The aim of this study was to investigate the relationships between Theory of Mind (ToM), Working Memory (WM), and Verbal Comprehension (VC). Performance of these variables was evaluated in 44 elementary students (6–12 years) diagnosed with ADHD. Their performance in all variables was collected through the Neuropsychological Battery (NEPSY-II) and the Wechsler Intelligence Scale for Children IV. The results showed that fifty percent of the participants were below the 25th percentile in ToM and that this low performance was not related to age. In addition, analyses showed statistically significant relationships between WM, VC, and ToM. Analysis of the effect of WM and VC on ToM showed that only WM explained the variance in participant performance in ToM. These results led us to raise the need to include ToM among the skills to be stimulated in programs for the treatment of ADHD, accompanying other skills related to social adaptation that are usually included in such programs. Likewise, considering that ToM implies putting into practice skills such as considering different points of view, attending to relevant aspects of the context, making decisions, inferring mental states, and predicting behaviors, we believe that through the stimulation of ToM, WM would also be stimulated. Attention Deficit Hyperactivity Disorder (ADHD) is characterized by a persistent pattern of hyperactivity/impulsivity and/or inattention  for at least six months in two or more settings and appearing before the age of twelve, altering the normal functioning of the person in question .It is a severe syndrome caused by a delay in neuropsychological development, which leads to dysfunctions in the mechanisms of Executive Control and Behavior Inhibition. Therefore, the disorder directly affects the Psychological Processes of the Executive System—working memory, attention, self-regulation of motivation and emotions, internalization of language, and processes of analysis and synthesis—and, consequently, executive functions such as planning, organization, decision making, etc. These functions are directly involved in teaching–learning tasks and are necessary for a correct family, academic, and social adaptation ,3,4,5,6.These cognitive skills play an essential role in the development and learning of children from 6 to 12 years of age in primary education, since they are directly involved in positive academic and social performance. In this demanding period, students are expected to be able to learn and comply with rules, show resistance to distracting stimuli, be able to plan and organize themselves in advance, solve school and social problems without the supervision of parents and teachers, etc. These self-regulatory activities require good executive control .Compared to control groups, different studies reveal that children with ADHD have difficulties in controlling their behavior, which leads to them failing in school and showing difficulties in interpreting social cues, in their interpersonal skills, and therefore, in their relationships with others, which are often not as satisfactory as they should be. All this means that, on the one hand, they are uninhibited, distracted, absent-minded, forgetful, disorganized, and not very persistent, and, on the other hand, oppositionist, and defiant, with a poorer capacity of adaptation  and scarUltimately, people diagnosed with the disorder have a low degree of self-control and consequently, a limited number of basic skills and inadequate motivation for school and social functioning. Classmates of children with ADHD often complain about the difficulties in maintaining interpersonal relationships with them , and chiIn recent decades, the Theory of Mind (ToM) construct has received enormous attention. This is due to the fact that it is a skill that combines both the cognitive and social competence of individuals, constituting a key facet of social cognition. Originally, ToM was defined as the ability to attribute mental states to oneself and others, as well as to reason about those mental states which are not observable  to make predictions about one’s own behavior and that of others . DefinedAlthough the assessment of ToM has changed over time, the best known evaluations are classics such as the “change of location” task  and the Beyond the ability to attribute false beliefs, there is considerable evidence that progress in the development of ToM continues to be made during the years of primary education ,19,20. AThe relationship between ToM and language ability has been widely documented, although it mainly refers to early ToM developments. Language is a fundamental tool for the development of the communicative interactions that stimulate the development of ToM; conversations with adults and peers expose children to the different perspectives that people possess . TherefoRegarding the relationship between ToM and cognitive development, Carlson et al.  exposed In the early childhood education stage, there is evidence of links between different measures in EF and the ability to attribute false beliefs . HoweverThe study by Im-Bolter et al.  integratIn recent years, interest has begun to emerge in the scientific community to develop cognitive profiles that help both to better understand the disorder and to evaluate and treat it ,38,39,40As far as WM is concerned, people with ADHD present less efficiency in the codification of the stimuli and a marked tendency to “prolong” the activation of the current stimulus. For this reason, they are classified as “forgetful” and as “only living in the present” without paying attention neither to the future consequences of their actions nor to the projection towards goals in time. However, this is mainly due to a less adequate use of coding and remembrance strategies than a structural problem of WM. Therefore, what varies with respect to subjects who do not suffer from ADHD is how they “use” WM . With reThe proposal of a deficit in ToM as a secondary symptom associated with ADHD, as a result of a deregulation of the executive system, is due to research that refers to an ideal inhibitory control and functioning of the working memory for an adequate Theory of Mind . This hyResearch, such as that of Huang-Pollock et al. , showed In a meta-analysis of studies conducted between 1979 and 2009, Uekermann et al.  pointed The study conducted by Caillies et al.  measuredBora et al.  carried After all the above mentioned, it is to be expected that many children with ADHD will not perform at the same level of ToM tasks as typically developing children. In addition, as noted before, previous studies have shown that WM and VC are predictors of the development of ToM in typically developing children. Therefore, since children with ADHD are characterized by low levels of development in WM but not in VC, it could be expected that WM would be the factor to best explain the inter-individual differences in ToM. However, given the role of language in the development of ToM, it was also thought that the participants’ VC would help facilitate performance in ToM by modulating the effect of WM. Therefore, the general aim of this study is to investigate the relationships between ToM, WM, and VC in primary education students diagnosed with ADHD. To do so, we examined the performance in ToM of children with ADHD between 6 and 12 years old in order to study if their level of development was different from the typical development in that age range. In addition, it was studied whether WM and VC contributed to predicting the performance of these children in terms of their ADHD.To examine performance in a wide range of skills linked to the ToM in primary education students with ADHD in order to check if they are in line with typical development.To study whether difficulties in ToM persist in the age range covered by primary education.To examine if WM and VC correlate with the performance of the participants in terms of their ToM.To study the effect of each variable  on the participants’ performance in terms of ToM.The following specific objectives were set:N = 32) were diagnosed with the combined type of ADHD, and 27.3% (N = 12) were diagnosed with ADHD with predominantly attention deficit, following the criteria of the DSM-IV-TR [This study was conducted with a group of 44 primary school students between the ages of 6 and 12. Of these, 72.7% .Presence of a significant deterioration of social and/or emotional activity.Absence of diagnosis of schizophrenia, psychotic disorder, or any other neurodevelopmental disorder, intellectual disability, and/or sensory or motor deficits.IQ equal or greater than 80.Primary school studentsNot having received—nor currently receiving—specific treatment in the field of social cognition.The following inclusion criteria were used:Secondly, contact was made with the families, informing them about the study and presenting them with the “Informed Consent” document, where the justification of the study, the objectives, and the procedure to be followed were mentioned. Subjects who met the criteria and accepted the conditions of the study were included. A descriptive and correlational design was chosen, seeking to obtain a causal relationship between several variables: performance in the Theory of Mind task and performance in cognitive tasks, specifically WM and VC. The selection of the sample was non-probabilistic, due to chance not being used. Only those participants who met specific inclusion criteria were admitted to the study.All participants were evaluated using the following two instruments:The NEPSY-II Neuropsychological Battery . From thThe TVM subtest requires the participation of both comprehension and verbal expression skills, even when supported by images. The procedure in each item is similar: the individual being tested is shown a picture, the situation related to the picture is described, and then, a question is asked that requires the individual to place him/herself in another person’s point of view in order to be answered correctly. The instrument provides a scale of percentiles, which allows the scores obtained by the individual assessed to be placed in relation to those of individuals with typical development of their age.Wechsler Intelligence Scale for Children IV or WISC-IV . The ageAccording to the authors of the test, the assessment of WM provides data on the subjects’ ability to temporarily retain certain information in memory, work or operate with said information, and generate a result; it involves attention, concentration, mental control, and reasoning. Regarding the assessment of VC, it informs about the capacity to define and establish semantic relationships between concepts, as well as to answer a series of questions about social situations or general principles. With the collaboration of specialists in the field of psychology, speech therapy, education, and neurology and team members of a specific Centre for Child and Adolescent Neuroscience in Malaga, we proceeded to contact, by telephone, the families of children diagnosed with ADHD who met the inclusion criteria, informing them of the study and requesting their voluntary participation. Later, those interested were called for an interview where the research was explained in detail and the interested parties signed the informed consent document.For the evaluation of ToM, WM, and VC, two sessions of approximately one and a half hours each were used, in which the participants were evaluated individually. In the first session, the subtests that make up the WM and VC were passed, while the ToM tasks took place in the second session. The time schedule of our experiment  lasted four months.Participants were placed into three groups according to their centile scores in ToM , and the frequency and percentage of participants for each group were obtained. Likewise, the means and standard deviations of the WM and VC indices of each group were calculated. Pearson’s correlation analyses were carried out to analyze whether there was a relationship between the age of the participants and the centile score obtained in ToM, as well as to study whether there was a relationship between ToM, WM, and VC. Finally, a multiple linear regression analysis was performed with the enter method to check the effect of WM and VC on ToM.As can be seen in p = 0.600). It is worthy to note the negative sign of the value of Pearson’s correlation coefficient, since it indicates that although there is no difference in the centile score between the younger and older participants, if there were, it would be in the negative direction—that is, the older participants would have the worst centile score.In order to check whether difficulties in ToM persist throughout the age range covered by primary education, an analysis of correlation between age and performance in ToM tasks was carried out. The results indicated that the correlation between both variables was not statistically significant  as well as between VC and ToM . In addition, WM and VC significantly correlated with each other . Since a correlation between WM and VC was found, a partial correlation analysis between WM and ToM was performed, controlling for the effect of VC, in order to ensure that the relationship between WM and ToM was not influenced by the effect of VC on WM. The results show that, once the effect of VC on WM was controlled, the relationship between WM and ToM was still statistically significant .The relationships between ToM, WM, and VC were also analyzed. The results showed that the relationship between WM and ToM was statistically significant  the centile score obtained in ToM and as independent variables (IVs) the scores in WM and VC, a multiple linear regression analysis was performed with the enter method. The assumptions of linearity, independence, homoscedasticity, normality, and non-collinearity have been fulfilled. The data are shown in The statistical data show that of the two independent variables, WM is the significant variable and that the model explains 29.4% of the variance obtained in the data referring to the centile score in ToM by the participants.This study has examined performance in a wide range of skills linked to ToM in primary school students with ADHD, as well as its relationship to their level of development in VC and WM.In regard to the first objective, the results show that a high percentage of students perform below the expected typical development range, placing them in very low percentiles. These findings are in line with those found by Caillies et al.  and UekeShowing these difficulties is relevant, since this characteristic for many people with ADHD only started drawing attention a few decades ago. For many professionals in education and psychology, this is not a feature to highlight in the profile of a student with ADHD, unlike other neurodevelopmental disorders (such as ASD) in which this difficulty is part of their profile. As we pointed out in the introduction, when the focus is placed on second-order false belief attribution tasks, we found research that observes a similar performance when comparing groups with ADHD and ASD ,48,49. TIn addition to verifying the existence of these difficulties, this study sought to investigate whether difficulties in ToM persist in the age range covered by primary education (second objective). After analyzing the results, we observed that there is no difference in the centile score between the younger and older participants. Considering the scores obtained by the older and younger participants, it is noteworthy that it is the older participants who are at a lower centile. This leads us to interpret that if, as we pointed out at the start, the improvement in cognitive skills leads to advances in the development of ToM, in the case of participants with ADHD who do not improve as expected in certain cognitive skills, these advances do not occur.The third aim of this study was to evaluate if WM and the VC correlate with the participants’ performance in ToM. As expected, based on the research on typical and atypical development of ToM cited in the introduction, the relationship between ToM, VC, and WM performance is positive. There is sufficient data to support that putting ToM into function requires representational skills mediated by language, as well as cognitive skills such as the ability to compare and contrast different points of view (one of the functions of WM).Finally, the fourth objective of this study was to test the independent effect that age, WM, and VC have on participants’ performance in terms of ToM. Analysis of the effect of these variables showed that only WM explained the variance in participants’ performance in terms of ToM. Precisely, as noted above, a lower performance in WM compared to VC is one of the features that characterize the cognitive profile of ADHD ,41. The Several studies, such as those by Bora et al. , HutchinConventional programs for the treatment of ADHD usually take into account variables that cause greater problems in social, school, and family adaptation, attributing a leading role to EFs. However, there are few programs that include ToM activities for this purpose. From the results found in the present study and those found by other researchers ,54, we pIn summary, WM was the only variable that explained the variance in participants’ performance in terms of ToM. In the primary education stage, the development of ToM is more advanced, requiring the cognitive ability to simultaneously manage a variety of mental states, which implies that there must be optimal development in WM. The data of this study do not point to a deficiency in WM; however, the participants do show lower scores in WM than in VC, which leads us to think that WM is not functional enough to successfully face ToM tasks.It is necessary to incorporate a block of contextualized ToM tasks within the psychoeducational intervention packages for elementary students with ADHD in order to contribute to the improvement of WM. These tasks would stimulate the following processes: (i) addressing the relevant keys to the situation, (ii) interpreting them, and (iii) building a response adjusted to the situation.Therefore, it would be expected that after the specific intervention, there would be an improvement both in ToM and in WM, thus reducing the problems of adaptation to all the environments where these skills are put into play.It is important to point out that the results obtained must be considered with caution because one of the main limitations of the present study is the small sample. The homogeneity of the sample has been prioritized (so that only elementary school students with ADHD and without other associated deficits were included). However, it must be recognized that the limited number of participants has been able to condition the significance of the findings and their power to convincingly answering the study objectives might not be high.All of this would justify that as future lines of research, the possibilities of expanding the study sample are considered. This would allow it to be distributed in different experimental groups in which the differentiated effect of WM on the ToM of the subjects could be verified according to the type of diagnosis , and even compare the results with a normotypic control group.


Attention Deficit Hyperactivity Disorder (ADHD) is a common neuropsychiatric disorder in which children present prefrontal cortex (PFC) related functions deficit. Proactive cognitive control is a process that anticipates the requirement of cognitive control and crucially depends on the maturity of the PFC. Since this process is important to ADHD symptomatology, we here test the hypothesis that children with ADHD have proactive cognitive control impairments and that these impairments are reflected in the PFC oscillatory activity. We recorded EEG signals from 29 male children with ADHD and 25 typically developing (TD) male children while they performed a Go-Nogo task, where the likelihood of a Nogo stimulus increased while a sequence of consecutive Go stimuli elapsed. TD children showed proactive cognitive control by increasing their reaction time (RT) concerning the number of preceding Go stimuli, whereas children with ADHD did not. This adaptation was related to modulations in both P3a potential and lateral prefrontal theta oscillation for TD children. Children with ADHD as a group did not demonstrate either P3a or theta modulation. But, individual variation in theta activity was correlated with the ADHD symptomatology. The results depict a neurobiological mechanism of proactive cognitive control impairments in children with ADHD. Attention Deficit Hyperactivity Disorder (ADHD) is the most widespread neuropsychiatric disorder in childhood, with an incidence of 10% to 12%  children have found that an increase in the activity of the dorsal ACC (dACC) is related to performance monitoring in several tasks that require reactive cognitive control , which they had been using for at least 3 months when recruited. They were asked to interrupt stimulant treatment on the day of the experimental session. TD participants were selected out of a large group of children from public schools who volunteered for the study.Written consent was signed by parents and written assent was signed by the children after a detailed explanation of the scope of the study, following guidelines and procedures approved by the Ethics Committee of the Pontificia Universidad Católica de Chile. All experiments were performed at the Cognitive Neuroscience Laboratory of the University.Participants performed the visual Go-Nogo task from Zamorano et al.  while thContinuous EEG recordings were obtained with a 40-electrode NuAmps 10/20 EEG System (Neuroscan). All impedances were kept below 5 kΩ. Electrode impedance was re-tested during pauses to ensure stable values throughout the experiment. All electrodes were referenced to averaged mastoids during acquisition and the signal was digitized at 1 kHz. Electro-oculogram was obtained with four electrodes. All recordings were acquired using Scan 4.3 and stored for off-line analysis. At the end of each session, the electrode position was digitized using a 3D tracking system (Polhemus Isotrak). For all analyses, we used a 30 s-period acquired before the beginning of each block as the baseline.Accuracy and RT were analyzed with both mixed ANOVA and mixed linear regression. For ANOVA, we used condition (Go or Nogo stimuli), diagnosis (ADHD or TD), and the progression of Go trial sequences (1 or 3 or 5 or 7 go sequence) as factors. For mixed linear regression, we used condition (Go or Nogo stimuli) and diagnosis (ADHD or TD) as factors and the progression of Go trial sequences (1 or 3 or 5 or 7 go sequence) as a linear regressor. For mixed linear regression, we used single trials per subject and clustered the errors (variance) per subject including random effects for the intercept and regressors. Data were tested for normality with the Kolmogorov–Smirnov test. When the data did not meet the normal distribution assumption, nonparametric tests were used.p = 0.3; Go trial ADHD: 201.7 range (144–236), TD: 218.6 (161–239), p = 0.003].EEG signals were processed using the LAN toolbox  were computed as the mean of the signal for each electrode and each participant over correct Nogo trials (the Nogo stimuli onset marked the time zero). P3a component was defined as the positive deflection with peak latency at about 380 ms post-stimulus onset and frontal topology. For the peak analyses, we explore for each subject the maximum positivity between 300–500 ms at the FCz electrode.Induced power distribution was computed using Wavelet transform for correct trials. We applied a 5-cycle Morlet wavelet for a time window of −0.5 s to 1 s around the onset of each stimulus (range: 2–45 Hz in 1 Hz Step). Then, we used all of the artifact-free single trials for the modeling. For the time-frequency analyses, we used the dB of power related to a baseline during the fixation phase (10 s before the beginning of each block). We computed the models for each subject based on the single-trial wavelet transform , b3 is the slope for the interaction between Nogo and Sequence (defined as the number of preceding Go stimuli in the sequence) and b4 is the slope for a dummy repressor indicating if an error occurs in the previous trials. The latter was used to control for reactive cognitive control. Thus, per each regressor and subject, we obtained a three-dimensional matrix , which we used in the second-level analysis. In this analysis, we compared each bin of the preceding matrices across subjects. We carried out two types of comparisons: (i) we explored for consistent modulations within the same condition and group. For this, we used the Wilcoxon signed-rank test, evaluating whether the means were other than zero  that showed the same effect . The cluster-level statistics were computed as the sum of the statistics of all sites within the corresponding cluster. We evaluated the cluster-level significance under the permutation distribution of the cluster that had the largest cluster-level statistics. The permutation distribution was obtained by randomly permuting the original data. For paired analysis, we permute the sequences of Go stimuli (1 or 3 or 5 or 7 preceding go stimuli). For unpaired analysis (group comparison) we permute the diagnosis factor (ADHD or TD group). After each permutation, the original statistical test was computed. After 1,000 permutations, the cluster-level significance of each observed cluster was estimated as the proportion of elements of the permutation distribution greater than the cluster-level statistics of the corresponding observed cluster.To correct for multiple comparisons, we carried out the CBP test  =  is assumed to be linearly related to a set of cortical sources Y(t) =   and to an additive noise N(t): X(t) = LY(t) + N(t), where L is the physical forward model. The inverse solution was then derived as Y(t) = WX (t) = RLT (LRLT + γ2C)−1X(t), where W is the inverse operator, R and C are the sources and noise covariance, respectively, and λ is the regularization parameter. R is the identity matrix that was modified to implement depth-weighing (weighing exponent 0.8). The regularization parameter λ was set to 1/3. To estimate cortical activity at the cortical sources, the recorded raw EEG time series at the sensors x(t) was multiplied by the inverse operator W to yield the estimated source current as a function of time at the cortical surface Y(t) = WX(t). Since this is a linear transformation, it does not modify the frequencies of the underlying sources. It is therefore possible to undertake time-frequency analysis on the source space directly. In this source space, we computed frequency decomposition using the Wavelets transform as in the scalp levels (see above). Since we used a small number of electrodes and no individual anatomy for head model calculation, the spatial precision of the source estimations was limited. To provide more information about the localization procedure, for all source estimations, we show the scalp distribution of the activity computed separately from the electrode space. Finally, to minimize the possibility of erroneous results, we only present source estimations when there are both statistically significant differences at the electrode level and when the differences at the source levels survive a multiple comparison correction [False discovery rate (FDR) q < 0.05].Source estimations were carried out in BrainStorm  and accuracy  in Go trials, whereas the ADHD group demonstrated a higher rate of commission errors during Nogo trials . Since changes in RT and accuracy related to the position of the Go stimulus in a sequence could be an indicator of proactive cognitive control, we assessed for differences in this progression between groups. To this end, we used both a mixed linear regression (assuming a linear progression) and a mixed ANOVA (with no a priori assumption related to a specific linear progression). For RT during correct Go trials, we found a linear progression in the TD group, while no such progression was found in the ADHD group  = 3.1, p = 0.001) and for the interaction between sequence and group  = −2.24, p = 0.026). To rule out possible confounding factors related to the slowing of reaction after an error (reactive cognitive control), we carried out an additional single-trial regression including if the preceding trial was an error or not . Interestingly in this regression, the interaction between sequence and group remains significant (t(8253) = −2.2, p = 0.027). For the accuracy of Nogo trials, we found that both groups demonstrated a progression, but the ADHD group presented a lower rate  = −2.27, p = 0.024), while the sequence factor was significant only in the ANOVA  = 1.58, p = 0.11).Both groups demonstrated similar RTs  in frontal electrodes. In the source analysis, we found that this modulation was bilateral, although with a predominance of left hemisphere activity (FDR q < 0.05). Interestingly, we did not find any significant cluster in the ADHD group. Using a permissive threshold (uncorrected p < 0.05) for the source analysis, we found a modulation in the right sensory-motor cortex in the same time-windows where controls presented significant P3a modulations. Comparing the progression in both groups, we extracted amplitude values at the peak of the P3a component (FCz electrode) and then carried out a correlational analysis per subject testing a monotonic increase . At the group level, TD children presented rho values that were greater than zero  and greater than those of ADHD children .The source analysis showed this modulation took place over the middle and superior frontal gyrus , precentral gyrus, and midcingulate cortex. At the scalp level, the ADHD group did not demonstrate significant modulations. This fact led to significant differences between groups. These differences mainly took place in the theta modulation over lPFC . HoweverThe diagnosis of ADHD has been associated with a broad plateau of symptoms and cognitive deficits that goes from impairments in working memory to motor control (Nikolas and Nigg, Consistent with the behavioral results, TD children showed an increased progression of P3a amplitude during successive Nogo stimuli related to the number of preceding Go trials. The modulation of this component, traditionally associated with the allocation of attentional resources (Polich and Bondurant, A broader interpretation of the modulation of the P3 component in sequential stimuli presentation is taking this response as a mixture of separable theta and delta activity, which is related to time-frequency activity in Go-Nogo paradigms Polich, . Our resIt is important to note some limitations of the current study. Source localization using EEG signals has several issues that decrease spatial precision. Future studies using magnetoencephalography or concomitant fMRI/EEG to precise the sources of the oscillatory mechanisms described here in more detail are needed. Additionally, this research was carried out only in male subjects. It is important to replicate these results in a sample of both females and males.In conclusion, the current study is the first examination of the neurobiological mechanisms of proactive cognitive control processes in children with ADHD. Results indicate that children with ADHD fail to increase P3a and theta oscillation related to the context, which in TD children is related to slower RTs when the expectation of a target stimulus increases. Altered theta oscillation in children with ADHD was related to their symptomatology, suggesting that this failure is linked to relevant behavioral impairments in real-world settings. The conclusions obtained by experimental studies are usually constrained by the tasks and measures used to conduct the studies, which makes it difficult to integrate the findings in clinical practice. However, disentangling the precise neurobiological and cognitive mechanisms underlying the symptomatology can give crucial information for adequate therapeutic interventions that can improve the quality of life of these patients. Thus, the consistent behavioral and neural findings related to the dysfunction in context-dependent cognitive control of children with ADHD might help to provide a useful framework for entangled observed symptomatology to neurobiological models to enrich the diagnosis, and more importantly, to improve the treatments of this developmental disorder.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by Comité de Ética de la Escuela de Medicina de la Pontificia Universidad Católica de Chile. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.FZ, PB, and FA: experimental design. FZ, PB, and XC: carry out experiments. FZ, PB, LK, DA-C, and XS: data analysis. FZ, PB, CL, CA, AF-V, and FA: wrote article.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.


ADHD patients and the controls also significantly differed in all the scales administered (with Cohen’s d between 0.92–4.70), except for the TEMPS-A Hyperthymia scale. A regression model indicated that ADHD was independently associated with higher scores of a negative temperaments/hypomania factor (Odd Ratio = 14.60) but not with suicidal ideation. A high incidence of suicidal ideation, comorbid psychiatric disorders, and negative affective temperaments was reported in adult ADHD patients, and clinicians should routinely assess risk factors for suicide among these patients.The present study was designed to shed light on a topic rarely explored and to suggest possible ways to detect risk factors for the presence of suicidal ideation and behaviors in a sample of adult patients with Attention-Deficit Hyperactivity Disorder (ADHD). This study also explored the association between ADHD, affective temperaments, the presence of hypomania symptoms, and suicide risk. We hypothesized that  (1) patients with adult ADHD would report more negative affective temperaments and more hypomania symptoms and (2) that they would have a higher suicide risk. The participants included 63 consecutive adult inpatients  with ADHD and 69 healthy controls . All participants were administered the Wender Utah Rating Scale (WURS), the Hypomania Check-List-32 (HCL-32), the Mood Disorder Questionnaire (MDQ), the Temperament Evaluation for Memphis, Pisa, Paris, and San Diego (TEMPS-A), and the Columbia-Suicide Severity Rating Scale (C-SSRS). Forty-six percent of the ADHD patients had an Axis 1 comorbid disorder. ADHD patients (compared to controls) more often reported suicidal ideation (46.0% vs. 5.9%, one-way Fisher exact test  Attention-Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with childhood-onset, marked by high and consistent levels of inattention, impulsivity, and/or hyperactivity that must be diagnosed before the age of 12 and can lead to functional impairment in multiple domains   suggesteAlthough ADHD has long been conceptualized as a developmental disorder, it usually persists into adulthood. In fact, its global prevalence in the general adult population is around 2.8%, ranging between 0.6% and 7.3% ,5. CommoHigher healthcare utilization and costs for multimorbidity, especially for the inpatient care of psychiatric disorders, are generally reported among adults who have had childhood ADHD, remitted or not . Many stThe co-occurrence of ADHD and another psychiatric diagnosis can complicate the recognition of ADHD . FurtherPersonality disorders, mainly those of cluster B and C, are estimated to be present in more than 50% of adults with ADHD , leadingThere is evidence of a positive association between ADHD and suicidality ,26,27,28p < 0.05).The presence of psychiatric comorbidities could be another risk factor for suicide in patients with ADHD . In a poThe aim of the present study is to investigate the presence of suicidal ideation and behaviors in a sample of adult patients with ADHD and to explore the association between ADHD, affective temperaments, the presence of hypomania symptoms, and suicide risk. We hypothesized that  (1) patients with adult ADHD would report more negative affective temperaments and more hypomania symptoms and (2) that they would have a higher suicide risk.A total of 132 adults took part in the present study: 63 consecutive adult inpatients with ADHD were admitted to the study in the order of presentation , according to the diagnostic criteria of the DSM-5 [Inclusion criteria: All patients were between 18 and 65 years old, diagnosed with ADHD according to the diagnostic criteria of the DSM-5 [The medical staff informed the participants about the relevance of the study, explaining its goals and purposes. All participants signed informed consent  voluntarily. The Institutional Review Board approved the study All the patients were administered the Wender Utah Rating Scale (WURS) ,32, the The Wender Utah Rating Scale (WURS) is a 61 item self-report instrument to measure the presence, frequency, and severity of ADHD symptoms experienced in childhood. Each item can be rated from 0–4 . Sample items include “As a child I experienced inattentive daydreaming” and “As a child in school I did not achieve my potential”. The score is calculated as the sum of the item answers, and a total score of 46 or more is consistent with a positive diagnosis for ADHD. For the present sample, Cronbach’s alpha was 0.98 for both the German and Italian samples.The Hypomania Check-List-32 (HCL-32) is a self-report scale for the assessment of hypomanic symptoms. It has 32 items with yes/no answers that examine the emotions, behaviors, or thoughts linked to hypomania. The total score is calculated by adding up the affirmative answers, and a total score of 14 or more indicates that the subject is potentially bipolar. Sample items include “I feel more energetic and more active” and “I have more ideas; I am more creative”. For the present sample, the Cronbach’s alpha values were 0.91 for the Italian sample and 0.94 for the German sample.The Mood Disorders Questionnaire (MDQ) is a self-report questionnaire featuring 13 items with yes/no answers to screen for hypomania or mania, including a final item measuring the level of impairment due to the symptoms on a 4-point scale. Sample items are “Has there ever been a period of time when you were not your usual self, and you were so irritable that you shouted at people or started fights or arguments?” and “Has there ever been a period of time when you were not your usual self and spent money or got you or your family into trouble?”. For the present sample, the Cronbach’s alpha values were 0.86 for the Italian sample and 0.89 for the German sample respectively.The Temperament Evaluation Memphis for Pisa, Paris, and San Diego (TEMPS-A) is a self-report instrument to measure affective temperamental traits. It consists of 110 items with true/false answers, which result in 5-dimensional scales: Depressive, Cyclothymic, Hyperthymic, Irritable, and Anxious. Sample items are “I am the kind of person who does not like change very much” and “I am totally comfortable even with people I hardly know”. For the present sample, the Cronbach’s alpha values were 0.77  and 0.78 (the German version) for Depression; 0.91 for Cyclothymia ; 0.79  and 0.83 (German version) for Hyperthymia; 0.87  and 0.86 (German version) for Irritability; and 0.87  and 0.89 (German version) for Anxiety.The Columbia-Suicide Severity Rating Scale (C-SSRS) is a semi-structured interview used to assess suicidal ideation and suicidal behavior. The first part explores suicidal ideation . The second part of the interview examines suicidal behavior, including preparatory acts, interrupted or aborted attempts , actual attempts, and non-suicidal self-injurious behavior.2) tests were used for the 2 x 2 and N x N contingency tables, respectively. Independent sample t-tests were used to evaluate the differences between groups based on dimensional variables. A Bonferroni correction for multiple statistical tests was used to correct the p values in bivariate analyses. Phi (φ), Cramer’s v (cφ), and Cohen’s d (d) indices were reported as measures of the effect size. φ and cφ values of 0.20 and lower indicate a weak or negligible association, values between 0.20–0.40 show a moderate association, values between 0.40–0.60 indicate a relatively strong association, and values of 0.60 and higher indicate a strong association. d = 0.20 indicates a small effect size, d = 0.50 is a medium effect size, and d = 0.80 is a large effect size. Because of the high correlations between the negative affective temperaments, HCL-32 and MDQ scores, and potential problems with multicollinearity, we included these variables in a principal axis factor analysis and calculated the factor scores for use as independent variables in regression models. The odds ratios (ORs) and their 95% confidence intervals (CI) were reported as measures of associations. All tests were significant for p < 0.05.All analyses were performed with the statistical package for social sciences . One-way Fisher exact tests and chi-squared (χp < 0.001) and age but only before correction for multiple testing . The ADHD patients and controls did not differ in their ethnic composition (one-way Fisher exact test p = 0.50). The patients (compared to controls) were younger (36.78 ± 11.55 vs. 42.39 ± 10.19) and more likely to be male (71.4% vs. 31.9%).The ADHD patients and controls differed in sex . A personality disorder was present in 20.0% of the Hyperactive/impulsive group and 28.6% of the Inattentive group . Suicidal ideation was present in 30.0% of the Hyperactive/impulsive group and 57.1% of the Inattentive group . A suicide attempt was present in 0.0% of the Inattentive group and 20.0% of the Hyperactive/impulsive group . Non-suicidal self-harm was present in 14.3% of the Inattentive group and 20.0% of the Hyperactive/impulsive group .The subgroups of ADHD patients did not differ in the presence of Axis 1 disorders. An Axis 1 disorder was present in 50.0% of the Hyperactive/impulsive group and 57.1% of the Inattentive group (χp < 0.001), but only 4.8% of the ADHD patients reported active suicidal ideation with the intent to die and a specific plan. However, after correction for multiple testing, no between-group differences were statistically significant for suicidal behaviors. Only 9.5% of the ADHD patients had a history of an actual suicide attempt , 20.6% reported a history of preparatory acts or behaviors and interrupted attempts (vs. 0.0% of the controls), and 15.9% reported self-injurious behavior without suicidal intent . ADHD patients (compared to controls) more often reported suicidal ideation   . ADHD pa2 = 103.49; df = 4; p < 0.001; Nagelkerke R2 = 0.73). The ADHD patients (compared to the controls) were more likely to have higher scores in their negative temperament/hypomania factors . ADHD patients (compared to controls) were also more likely to be men . Age  and suicidal ideation  were not independently associated with the criteria.Because of the high correlations between affective negative temperaments, the HCL-32 and MDQ scores, and the potential problems with multicollinearity, we included these variables in principal axis factoring, resulting in a single factor with an eigenvalue of 4.04 and 67.4% of the variance explained. The factor loadings ranging between 0.56 for the HCL-32 and 0.89 for TEMPS-A Anxiety). The factor score was included as an independent variable, along with sex, age, and suicidal ideation, in a logistic regression model with groups as the criterion. This model explained the group differences well , MDQ, and HCL-32. These results are consistent with previous research findings: Torrente et al.  reportedLastly, to detect possible risk factors for adult ADHD, we developed a logistic regression model with the clinical and control group as the criterion and the factor score for temperament as an independent variable, along with suicidal ideation, sex, and age. The results showed that being male and having higher scores for the temperament factor  played a role in predicting the presence of ADHD in our sample. Age and suicidal ideation were not associated with ADHD. Landaas and colleagues  observedThis study had some limitations. First, we assessed the mania/hypomania symptoms, ADHD symptoms, and affective temperaments through self-report questionnaires , but we did not use direct observation of actual behaviors or any diagnostic interviews conducted by a clinician, except for suicidality. Observations and interviews may have been more reliable and allowed us to obtain more accurate and specific information about the patients’ mental health. Moreover, since all the questionnaires were self-reported, social desirability may have been present in the patients’ responses. Second, the number of participants was modest. Lastly, given the cross-sectional nature of the study, definitive conclusions cannot be made regarding the cause–effect relationships among the variables, and we did not consider fluctuations over time.Clinicians should routinely assess the presence of comorbid psychiatric disorders and suicide risk in adult ADHD patients. Adult ADHD patients frequently report suicidal ideation, sometimes with specific plans to commit suicide. The size of the effect for history of suicide attempts and interrupted attempts was more limited. Negative affective temperament, hypomania, and deficient emotional self-regulation were also present, and their roles as risk factors for suicide should be investigated in future studies. The early detection of conditions that may worsen mental health or exacerbate further disorders is fundamental and could contribute to the design of more effective treatments. We recommend a multimodal approach that considers the different facets of ADHD and is effective in managing the social, professional, and emotional areas compromised by the disease.


Deficits in visual statistical learning and predictive processing could in principle explain the key characteristics of inattention and distractibility in attention deficit hyperactivity disorder (ADHD). Specifically, from a Bayesian perspective, ADHD may be associated with flatter likelihoods (increased sensory processing noise), and/or difficulties in generating or using predictions. To our knowledge, such hypotheses have never been directly tested.n = 17) differed from a control group (n = 30) in implicitly learning and using low-level perceptual priors to guide sensory processing. We used a visual statistical learning task in which participants had to estimate the direction of a cloud of coherently moving dots. Unbeknown to the participants, two of the directions were more frequently presented than the others, creating an implicit bias (prior) towards those directions. This task had previously revealed differences in other neurodevelopmental disorders, such as autistic spectrum disorder and schizophrenia.We here test these hypotheses by evaluating whether adults diagnosed with ADHD (We found that both groups acquired the prior expectation for the most frequent directions and that these expectations substantially influenced task performance. Overall, there were no group differences in how much the priors influenced performance. However, subtle group differences were found in the influence of the prior over time.Our findings suggest that the symptoms of inattention and hyperactivity in ADHD do not stem from broad difficulties in developing and/or using low-level perceptual priors. Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate levels of inattention, hyperactivity, and/or impulsivity that substantially impact psychosocial functioning , 2. The likelihood distribution) with implicit beliefs or expectations about the state of the world (prior distribution). Bayes’ rule is used to combine each source of information in a probabilistically ‘optimal’ manner, i.e. the most reliable (precise) source having the greatest influence upon perception. The prior acts as a summary of past experiences used to predict the most likely cause of sensation from noisy and ambiguous sensory data Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Richards 2020The researchers aimed to test whether there are perturbances in Bayesian inference in individuals with ADHD which might explain the symptoms they experience. During a dot-motion detection task, participants were required to indicate the angle of the movement of the dots, as well as whether the stimulus was present in the first place. Stimuli were mostly like to be moving towards 32 degrees with respect to a reference point, and less likely at 4 other locations either 16 or 32 degrees from the most likely reference point in either direction, creating 5 motion conditions. Behavioural analysis as well as a modelling analysis was used to explore differences between groups. The researchers found a bias towards reporting motion in the most likely direction. However, there were no group differences in these biases. Computational modelling further did not reveal any differences across groups. The paper is well written and the methods are sound. There are a few concerns I would like to raise, but I don’t think they will stand in the way of getting the paper published after some revisions.Major:1. The introduction could benefit from a bit more information on how supposedly reduced priors in perceptual inference map onto the symptoms of ADHD specifically, in particular in the presence of a null-finding, and the researchers discussing a similar hypothesis for autism.2. The task uses a stair-case procedure to control for each subject when the dots are visible. I’m wondering whether this not eliminates a possible effect of sensory precision, and if not why not?3. Given effects of dopamine on the usage of prior expectations in perceptual inference , I wonder to what extend medication history affects the results. I realise it is a small sample, but it would be good to do an exploratory analysis to see how effects differ between those with a history of stimulant use between those who do not.Minor:1. Why is there not a bias found towards the 32 angle in the 0degree condition. It is equally far from the 64 condition where the strongest effect is found, so it is surprising there is no effect in what otherwise is a very similar condition.2. With regards to the task, could you clarify whether participants had to do the estimation and detection task simulataneously or sequantially? Do they require the same button response? How does this work exactly?3. The results are controlled by AQ scores which makes sense, but is there a correlation in the first place?Reviewer #2: This well-written paper depicts an important study that applies a visual statistical learning task and associated models developed by this research group  to investigate potential impairments in Bayesian inference in ADHD. As the authors emphasize, this study is the first to probe directly Bayesian inference in ADHD and utilize a task and model capable of finding out if ADHD participants have less precise sensory representations or likelihoods or deficits in forming (or using) predictions or priors. This is a valuable endeavor as there is increasing evidence of alterations in Bayesian inference in several neuropsychiatric disorders.This paper has very many additional strengths, both conceptual and methodological: it is one of the few investigations of implicit learning in ADHD, the writing is clear and compelling, the analyses and models are thoroughly presented and executed and thus convincing . Overall, I am very happy to see this powerful paradigm applied to ADHD and I believe this article is great and enthusiastically recommend it for publication. However, beforehand, I would like to see a few aspects developed and unpacked in more detail.Specific comments.1. Overall, I believe the introduction is nicely written and thorough and helps place this work within the broader literature of ADHD and computational psychiatry. However, the authors could also more explicitly allude to the larger literature of reward learning in ADHD and contrast with implicit learning, which can plausibly also be impaired in ADHD, but has been understudied. Something along the lines of: While impairments in reward learning and response have been extensively characterized in ADHD and are thought to stem from dopaminergic dysfunction , fewer studies have investigated implicit learning in ADHD. The ability to implicitly learn the statistics of a given environment in a short time frame allows for flexible and effective interaction with and function within that environment.2. page 10. “The evidence of difficulties in implicit learning, crucial for the development of priors, is more mixed in ADHD, with some studies finding a difference [19-22], whereas others do not [23-26]. “ In the discussion, the authors do justice to the possibility that the effects of medication might account for the lack of difference between Controls and ADHD . Are there differences in the medication status of the participants from the studies which found differences in implicit learning in ADHD vs the ones which did not?3.page 10. Citations [27-29] seem more suitable to be placed after “ADHD” vs where they are now: “There is also consistent evidence showing differences in frontostriatal and frontocerebellar circuitry in ADHD, areas implicated in implicit learning [27-29]. “ Other citations would support that these areas are also implicated in implicit learning. 4. Results related to Figure 5. The authors are testing several alternative models and the Bayes_P model convincingly captures the data best out of the models tested. The Bayes_P model seems to capture the data well and we can see this visually in Figure 5. I was wondering about two things. First, it seems that in Chalk et al, 2010 and in Karvelis et al, 2018 the Bayes model with a random lapse was able to capture the data very well, but in the Valton et al, 2019 and this paper the Bayes P with the prior-based lapses outperformed the Bayes model, even in Controls. What could account for this difference? One difference I can see is that the lapse parameters were estimated at lower values in Karvelis vs this paper, and thus accounted for a smaller part of the data? Second, while the model fits overlap with the data well, they do not seem quite as impressive as in Chalk or Karvelis. Is this fair to say? If so, what might account for these data sets being harder to fit? Relatedly, this might warrant a bit more caution in the interpretation of the model parameters.5. Page 19 .Last sentence of results. The authors write “Similarly, ASRS did not correlate with any of these model parameters. “, but do not provide values.6. Page 19. Discussion, end of the first paragraph: “there were no group difference in model parameters. “ It is worth spelling out again the model parameters: the mean of the acquired prior, the uncertainty of the prior or the uncertainty of the likelihood. Also, perhaps consider adding that while caution is warranted in the interpretation of individual model parameters, the goodness-of-fits of the Bayes_P model was comparably good across the ADHD and Control groups?7. Page 21. The authors end with “further testing is warranted in larger, more heterogeneous samples, and with more complex experimental tasks.” In the Chalk paper, the authors write in the discussion: “Future work could investigate this using a more complicated distribution of presented stimuli or statistical learning paradigm that produces slower learning of stimulus expectations . “ I believe such a discussion point might benefit from a bit more emphasis here, as impairments in ADHD might be revealed in tasks with increased complexity of the prior distribution to be learnt and used, or with slower tasks. The authors’ finding of differences in the influence of the prior over time lends support to this possibility.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, AttachmentRevision Richards Sept 2020.docxSubmitted filename: Click here for additional data file. 12 Nov 2020please see reply to reviewersAttachmentreply.docxSubmitted filename: Click here for additional data file. 16 Nov 2020Visual statistical learning and integration of perceptual priors are intact in Attention Deficit Hyperactivity DisorderPONE-D-20-20668R1Dear Dr. Series,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with disordered eating, especially addictive-like eating behavior . The exact mechanisms underlying this association are unclear. ADHD and addictive-like eating behavior are both associated with negative affectivity and emotion dysregulation, which we hypothesized are mediators of this relationship. The purpose of this systematic review was to review the evidence related to this hypothesis from studies assessing the relationship between childhood or adulthood ADHD symptomatology, negative affectivity, emotion dysregulation and addictive-like eating behavior. The systematic review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) recommendations. The literature search was conducted in PubMed and PsycINFO . Out of 403 potentially relevant articles, 41 were retained; 38 publications reported that ADHD and disordered eating or addictive-like eating behavior were significantly associated, including 8 articles that suggested a mediator role of negative affectivity or emotion dysregulation. Sixteen publications reported that the association between ADHD symptomatology and disordered eating or addictive-like eating behavior differed according to gender, eating behavior and ADHD symptoms . We discuss the practical implications of these findings and directions future research. Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by impairing levels of inattention and/or hyperactivity-impulsivity, which is thought to begin generally in childhood (before the age of 12) and significantly interferes with social, academic, and/or occupational functioning. Childhood ADHD prevalence is estimated to be between 5 and 7% . CurrentAn important research area related to addictive-like eating behavior focuses on the “food addiction” phenotype. According to Gearhardt and colleagues 2009) , this ca09 , thisDifferent explanations have been proposed to explain the association between adult ADHD and addictive-like eating behavior. One hypothesis is that the impulsivity dimension of ADHD symptoms may explain the co-occurrence of ADHD and addictive-like eating behavior, such as binge eating . The impAnother hypothesis regarding the relationship between adult ADHD and disordered eating behavior concerns the emotional self-regulation difficulties observed in both groups. Emotion regulation refers to conscious and unconscious processes regulating emotions. “Because emotions are multicomponential processes that unfold overtime, emotion regulation involves changes in emotion dynamics, or the latency, rise time, magnitude, duration, and offset of responses in behavioral, experiential, or physical domains” . Five tyThe hypothesis of a mediating role of emotion dysregulation in the association between ADHD and disordered eating is supported by the strong association found between emotion dysregulation and ED . EmotionThe role of emotion dysregulation in the association between ADHD and addictive behavior has also been investigated in gambling disorder. For example, Mestre-Bach and colleagues (2019) found people with gambling disorder and ADHD symptomatology had greater emotion regulation difficulties than those without ADHD. The authors found that individuals with ADHD-gambling disorder comorbidity had higher rates of the following emotion regulation difficulties: non-acceptance of emotional responses, difficulty pursuing goal-directed behaviors when experiencing negative emotions, difficulty controlling impulsive behaviors when experiencing negative emotions, limited access to emotion regulation strategies, and lack of emotional clarity . Their rTo fill this gap, the aim of this study was to conduct a systematic review of studies investigating the association between childhood/adult ADHD, negative affectivity, emotion regulation, and disordered eating, with a specific focus on addictive-like eating behavior . We investigated negative affectivity, a common term involving many negative emotions such as anxiety, depression, negative urgency, stress. To this end, we first explored the characteristics of studies conducted in this field of research. In order to investigate the association between ADHD and disordered eating, we examined the prevalence of ADHD and disordered eating comorbidity within different populations. Next, we assessed negative affectivity and emotion regulation in individuals with ADHD, and finally we examined the involvement of these features in the relationship between ADHD symptomatology and addictive-like eating behavior. Due to potential difference in these relationships between children/adolescents and adults, we investigated both populations.We hypothesized that: (1) individuals with disordered eating would show more ADHD symptoms; (2) individuals with ADHD symptoms would have higher levels of disordered eating; (3) ADHD symptoms would be associated with severity of addictive-like eating behavior; (4) the level of ADHD symptoms would be associated with high levels of negative affectivity and emotion regulation difficulties; (5) negative affectivity and emotion regulation difficulties may be mediators in the relationship between ADHD symptoms and addictive-like eating behavior.This systematic review included publications investigating the association between ADHD and addictive-like eating behavior such as loss of control overeating, binge eating, and preoccupation with food, which are the main FA symptoms, and some DSM-5 EDs  such as BN and BED, which show high FA prevalence ,20.This review was undertaken according to the quality standards of the Preferred Reporting Items for Systematic reviews and Meta-Analyses PRISMA; .We conducted the literature search on 2 September 2020; the systematic literature review methodology included analysis of the electronic databases PsycINFO and PubMed. In order to identify all relevant publications on the association between [ADHD] and [FA symptoms and/or disordered eating], we used the following key words: [“ADHD” OR “attention-deficit hyperactivity disorder”] AND [“food addiction” OR “binge eating” OR “eating disorder” OR “bulimia” OR “obesity” OR “obese” OR “overweight”]. We included studies that used these keywords in their abstract , concerned directly or indirectly with ADHD and eating behavior (criterion I6) and investigating ADHD and disordered eating symptoms in the same individual (criterion I7). These inclusion criteria led to exclusion of review and meta-analysis articles (criterion E5), publications which did not address ADHD and eating behavior directly or indirectly, or focused on ADHD treatment or medical imaging (criterion E6). We also excluded all publications that investigated the impact of parents’ disordered eating or body mass index (BMI) on their child’s ADHD symptoms (criterion E7).The papers thus retained were then read in full and appraised. We did not use a specific tool to appraise the quality of these studies, but they were checked for all the inclusion criteria and selection errors. We also checked that all the studies assessed ADHD and eating behavior using a validated instrument such as self-administered questionnaires or clinical interviews (criterion I8).Regarding the characteristics of the populations studied, as our aim was to provide an overview of the association between ADHD and disordered eating, we did not consider age or gender as exclusion criteria.To investigate the characteristics of the publications, the following data were extracted: author names, country and year of publication, source, sample characteristics , study design. We also extracted data about the prevalence of ADHD in individuals with disordered eating and the prevalence of disordered eating in individuals with ADHD. We thus identified the ADHD assessment tools used, the use of medication especially for individuals with ADHD, the type of eating behavior and the tools used to assess it. Finally, we examined the main results and conclusions about disordered eating and ADHD comorbidity. In this way, we extracted data regarding the association between ADHD and disordered eating, especially addictive-like eating symptoms and the involvement of negative affectivity and emotion self-regulation.It should be noted that we use the word “symptom” to describe features of disordered eating and ADHD assessed only through self-administered questionnaires, and “diagnosis” or “severity” when assessment was through clinical interviews. Moreover, we use the word “eating disorder” (or ED) only for DSM disorders such as BN, BED and AN, and the word “disordered eating” as a generic word to include all pathological eating behaviors/symptoms such as binge eating, food addiction, loss of control overeating, strong desire for food, preoccupation with food, bulimic symptoms….-n = 38), including 30 publications which focused on the association between ADHD and BMI [No data about behavioral features of eating n = 3, includi and BMI ,83,84,85-n = 4) [Non-representative sample, e.g., autism spectrum disorder (n = 4) ,87,88,89-n = 10) [Previous selection errors (n = 10) ,96,97,98-n = 3) [Investigations did not include ADHD-disordered eating association (n = 3) ,100,101-n = 1) [No access to full text (n = 1) We initially identified 403 articles, of which 97 were screened and selected for full-text reading. After full-text reading, 56 publications were excluded for the following reasons:Thus, 41 publications were included in this systematic literature review for qualitative synthesis . Others were conducted in Sweden , France, Canada , the UK , Spain, Brazil , Norway, Australia, Israel, Korea, Switzerland, Greece, Iran, Germany and China . One study did not specify the country of recruitment.The majority of these studies were conducted in the USA , and 19.5% were prospective longitudinal studies (n = 8).Among the 41 publications, 80.5% were cross-sectional (Nineteen studies were conducted with children and/or adolescents (46.3%) and 24 with adults (58.5%). Two studies had a mixed adolescent-adult sample (4.9%).n = 19) involved clinical populations: patients with severe obesity recruited in obesity and centers or prior to bariatric surgery (n = 8), patients with disordered eating (n = 6), ADHD outpatients (n = 2), or patients recruited in psychiatric departments (n = 3).Twenty-two studies were conducted with participants from the general population (53.7%), and 46.3%  in 21 studies (51.2%), and through self-administered questionnaires in 20 studies (48.8%).For children and adolescents, the main assessment tool for ADHD was the Kiddie Schedule for Affective Disorders and Schizophrenia  and the ADHD Rating Scale . For adults, ADHD was mainly assessed with DSM-IV or DSM5 semi-structured interviews using the Composite International Diagnostic Interview (CIDI), the Diagnostisch Interview Voor ADHD bij volwassenen (DIVA 2.0), or the Structured Clinical Interview for DSM Disorders (SCID). The main self-administered questionnaire was the Adult ADHD Self-Report Scale . It should be noted that some studies used the ASRS, a screening scale, as a diagnostic tool.n = 13), included in the diagnostic tool or additionally reported mainly through the Wender Utah Rating Scale (WURS) (n = 3).Fifty-four percent of the studies with adults included a retrospective assessment of childhood ADHD symptoms , the Children’s Eating Attitude Test (ChEAT), the Child Eating Behavior Questionnaire (CEBQ), the Eating Disorder Examination Questionnaire (EDE-Q) (n = 3 for each), and the Child Eating Disorder Examination (ChEDE), which specifically assesses loss of control overeating (n = 3). None of the studies used the Yale Food Addiction Scale for Children.Among the studies of children-adolescents, 7 (36.8%) assessed eating behavior through interviews (including semi-structured interviews), 10 (52.6%) through self-administered questionnaire, and 2 (10.5%) used both interviews and self-administered questionnaires. Various tools were used to assess disordered eating behavior, including the following self-administered questionnaires: the Eating Disorder Inventory-2 (EDI-2) (n = 3 for each). The main self-administered questionnaires were the Binge Eating Scale (BES) to assess binge eating (n = 5), the original (DSM-IV-TR based) Yale Food Addiction Scale (YFAS) and the YFAS 2.0 (DSM-5 based) to assess FA (n = 2), the EDE-Q and the EDI-2 to assess disordered eating (n = 4 for each), and the Bulimic Investigatory Test Edinburgh (BITE) to assess bulimic symptoms (n = 4).For adults, 14 studies (58.3%) used professional interviews (including semi-structured interviews), 14 publications (58.3%) were based on self-administered questionnaires investigating disordered eating, and 4 (16.7%) assessed disordered eating through both interviews and self-administered questionnaires. The main ED diagnostic tools used during clinical interview were the Mini International Neuropsychiatric Interview (MINI) and the SCID (p < 0.001). The odds of loss of control overeating were increased 12.68 times for children with ADHD : 3.11–51.64; p < 0.001) after adjusting for age, sex and race [p < 0.001) [Four studies focused on the association between disordered eating and addictive-like eating behavior among children with ADHD symptoms . They shand race . Another< 0.001) . Moreove< 0.001) , adolescWithin adult population, eight studies assessing disordered eating prevalence among individuals with ADHD symptomatology.p < 0.01), especially for the inattentive subtype .Two of these studies, with no control group, found a prevalence of 8.6% for BN , and 1.1n = 4) reported that ADHD was associated with a higher risk of addictive-like eating symptomatology: food addiction, binge eating, uncontrolled eating, significant distress in relation to food, and made him/herself be sick because he/she felt uncomfortably full [p = 0.034) [All publications assessing addictive-like eating symptoms in individuals with ADHD symptoms  .Three studies of overweight or obese children assessed ADHD prevalence . One stup < 0.05; [p = 0.026). Furthermore, Kim and colleagues (2018) [Rojo-Moreno and colleagues (2015)  and Moha < 0.05; : 7.6% vss (2018)  found thThree studies conducted in adults with severe obesity, recruited in obesity hospital departments, reported the prevalence of ADHD . Nielsenp = 0.392).Five studies assessed ADHD in clinical populations of women with ED. High ADHD prevalence was found, especially among women with ED involving binging/purging behavior : from 10.2% to 49.8% ,123,124.p = 0.018).After assessing disordered eating in a general non-clinical population, Brewerton & Duncan (2016)  found thTwelve studies explored the association between ADHD and addictive-like eating in children or adolescents.p < 0.05) [p < 0.001) [p = 0.0003), and that severity of ADHD hyperactivity/impulsivity symptoms was greater in binge-eating and non-binge eating individuals than in controls (Bonferroni corrected p < 0.01). Patients who reported binging/purging behavior scored higher on both inattentive and hyperactivity/impulsivity ADHD subscales [Kim and colleagues (2018)  found th < 0.05)  or binge< 0.001) . In the ubscales . Kurz anubscales  used a lTwo studies conducted with non-clinical samples of children found that ADHD symptoms  and ADHDp < 0.05), objective binge eating  [p < 0.0001), emotional overeating  and emotional undereating  [p < 0.0001), EDE-Q , EDI-2  [Some studies corroborated these results through correlation analysis. They found that ADHD severity was positively correlated with objective overeating  , BN symp 0.0001) , and witctively) .2(1) = 16.61, p < 0.001; ꭓ2(1) = 10.64, p < 0.01), but not subjective binge eating (ꭓ2(1) = 1.30, p = 0.25).Four studies conducted regression analyses and found a significant association between ADHD and disordered eating, and more specifically addictive-like eating behavior. These studies showed that ADHD symptoms were associated with loss of control overeating and binge eating , food prFurther investigations involving mediation analyses highlighted the mediator role of loss of control overeating and binge eating in the relation between ADHD and BMI ,105.p = 0.024) and purging  behaviors at 16 or 19. However, Yilmaz and colleagues (2017) [p < 0.01).Four longitudinal studies found a positive association between ADHD symptoms during early-childhood and addictive-like eating behavior in later childhood or adolescence ,129,130.Thirteen studies focused on the association between ADHD and disordered eating in adults.p = 0.007 and p < 0.001, respectively). Nazar (2018) [p = 0.07), but greater binge eating among those with comorbid ADHD-ED (p < 0.001). In individuals with ADHD diagnosis, there was no difference between individuals with and without ED comorbidity in terms of inattentive and hyperactivity/impulsivity symptomatology (p = 0.53 and p = 0.75 respectively). Van der Oord and colleagues (2017) [p < 0.01). In this population, ADHD diagnosis was associated with bulimic symptoms, greater binge eating and higher FA scores [In a study with mood disorder outpatients, Woldeyoannes and colleagues (2015)  found nor (2018)  found nos (2017)  assessedA scores ,11. Simip = 0.046), which was not found for either inattentive or hyperactivity-impulsivity factors (p = 0.06 and p = 0.016 respectively) [p = 0.001), especially for binging , purging  and loss of control overeating  symptoms. This association remained significative only with ASRS inattentive factor, especially for binging and loss of control overeating. Regression analyses confirmed the predictive role of high ASRS scores on the persistence of disordered eating  [Six publications involved samples of individuals with ED. They found that ADHD symptomatology and diagnosis were associated with ED, especially binging/purging behaviors such as BN and AN binge/purge subtype, which were related to inattentive symptoms ,123,124.36–4.91) .p < 0.001) [p < 0.001; [p < 0.001). Similar results were found for patients with ED [p < 0.001) and hyperactivity/impulsivity symptoms . However, Hanson and colleagues (2019) [p > 0.05).Among the six studies that analyzed the correlations between ADHD symptomatology and disordered eating, three were conducted with a student population and found positive correlations between ADHD and bulimic symptoms   and bing< 0.001) : r = 0.4< 0.001; : r = 0.2 with ED ,123 or s with ED , for bots (2019)  found noFive studies conducted regression analyses. Woldeyoannes and colleagues (2015)  showed tNielsen and colleagues (2017) and Brunault and colleagues (2019) reported that addictive-like eating was more strongly associated with adulthood than childhood ADHD (: the corp < 0.0001) [Two studies conducted with children found that ADHD group had more adolescent with clinical internalizing    and ADHD 0.0001) .p < 0.008 to 0.42, p < 0.001) [p < 0.001 to 0.38, p < 0.001) [p < 0.0001 and r = 0.57, p < 0.0001 respectively) and depressive symptoms  [p < 0.001), mood disorders such as major depressive disorder , as well as borderline personality disorder traits (24.0% vs. 2.7% p < 0.001). Gorlin and colleagues (2016) [Among the studies included in this review, twelve focused on the comorbidity of ADHD symptoms and negative affectivity. Many of them identified a high correlation between ADHD symptoms and anxiety  ,120 and < 0.001) ,120,132.< 0.001) ,122. As < 0.001) ,110,111,< 0.001)  showed tp < 0.001) and more frequent perceived stress  [p = 0.02) [p < 0.001, hyperactivity: r = −0.348, p < 0.001 and impulsivity: r = −0.476, p < 0.001) [p < 0.001 for both inattentive and hyperactivity/impulsivity ADHD symptoms) [ADHD symptomatology was also associated with a higher number of stressful life events (3 vs. 1.7 < 0.001) . In addi = 0.02) , higher  = 0.02) . These r< 0.001) , and posymptoms) .Tong and colleagues (2017)  clarifieA study with adults by Jacob and colleagues (2018)  reportedp = 0.350 and hyperactivity/impulsivity: r = −0.05; p = 0.307 respectively) [Similarly, Kaisari and colleagues (2018)  found thctively) .Christian and colleagues (2020)  found thWilliamson and colleagues (2017)  investigThe purpose of the present study was to investigate the association between ADHD symptomatology, disordered eating, especially addictive-like eating behavior, and emotion self-regulation. We noted a significant association with disordered eating  in 38 publications, eight of them highlighting the mediator role of negative affectivity and emotion dysregulation. This trend was qualified in 19 publications; 16 publications reported differences depending on type of disordered eating behavior, gender or ADHD symptoms. The majority of results thus suggest that both childhood and adulthood ADHD symptomatology is associated with a higher risk of addictive-like eating behavior, especially binging and/or purging, loss of control overeating, emotional overeating and binge eating, bulimic symptoms, as well as a strong desire for food, food responsiveness and food preoccupation. Furthermore, some authors suggest that ADHD symptoms during early childhood lead to disordered eating during later childhood or adolescence.Several authors found that severe obesity or ED comorbidities increased the strength of the association between ADHD and disordered eating, especially binge eating. Their results indicate that binge eating and purging behavior play a key role in this association, particularly the BN and AN binge/purging subtype. According to Granero and colleagues (2014) , this suLongitudinal studies demonstrate that a combination of high inattention and hyperactivity/impulsivity symptoms in childhood lead to increasing BMI in late childhood and to ED in adolescence through addictive-like eating behaviors. However, some publications reported that disordered eating is particularly linked to inattentive symptoms. It is not possible in this systematic review to draw clear conclusions about the involvement of inattentive and/or hyperactivity/impulsivity ADHD symptoms in the association between ADHD and addictive-like eating behavior, and further investigations are needed.The second aim of this systematic review was to examine the mediator role of negative affectivity and emotion self-regulation in the association between ADHD and addictive-like eating behavior. We showed that high ADHD severity would be associated with a high risk of disrupted emotion regulation, negative affectivity , which mediate the link between ADHD symptomatology and disordered eating, especially addictive-like eating behavior. Some studies show that ADHD symptoms are associated with high emotion dysregulation ,135, impThe studies included in this systematic review suggest a pattern of links between ADHD symptomatology, negative affectivity, emotion regulation, and addictive-like eating behaviors . ADHD syA better understanding of the mechanisms underlying the association between ADHD symptomatology and disordered eating suggests new approaches to psychological interventions. In view of the high incidence of disordered eating among people with ADHD, it seems important to identify any maladaptive eating behavior. Interventions aimed at assessing and targeting emotion dysregulation could be an appropriate way of preventing disordered eating behavior and FA, as well as comorbid anxiety and depression disorders. Integrative cognitive-affective therapy (ICAT) adapted to BN and BED targeting emotion regulation  has been shown to be effective in reducing the frequency of binge eating . SimilarThis review has a number of limitations. First, it does not provide any causal link. Indeed, as far as we know, no study investigated the effect of ADHD negative affectivity or emotion dysregulation therapeutic interventions on addictive like eating behavior. This link could be of interest for further studies. Moreover, this systematic review includes only qualitative and no quantitative analyses. The variety of populations studied  and methods used to assess ADHD and disordered eating make it difficult to draw clear conclusions. In addition, some studies were based on ADHD diagnosis criteria of the DSM-IV-TR and others on DSM5 criteria, with a change of symptom onset from 7 to 12 years of age, making it difficult to compare results. Another limitation involves publications which did not provide necessary information about current medication. Indeed, medication can conceal symptoms of disrupted emotion and eating, so there is an impact on results of investigations. Furthermore, as only a few studies assessed food addiction directly, we included those involving addictive-like eating symptoms and various aspects of food addiction. It should be noted that the addictive nature of food is still under debate, notably whether features of substance addiction can be applied to food, the addictive power of palatable food, common features such as tolerance and withdrawal, and the distinction between food addiction and binge eating. However, people presenting with this type of pathological eating suffer in similar ways as those with substance use disorder, including “feelings of deprivation when the substance is withheld, a propensity to relapse during periods of abstinence, and consumption that persists despite awareness of negative health, social, financial, or other consequences” . The pubFuture studies should investigate in greater depth emotion regulation difficulties in comorbid adult ADHD and addictive-like eating behavior, and the involvement of specific ADHD symptoms such as inattention, impulsivity and hyperactivity. This could clarify which emotion regulation strategies and ADHD symptoms should be targeted in clinical interventions. It would be interesting to investigate specific symptoms of ED in order to identify common sub-groups. The majority of studies of ADHD symptomatology in people with disordered eating were conducted with female samples, although some authors noted male-female differences in the relationship between ADHD and disordered eating. Future studies should thus investigate distinctive male characteristics in order to determine whether clinical interventions should be gender-specific. In addition, in order to identify causal links between ADHD symptomatology and addictive-like eating behavior, more longitudinal studies are needed. This would make it possible to set up early interventions with children with ADHD and investigate the impact on ADHD symptomatology, eating behaviors and risk of obesity in adolescence and adulthood. An important area of research would be to focus on the interplay between dysregulation of sleep, weight gain and emotional dysregulation, as it has been suggested by some authors  that altDespite its limitations, this review provides information about the co-occurrence of ADHD symptoms and addictive-like eating behavior. It confirms the strong association between ADHD, emotion dysregulation and binge eating/addictive-like eating behavior in both clinical  and non-clinical populations. The data support the hypothesis of a mediating role of negative affectivity and emotion self-regulation difficulties in the association between addictive-like eating behavior and ADHD. This review paves the way for future therapeutic interventions that could improve clinical outcomes for people with ADHD and disordered eating.