background dataset by the pre-vaccination repertoires gave better results. Nevertheless, if no suitable background dataset is available, the enrichment analysis, as implemented in the TCRex web tool, still provides an easy and fast exploration of a TCR dataset, since it gives an initial overview of the possible TCR targets that may be enriched in this dataset. As bulk sequenced TCR repertoires are commonly not annotated with their epitope specificities and require laborious additional experiments to discover these targets, this enrichment analysis can inform subsequent experimental efforts.tcrex.biodatamining.be. This tool will facilitate the study of the epitope specificity of TCR sequences that underly immune responses in different disease-associated studies.Taken together, we have developed a novel strategy to study the epitope-specificity of full TCR repertoires. Our approach relies on the identification of epitope-specific T cell receptor sequences using epitope-specific prediction models. This enables the prediction of human epitope-specific TCR sequences and the identification of enriched TCR repertoire targets. We have embedded our method into a user-friendly web tool, called TCRex, which is available at https://github.com/mptouzel/pogorelyy_et_al_2018, https://www.pnas.org/content/115/50/12704/tab-figures-data), DeWitt et al. (https://clients.adaptivebiotech.com/pub/dewitt-2015-jvi), and Emerson et al. (https://www.nature.com/articles/ng.3822).The training dataset of the TCRex web tool will be made available by the authors upon request. The TCR repertoire datasets analyzed in this study were obtained from three different published studies: Pogorelyy et al.  (https:/BO, KL, and PMe conceived the study. SG, PMo, WB, and ND implemented the TCRex web tool. SG performed the analyses. All authors interpreted the results and contributed to the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A need exists to increase our understanding of the association between maladaptive personality traits, psychopathological symptoms, game preference, and different types of video game use. In the present study, we used a person-centered approach to identify different subtypes of video game players and we explored how they differ in personality profiles, clinical symptoms, and video game usage.We assessed problematic gaming via the nine-item Internet Gaming Disorder Scale and self-reported screen time playing video games in a sample of 366 adolescents and young adult gamers. Participants also completed measures on maladaptive personality domains , alexithymia , and psychopathological symptoms (DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure) and reported which genre of video games they preferred.Using a person-centered, cluster-analytic approach, we identified four clusters of video game players  presenting peculiar combinations of problematic gaming scores and time spent online playing video games. Non-problematic gamers  represented the majority of the sample (62.3% of the participants). Highly involved gamers who exhibited excessive screen time playing video games (Disordered gamers) presented the highest level of maladaptive personality traits and psychopathological symptoms, and were characterized by the greatest use of Multiplayer Online Battle Arena (MOBA) games.These results have clinical implications on suggesting the importance to determining whether or not problematic gaming activities reflect a dysfunctional emotion-focused coping strategy to avoid inner unpleasant emotional or a more generally compromised emotional and social functioning. In the last two decades, an extensive amount of research has been carried out on Internet addiction disorder (IAD), a presumptive new clinical condition that has proved to be an umbrella term including different Internet-related psychopathologies . Among tHowever, there is still a lack of consensus on criteria, definition, and conceptualization of problematic gaming patterns as a specific psychiatric disorder . In factAccording to Personality traits are specific patterns of behavior, emotion, and thought that are relatively stable over time and situations. Some personality traits have been positively associated with problematic video gaming: neuroticism , aggressDifficulty with emotion regulation could be a general risk factor for developing problematic video game use . AlexithIn regard to related psychopathological symptoms, problematic gaming has been associated with a wide number of clinical conditions  such as As recommended in recent literature on problematic gaming and behavioral addictions more generally, it is important not to pathologize common behaviors . As prooDifferent types of video games have different addictive potential . ProblemIn MOBA games, two teams composed of different players compete each other. Differently to MMORPGs, MOBA games do not develop in never-ending worlds; yet they provide extensive feedbacks to the players, stimulating competition and social interaction. MOBA games have also been associated with problematic gaming in research .The addictive potential of other game genres and typologies is still under debate. While there is some evidence that an excessive use of first-person shooter (FPS) is related to increased clinical symptoms , it remaIn this study, we aimed to explore the relationships among maladaptive personality domains, alexithymia, psychopathological symptoms, game preference, and problematic gaming in adolescents and young adults. In fact, individuals within these age groups use video games the most  and moreA person-centered approach treats the person as the unit of analysis, by identifying meaningful subgroups  of participants characterized by distinct pattern of relationship on the variables of interest that differentiate them from other subgroups of participants. This allows researchers to relate these clusters to meaningful other variables e.g., . AccordiIn line with previous research, we expected to identify a subset of problematic gamers with increased time spent online playing video games and high scores on IGD. Moreover, we expected that the video game players identified as problematic gamers would present higher levels of maladaptive personality traits , psychopM = 21.64 years, SD = 3.77) recruited via an online survey. An advertisement was shared on different video game platforms, such as video game forums, Facebook video game groups and web-pages dedicated to video games. Out of 400 participants who accessed the online survey, 366 (91.5%) accepted to participate and signed the electronic informed consent. All questions in the online survey were mandatory, so there were no missing cases. The socio-demographic characteristics of the sample are described in The study involved 366 Italian adolescents and young adults  aged from 15 to 30 years (t(364) = −1.98, p = 0.98) or gender and years of education (t(364) = −0.09, p = 0.77). The mean number of hours spent daily on video games was 3.58 on average , which is comparable to the findings of other studies , self-identifying as a “gamer”, and reporting no use of psychotropic medications. All of the participants gave their informed consent and completed an anonymous questionnaire containing socio-demographic information , the number of daily hours they used video games, the type of video games used, and self-reported scales on IGD, maladaptive personality domains, alexithymia, and psychopathological symptoms. Anonymity of the participants was guaranteed . Participants did not take any compensation for their involvement in the study. The study was carried out according to the Ethical Code of the Italian Association of Psychology (AIP) and the American Psychological Association (APA).The questionnaire included questions concerning gender, age, marital status, educational level, and employment to obtain a profile of the respondents’ demographic features.Participants were asked to indicate (yes/no) which genre of video games they play among Massively Multiplayer Online Role-Playing Games (MMORPG), Multiplayer Online Battle Arena (MOBA), browser games, first-person shooter (FPS) games, real time strategy (RTS) games, or simulation games.The Italian nine-item Internet Gaming Disorder Scale IGD-9;  was usedThe Italian version of the Personality Inventory for DSM-5 Brief Form  describing video game players’ socio-demographic carachteristics, personality functioning and psychopathological symptoms, (2) identifying clusters of players based on hours spent on video games and IGD-9 scores, and (3) examining how personality functioning and psychopathological symptoms were related to cluster membership.p-value of 0.05 was set as the critical level for statistical significance. As in other studies that used this statistical procedure , we performed a two-step cluster analysis. This is an exploratory procedure that allows researchers to identify data-driven subgroups (clusters) within sufficiently large data set  or cell residuals (for Chi–square tests).Finally, in order to analyze associations between sociodemographic variables, maladaptive personality domains, alexithymia, psychopathological symptoms, and game preferences in the identified clusters, a series of one-way ANOVAs and Chi–square tests were performed using the clusters as factors. The cluster differences were assessed using N = 366) are shown in Descriptive statistics of the sample , maladaptive personality traits , and psychopathological symptoms . Unexpectedly, a pattern of significant and negative associations was observed in the relationship between alexithymia and IGD-9 scores , maladaptive personality traits , and psychopathological symptoms . A pattern of significant and positive associations emerged among IGD-9 scores, time spent online playing video games  but a non-problematic gaming use score , so we used labeled them Passionate gamers. This was the significantly largest group identified by cluster analysis , χ2(3) = 40.34, p < 0.01.The first cluster was characterized by a relatively high amount of time spent on video games  because its members used video games for a low amount of time  and had non-problematic gaming use .The second cluster was labeled Occasional gamers , because they played an average number of hours on video games  but reported high levels of preoccupations and symptoms concerning their game use .We labeled the members of the third cluster as Preoccupied gamers  because they were characterized by a very high amount of time on video games  and reported severe symptoms of problematic game use . The four clusters did not differ with respect to gender, χ2(3) = 2.08, p = 0.56; age, F = 0.49, p = 0.69; and years of education, F = 1.64, p = 0.18.We labeled the members of the fourth cluster as Disordered gamers  are reported in Descriptive statistics for each cluster and the ANOVA results  = 14.44, p < 0.01, and a moderate, at-the-limit-of-significance use of MMORPG games, χ2(3) = 7.61, p = 0.05, with respect to other players.Subsequently, we explored the video games use (preferred genres of games) by the gamers. Frequencies of variables of interest are shown in The first aim of the current study was to explore the associations among problematic gaming, time spent online playing video games, psychopathological symptoms, maladaptive personality traits, and alexithymia in a group of adolescent and young adult video game players. In line with the literature, a pattern of positive associations was found among problematic gaming, time spent online playing video games, psychopathological symptoms, and maladaptive personality traits. Unexpectedly, alexithymia scores were negatively associated with IGD-9 scores, psychopathological symptoms, and maladaptive personality traits.Subsequently, we aimed to identify different subtypes of video game players presenting peculiar combinations of IGD-9 scores and times of video game use, and to explore the link between different profiles of video gamers and psychological maladjustment.Two clusters  likely included non-problematic gamers and represented the majority of the sample (62.3% of the participants). These gamers showed IGD-9 scores below the Italian cutoff point of 21 used to determine the presence of problematic gaming use , but onlper se  regardless of the number of hours spent daily on video games, with respect to the other identified clusters. This result is in line with literature stating that high involvement in gaming is not problematic per se , but it per se . The firper se , it is lper se . For theper se  rather tper se . On the The second subtype of non-problematic gamers is composed of Occasional video game players, who present even less psychological maladjustment, in comparison to Passionate gamers. This subgroup of gamers probably includes those individuals who consider video games as a hobby among others. However, it should be noted that even in this case, high levels of alexithymia were reported. This result must be taken with caution because in the present research we have not taken into consideration non-players of the same cohort. Therefore, other studies are needed to investigate alexithymia in low- and high-engaged gamers.The other two groups identified by cluster analysis appear as more problematic. Both clusters of gamers showed IGD-9 scores over the Italian cutoff point of 21 used to determine the presence of problematic gaming , but onlFinally, the last group was composed of Disordered gamers. As expected, these video game players presented the highest level of psychological maladjustment. In particular, they showed the most compromised personality functioning characterized by high levels of psychoticism, psychotic symptoms and suicidal ideation: an overall severe clinical picture that seems to suggest that the virtual world of video games for some of these players could be taken as an alternative to a real social world, as a sort of a “psychic pit” paradoxically protecting these individuals from distressing and perhaps disordered mental states . In otheRegarding to the genre of video game used, Disordered gamers are characterized by the highest use of MOBAs and by a moderate, at-the-limit-of-significance use of MMORPGs with respect to other players. This may be explained with the fact that by providing very frequent feedback and updates on international rankings and statistics, MOBA games currently involves much more players than MMORPGs . On the As with every research, the present study comes with a number of limitations. First, the high percentage of male participants and the limited sample size must be highlighted to avoid generalizations. Furthermore, we used a convenient sample, by recruiting video game players in gaming web-pages and forums so, there is a need to replicate these findings in larger groups of gamers. Second, it is acknowledged that collecting clinical information by means of self-report measures can present relevant bias problems , althougHowever, despite these limitations, our cross-sectional findings support the hypothesis that a multidimensional perspective on different type of gaming behaviors may be particularly informative for clinicians dealing with individuals who display problematic video game use. Our findings showed two subgroups of video game players who showed problematic gaming. Highly involved gamers who exhibited excessive screen time playing video games presented the highest level of maladaptive personality traits and psychopathological symptoms. Highly involved gamers who exhibited an average screen time playing video games presented a high level of negative affectivity . Therefore, it appears to be important to determine whether or not problematic gaming activities reflect a dysfunctional emotion-focused coping strategy to avoid inner unpleasant emotional states  or a more generally compromised emotional and social functioning . This distinction could help clinicians understand the factors underlying problematic gaming patterns, and this may foster tailored psychological interventions for people with problematic gaming. In fact, our findings suggest that the assessment of the psychological and psychopathological factors underlying problematic gaming could be more informative than an assessment procedure that solely focuses on the likelihood of a diagnosis . For example, a preoccupied video game player could benefit from interventions designed to improve emotion regulation and the capacity to reflect on his or her own mental states . In contrast, a disordered gamer could benefit from interventions that focus on disordered personality functioning  on one side, and on problematic gaming symptoms on the other side  on the other side. Thus, the investigation of the psychological problems and needs of individuals who display excessive game use via a person-centered approach represents a critical opportunity for increasing the scientific understanding of problematic gaming behaviors and, ultimately, for developing tailored and effective treatment.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by the Internal Review Board for Psychological Research of the UKE–Kore University of Enna. Written informed consent to participate in this study was provided by participant or their parent or guardian.AM provided substantial contributions to the conception of the work, deep analysis of the literature, study design, development, and final approval of the manuscript. TM and PC contributed to the development and revision of the work with deep literature analysis and agreement for final approval of the manuscript. GS contributed to the development of the work, with literature review, data acquisition, and agreement for final approval of the manuscript. MC contributed to the revision of the work and agreement for final approval of the manuscript. AS contributed to the conception and deep revision of the work, with literature analysis, contribution to data analysis, and agreement for final approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.