Working memory (WM) impairments in ADHD have been consistently reported along with deficits in attentional control. Yet, it is not clear which specific WM processes are affected in this condition. A deficient coupling between attention and WM has been reported. Nevertheless, most studies focus on the capacity to retain information rather than on the attention-dependent stages of encoding and retrieval. The current study uses a visual short-term memory binding task, measuring both behavioral and electrophysiological responses to characterize WM encoding, binding and retrieval comparing ADHD and non-ADHD matched adolescents. ADHD exhibited poorer accuracy and larger reaction times than non-ADHD on all conditions but especially when a change across encoding and test displays occurred. Binding manipulation affected equally both groups. Encoding P3 was larger in the non-ADHD group. Retrieval P3 discriminated change only in the non-ADHD group. Binding-dependent ERP modulations did not reveal group differences. Encoding and retrieval P3 were significantly correlated only in non-ADHD. These results suggest that while binding processes seem to be intact in ADHD, attention-related encoding and retrieval processes are compromised, resulting in a failure in the prioritization of relevant information. This new evidence can also inform recent theories of binding in visual WM. Nevertheless, attention is neither the only cognitive process affected in this condition nor the most affected one. For instance, the search for specific impairments in selective attention and orienting attention in ADHD has not yielded consistent results6. Among other cognitive processes, frontal executive functions impairments are consistently reported in ADHD8. Moreover, Working Memory (WM) impairment is considered a significant cognitive feature differentiating between ADHD and non-ADHD children9.Attention-Deficit/Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disorder characterized by attentional difficulties, hyperactivity and impulsivity11. WM is defined as a limited-capacity system responsible for encoding, retaining or maintaining, and manipulating cognitive representations of stimuli. Such memory system encompasses independent phonological (PH) and visuospatial (VS) subsystems, and a central executive (CE) component, responsible for the attentional control. A fourth component, the episodic buffer, was later added12. Other authors, like Cowan13 or Engle14, have proposed WM models that emphasize the predominant role of attention throughout WM stages. Attentional involvement in each memory stage has also been a matter of debate. It has been suggested that attention is necessary for encoding, updating and retrieval but has a limited role during retention15.The predominant theoretical model of WM is Baddeley’s multi-component model19. The evidence gathered to date suggests that binding of features in visual WM requires no more attentional resources than processing individual features. The consistency of these findings across a thorough experimental series, led Allan Baddeley to revise the WM model20. Such a revision tried to address the concept and function of the episodic buffer; a WM component wherein binding functions were thought to be carried out via the support from attention. Despite the attractiveness of these studies, they focused on healthy samples of university students who, although subjected to experimental manipulations of attention, may have enough available resources to cope with attentional interference and still perform the task at a high level of accuracy. It would be highly desirable to further investigate visual WM binding in individuals with attentional disorders, such as those diagnosed with ADHD.In the experimental psychology literature, several studies have been reported which aimed to investigate the extent to which binding surface features in visual WM is an automatic or an attentional demanding function9. Nevertheless, processes like conflict detection, detecting mismatch from expectations, shifting or interrupting a response, and the effortful allocation and maintenance of attention and working memory resources towards the attainment of a future goal appear to be compromised in ADHD9. Metanalytic studies suggest that processes such as executive attention, working memory, along with decision making factors like motivation and reward are central to understand the ADHD cognitive profile21. Reaction times variability is also considered part of this cognitive profile3.Traditionally, WM is considered part of the executive functions. Both WM and executive functions, have been criticized due to their limited specification21. Regarding the specific WM deficit in ADHD both, phonological and visuospatial components seem to be affected, being the task demands on the central executive (CE) one of the key moderators to explain the results23. That is, the most sensitive WM tasks in ADHD are those with high demands of CE component. For example, those that require the participants to remember stimuli and later recall them in a different pattern than the originally presented, or those that require to compare a newly presented stimulus with a representation in WM and to update that representation. ADHD subtype (predominantly Inattentive or Combined) seems to have no significant effect on WM dysfunction, perhaps due to their shared inattention symptomatology25.When children, adolescents and adults with ADHD are assessed, WM and other executive dysfunctions stand out as the ones with the most reliable discriminative power26. These impairments have been related to academic underachievement due to poor acquisition of cognitive skills in children, which may also have a long-term impact in social development and quality of life28. Unfortunately, despite the consensus about the relevance of WM deficits in ADHD, the precise mechanisms that affect ADHD performance in WM tasks are poorly understood30. Moreover, WM training seems to have a limited beneficial impact on ADHD, even when a significant improvement in WM performance is achieved31. This emphasizes the need to understand and empirically document the nature of the WM impairment in ADHD in relation with other process such as attention, to improve the development of diagnostic or intervention tools.The close relationship between selective attention and WM has long been considered a natural candidate to explain WM impairments in ADHD33. Interestingly, reduced P3 amplitude in ADHD has been described both in children34 and adults35. The retention stage is commonly studied by means of contralateral delayed activity (CDA) which is sensitive to WM load and capacity36. This ERP component is a negative slow deflection usually detected at contralateral parietal sites that exhibit larger amplitude  as the number of items in WM increases37.Electrophysiological measures such as event related potentials (ERP) are especially useful to study WM, as they allow to differentiate stages of encoding, retention and retrieval which cannot be directly inferred from behavioral responses. Encoding and retrieval are systematically associated with P3-like ERP components38. This task usually consists of the presentation of an array of stimuli for a short period of time (S1), which must be kept in memory (retention period) until the presentation of a test stimuli array (S2), where the subject must respond whether the test stimuli is the same or different . This design allows the evaluation of the three stages described for WM: encoding, retention and retrieval. In a recent study, Spronk et al.29 used a CDT to evaluate the impact of distractors on the retention capacity of WM, by comparing adolescents and adults with ADHD and healthy controls. They found that adolescents were more affected than adults by the presence of distractors but found no differences regarding encoding and retention between groups. However, this study evaluated only up to the retention period and not later stages where the subject must contrast the target with the memory representation and generate a response. Post-retention is highly overlooked in most WM and ADHD studies39. Nevertheless, ADHD difficulties in working memory updating and retrieval have been previously reported using a different WM task39. Additionally, previous results of our groups suggest that the use of cognitive resources and particularly attentional resources in ADHD reflects a differential style more than a deficit pattern or a deficient capacity2. Such types of functional impairments could well affect the use of WM in the post-retention period more than the process of forming representations in WM. Thus, an accurate characterization of encoding and retrieval working memory stages in ADHD could be relevant to better understand WM role in this condition.Change detection tasks (CDT) have proved to be a successful paradigm to specifically explore attention and WMIn summary, consensus exists regarding a poor performance of subjects with ADHD in tasks that explore WM functioning. Nonetheless, there is no clear evidence concerning which process or mechanism is actually compromised. Moreover, whether this deficit depends on encoding, retention, or retrieval processes (or some combination of them) is still unknown. Likewise, the retrieval of the information from WM is also an important and mostly overlooked stage that should be explored in ADHD.et al.40 designed a visual short-term memory binding task that allows studying binding and change detection controlling the potential influence of the spatial location and spatial relations within the stimuli arrays. It has allowed to describe specific patterns of WM impairment in other conditions (e.g. Alzheimer’s Disease)41. In the present study we use a modified version of this experimental design that differs from previous studies controlling two possible confounding aspects. First, it minimizes the possibility of linguistic rehearsal by using no nameable polygons and non-primary colors as stimuli. Second, it allows controlling the use of spatial cues by changing the spatial location of stimuli between S1 and S2 displays. WM binding is studied by contrasting blocks in which all shapes are presented in black, so only the shape should be retained (Shape-Only) and others in which the binding of shape and color is necessary to solve the task (Color-Shape binding).Here we develop a novel approach aimed to explore functional indicators  of the different WM stages to further our knowledge on their role in the ADHD related WM impairment. Parra 43. This would be especially manifest when S2 stimuli are different from S1. The need for WM binding (color and shape) could impact overall behavioral performance in all participants, but we expect no specific differences regarding ADHD diagnosis. At the electrophysiological level, we predict that encoding and retrieval impairment in ADHD will be associated with a reduced amplitude of the corresponding P3 ERP components. Additionally, we expect that these electrophysiological markers should be correlated . In the retrieval stage, group differences should be more evident when S1 and S2 are different. Notwithstanding, these electrophysiological patterns should hold regardless of whether individual or bound features are the memoranda.Taking into account the ADHD performance on other related tasks, as well as evidence gleaned from the experimental psychology literature, we expect in this study that impaired attention would impact on both encoding and retrieval, resulting in a poorer overall performance in ADHD participants1, 34) = 0.16832, p = 0.68419), IQ  = 2.8138, p = 0.10263) and educational level (school grade). A complete clinical neurological and psychological evaluation was conducted in all the participants to rule out any potential confound. That included Conner ‘s Rating scale for parents and teachers, MINI-KID, STAI anxiety inventory and WISC III test. Subjects with antecedents of any other Neurological or Psychiatric disease were excluded from the study. Comorbid symptoms of anxiety and conduct disorder were observed, but no ADHD participant met the criteria for any mayor comorbid disorder. They were being treated with methylphenidate for at least four months, but suspended medication 24 hours prior to the study.A group of 18 adolescents diagnosed by a certified pediatric neurologist with ADHD Combined Subtype according to the DSM-V criteria, that were being treated at the neurology service of the Luis Calvo-Mackenna Children’s Hospital in Santiago and an equal number of non-ADHD adolescents, from public schools of the same metropolitan area, voluntarily participated in the study. Their ages were from 12 to 14 years (12.61 ± 0.80). They were matched by age , 3° to the left and right of a central fixation cross on a grey background. Each item size was 1° and was, at least, 2° apart from any other item. Items for the study display (S1) were randomly selected from a set of eight polygons and eight colors and randomly allocated to the 9 positions within the virtual grid. During the test phase (S2), the same three locations used during the study phase were used but items locations were interchanged. Hence, items were never presented in the same locations across the study and test display. By this way, spatial location was render uninformative  followed by the test. Each block presented 80 trials . In the last case, two shapes or two colors were replaced by different ones. There was a total of 320 trials. The total duration of this task was approximately 35 minutes.During each trial a fixation cross was presented, and participants were asked to keep their eyes on it and to press a key to initiate the trial. Fixation remained on screen throughout the trial. After 400 ms, two arrows were presented for other 400 ms above and below fixation. Arrows direction indicated which side should be attended. After a delay of 600 ms the S1 array was presented for 1000 ms, followed by a 1000 ms retention interval. Then, the S2 array is presented and remained on until the participant responded “Same” or “Different”, by pressing a specific key with their dominant hand.EEG recording sessions began by asking participants to sit comfortably in a dimly lit, electrically and acoustically shielded room. A shin rest device was used to reduce unwanted head movements. Participants receive standard verbal instructions about the experimental procedure and the experimental task. All participants were evaluated individually.Electrophysiological signals were recorded using a NeuroScan 40-channel Digital Electroencephalograph with a high-resolution NuAmp amplifier. A 40-channel cap (Quick-Cap) from the same company was used for electrode placement following the international 10/20 electrode sites and linked mastoids as the reference. Impedances were kept below 5 kΩ throughout the recordings. A/D sampling frequency was set at 1000 Hz. A band-pass digital filter between 0.1 and 30 Hz was later applied to remove unwanted frequency components. Two additional bipolar derivations were used to monitor vertical and horizontal ocular movements .51. Eye movements or blink artefacts were corrected using ICA . Remaining trials that contained voltage fluctuations exceeding ±100 μV (microvolts), transients exceeding ±100 μV, or electro-oculogram activity exceeding ±50 μV were rejected. Artifact free waveforms were segmented into 1200 ms epochs starting 200 ms before the onset of S1 and S2 arrays. Separate average waveforms for each condition were generated.For behavioral data, the percentage of correct responses (accuracy) and reaction times (RTs) were measured in all subjects and conditions. Regarding ERPs, offline EEG signals were analyzed using EEGLAB/ERPLAB Matlab toolbox52. For simplicity, only the results from posterior midline (Pz and Oz) were shown. Post hoc comparisons were assessed with Tukey HSD test. Greenhouse-Geisser and Bonferroni corrections were applied to compensate for violations of sphericity and multiple comparisons. Only statistically significant results of ERPs (p < 0.05) were used to test the association among sequential ERP components. Correlations were explored using Pearson’s correlation coefficient (r). Fisher’s R to Z procedure was later used to compare correlations coefficients53. Follow up Bayesian analysis were performed to explore non-significant interactions in early and late encoding P3 components using JASP software 0.11.154.We used a mixed model ANOVA with repeated measures for behavioral  and ERP variables. The Encoding period of ERP components analysis has two levels: (1) Group (ADHD vs no-ADHD); and (2) Condition (Single Shape vs. Shape-Color Binding). For the retrieval period , a three levels analysis was performed: 1) Group (ADHD vs no-ADHD); 2) Condition (Single Shape vs. Shape-Color Binding); and 3) Trail type (Same vs. Different). All statistical calculations on ERPs were performed using individual waveforms. Mean amplitude in the windows 100–130 ms for P1, 180–210 ms for N1, 320–430 ms for early-P3, 430–600 ms for encoding late-P3, and 320–430 for Retrieval P3 were selected. P1 and N1 amplitudes were measured on the occipital region  yielding similar results. Encoding early and late P3 and retrieval P3 were measured in Parieto-Occipital midline region . Selection of electrodes sites and ERP measures was conducted following the recommendations previously described for this type of procedures = 10.047, p = 0.00322, η2 = 0.23) compared to the non-ADHD group. There was a significant and expected main effect for condition type, whereby Only-Shape resulted in better performance  = 39.803, p = 0.00000, η2 = 0.54) than Color-Shape condition, but no Group x Condition interaction  = 0.020, p = 0.88969, η2 = 0.00) was found. Trial Type (same or different) was also significant. When S2 was different from the S1, a significant reduction in the hit rate  = 13.817, p = 0.00072, η2 = 0.29) was observed. A significant interaction was found between Group and Trail Type  = 5.2559, p = 0.02818, η2 = 0.13). Post-hoc analyses showed a significant drop of performance in the ADHD group during different trials    = 13.035, p = 0.00097, η2 = 0.28). The Color-shape condition showed slower RTs than the Only-Shape condition  = 6.2918, p = 0.01706, η2 = 0.16). No other effects were observed.Regarding reaction times (RTs), the ADHD group showed slower responses than the non-ADHD group in all conditions =12.304, p = 0.00129, η2 = 0.27) compared to the Color-Shape condition. No differences in amplitude between groups were observed =00440, p = 0.94751, η2 = 0.00). The following N1 component showed no amplitudes differences between groups =0.08056, p = 0.77826, η2 = 0.00), or conditions =1.3163, p = 0.25927, η2 = 0.03). After this negativity, a wide P3-like positivity was identified in the parieto-occipital region of the scalp, with a peak around 340 ms and extended in time up to 600 ms. The earlier segment of this component, between 320 to 430 ms, exhibited larger amplitude in the non-ADHD group than in the ADHD =5.3294, p = 0.02718, η2 = 0.14), no significant amplitude difference by condition =2.9067, p = 0.0973, η2 = 0.07) or Group x Condition interaction =0.1811, p = 0.67310, η2 = 0.00) were found. The later part of this positivity (430–600) also showed larger amplitude in the non-ADHD =6.4352, p = 0.01595, η2 = 0.16), and larger amplitudes for the Color-shape condition =5.8871, p = 0.02071, η2 = 0.15). Again, no significant Group x Condition interaction =2.4440, p = 0.12723, η2 = 0.06) was found  and strong support in the P3 late windows (BF10 = 0.014).The P1 component at encoding showed larger amplitudes for the Shape-Only condition =0.26277, p = 0.61154, η2 = 0.00). There was a significant main effect of trial type, due to larger P3 amplitude in the Same trials compare to the Different ones =4.9394, p = 0.03301, η2 = 0.13). A statistically significant interaction between group and Trial type was observed =4.3989, p = 0.04347, η2 = 0.11). Follow up post-hoc contrasts showed that while P3 amplitude differentiated between Same and Different trials in the non-ADHD group , it was not the case for the ADHD group  = 0.5230, Different Z(obs)= 0.7750.The amplitude of the encoding early-P3 significantly correlated with that of the retrieval period, both in the Same  and Different  trial types. Follow up analysis showed that these significant correlations were driven by the results from the non-ADHD group: Same  and Different . The equivalent analysis in the ADHD group showed no significant correlation: Same  and Different  . The electrophysiological results evidenced significant differences between the groups in ERP components elicited during encoding and significant interaction Group x Trial Type during retrieval. The need to bind color and shape resulted in no significant Group x Condition interaction, suggesting that ADHD has no differential impact on binding functions carried out in WM. There was a significant correlation between the amplitude of the P3 component elicited during encoding and that elicited during retrieval that was significant only in the non-ADHD group. These results have important implications for our understanding of the involvement of WM in ADHD and the functional organization of this cognitive function. We discuss these implications below.45. They are interpreted as the cost of integrating features into objects to be kept in WM and are in line with the predictions from the feature integration theory55. Additionally, all participants performed better when the study (S1) and the test arrays (S2) were composed of the same items relative to trials where they had to detect and report changes happening in the test array. That is, when they had to update the WM representation to account for a change. These results are in line with previous studies using similar WM tasks56. Our hypothesis of ADHD’s poorer performance in all conditions was also confirmed, supporting previous reports in the literature42. Interestingly, this was significantly increased when a WM updating was needed.The behavioral results of the current study supported our original hypothesis. All participants showed better accuracy in the “Shape-Only” than in the “Color-Shape” condition. This result has been previously observed in other studies using similar experimental designs57. For instance, a deficient filtering of the incoming information could overload WM, rendering it also deficient59. This idea implies that attention and WM resources operate in tandem to process the available stimuli with the former supporting the latter. Nevertheless, the characterization of attention impairments in ADHD does not support this notion. The idea of a deficient filtering in ADHD causing an overload of working memory and resources depletion has been disputed59. Previous studies from our group2 point in a different direction. First, although ADHD do have problems when dealing with distractors it is not necessarily due to a deficient attentional filtering. Instead, they seem to follow task relative relevance to select and pay attention to objects2. Furthermore, several studies have proven that specific attention deficits in ADHD could be elusive5. The most consistent finding points to a dysfunction in executive attention, as part of a more general executive functions impairment that also include WM60 . In this way, administering attention and WM resources seems to be the most typical problem. Therefore, a clear description of how the different WM sub-processes  operate in this population and how they relate to each other (and to attention) seems critical to understand WM deficits in ADHD.Traditionally, poor behavioral performance of ADHD individuals on WM tasks has been explained in terms of a dysfunctional attentional process that impairs proper use of WM resources61. In the present study, we found significant amplitude differences between conditions but no differences between groups. These findings also point against a deficient early visual filtering as a mechanism that could explain attention-WM impairment in ADHD2. On the contrary, the P3 component has been linked to working memory and attention since its earliest descriptions62. P3 amplitude has been suggested to indicate working memory updating32 but also resource allocation63. The amplitude of P3 is known to be affected by attention allocation and, interestingly, a reduced P3 amplitude has been reported in ADHD patients through a wide variety of cognitive tests34.As previously stated, behavioral responses do not allow to discriminate between the different WM stages and their potential contribution to the impairment. ERPs have a high temporal resolution and different components have been described as functional indicators of distinct attention and WM processes. Attention allocation impacts the amplitude of early components of the visual ERP , increasing their amplitude64. Its amplitude has been related with the efficacy of encoding and retrieval66. For example, Friedman and Johnson67 found that items subsequently recognized or remembered elicited larger encoding P3 than those that were later missed. In this line, the decreased P3 amplitude in ADHD would point to a deficient WM encoding process. This way of interpreting P3 amplitude falls within the frame of the “context updating theory” proposed by Donchin and Coles32 which suggested that P3 amplitude reflects the effort to continuously update new relevant information to the representation held in WM. Another view (non-necessarily opposite) suggests that P3 amplitude reflects the allocation of attentional resources necessary to categorize stimuli for encoding and to discriminate its relevance in the retrieval stage64. Although the exact meaning of WM-related P3 amplitude modulations is still a matter of discussion, the correlation between its amplitude and WM efficiency seems reliable. WM representations are flexible and can be modulated dynamically according to changing goals and expectations68, and such process requires dynamic allocation of attention and representation updating which modulates P3 amplitude.In the present study, the encoding and the retrieval periods were characterized by the presence of the P3 like component elicited by the study array and the test array respectively. In both cases these components had larger amplitude in non-ADHD than in ADHD. These WM-related P3 components have been previously reported in several WM tasks69, where larger P3 amplitudes are reported for the old items compared to new ones. It has been suggested that such amplitude modulation reflects activity from a recollection-sensitive regions in the lateral parietal cortex, functionally indexing the representation of recollected information66. Alternatively, in the context of a change detection task, this amplitude modulation could also be interpreted as reflecting a more exhaustive memory search in the “Same” condition until the presence of a change has been ruled out. The latter view is consistent with the notion that correctly detecting change implies recollection while detecting “sameness” or absence of change, involve identifying familiarity70. Taken together the results from the P3 component presented here suggest that both the encoding and retrieval WM processes could be compromised in ADHD.Regarding the retrieval stage, we found larger P3 amplitude for the “Same” condition compared to the “Different” one. These effects are in line with previous results described as the new-old effect in studies of recognition memoryThe amplitude of Encoding and Retrieval P3 components were significantly correlated both in the same and in the different trial types. This correlation was apparently driven by the results from the non-ADHD group. Nevertheless, a follow up analysis comparing correlations coefficients between groups resulted non-discriminative, probably due to limited sample size. Given the sequential nature of the task and the probable participation of attention both during encoding and retrieval the correlations of this neurophysiological indicators was an expected result but needs further confirmation using a larger sample of participants.20, and not the Prefrontal Cortex anterior executive network usually reported to be affected in ADHD71. It should be noted that parietal regions have also been reported to be affected in ADHD72. Nevertheless, metanalytic studies suggest that ADHD impairments in different neural networks are closely related to the tasks or domains being evaluated73. While parietal dysfunctions in ADHD have been mostly related to attentional orienting tasks 73, visuospatial working memory tasks have been mostly correlated with frontal regions dysfunctions74. These findings have important implications for current understanding of the functional organization of WM which we address in the next section. According to our results, binding functions carried out in WM seem to be intact in ADHD. Nevertheless, more specific studies are undoubtfully needed to explore in depth WM binding in this condition71.Another WM process that could potentially account for the impairments seen in ADHD is the binding process itself. That is the construction of an integrative process of objects in WM. In the current work we did not find significant difference between groups regarding the binding condition (Shape-Only Vs. Color-Shape), neither behaviorally nor electrophysiologically. Even though, the Color-Shape condition resulted in larger reaction times in all participants. To our knowledge this is first study to assess WM binding processes in ADHD. The lack of group differences could be explained by that fact that the neural system subserving binding processes has been reported to involve a posterior network of parietal, temporal and occipital areaset al.75. This could potentially impact different subprocesses. First, during the encoding stage reduced P3 amplitude could reflect deficient attention allocation to the relevant aspects when creating a representation. Then, during the retrieval stage the smaller P3 amplitude in ADHD, which also fails to discriminate between “Same” and “Different” (as it occurs in the non-ADHD group) could be interpreted as evidence of a widespread failure to assign post-selection priority to the representations held in memory and their posterior update to solve the task at hand. This seems to be especially clear when the test stimuli were different from the study ones.In this context, our results could be interpreted as a failure in ADHD adolescents to update WM representations to accomplish task demands or a failure in the prioritization of representations as suggested by Myers 19. Thoroughly conducted experiments have manipulated attention during visual WM binding tasks using different approaches and all have failed to demonstrate that binding requires resources above and beyond those needed to process individual features. Baddeley12 envisaged that the episodic buffer was the WM component where such binding operations would occur supported by attention. Should this proposal be valid, any WM operation requiring binding would be dramatically affected if attentional resources are not available during such operation. Clearly, that was not the case in the series of experiments above described. These consistent findings led Baddeley and collaborators16 to revise the WM models and reconsider the function of the episodic buffer. The new revision suggests that low-level binding functions, such as those needed to integrate surface features and form objects identity, can be carried out outside the episodic buffer, being areas in the posterior part of the brain likely neural correlates20. However, a potential limitation of these experiments is that attention was experimentally manipulated making it possible that individual differences in attentional resources would have impacted on such outcomes. A more reliable approach would involve individuals with attentional impairments such as those diagnosed with ADHD. In the current study we addressed this issue in such a population. We have confirmed that individuals with attention impairment, as demonstrated by their clinical profiles and general WM functions, are still able to hold bound information in WM. This is the first study reporting such findings which, to the authors’ views, support the notion that such binding operations could be automatic.This study provides valuable evidence to further assess recent positions regarding the functional organization of WM. A question that has received substantial attention in recent years is whether binding functions operating in WM require additional attentional resources16, other bottom-up low-level attentional functions might support the binding of surface features within integrated objects. Such functions, which are seemingly supported by a posterior network involving parietal, temporal and occipital areas76, might be less vulnerable to conditions impacting on attention such as ADHD or even depression40.A potential account for such relation between attention and binding function of WM has been linked to the type of attention needed to support this function. While executive attention seemingly driven by functions of the prefrontal cortex might be crucial for binding operations happening within the episodic buffer80. Control networks recruited during WM tasks are sensitive to neurodevelopmental factors which affect the patterns of connectivity integration/and segregation81. A significant body of literature suggests that frontal networks, as those sub-serving WM, seem to be affected in ADHD85. This would explain the overall WM impairment seen in these patients in the current study.Recent studies have pointed to a functional integration deficit of connectivity-based pathophysiologic process in ADHD45. This approach reduces but can’t ensure the complete avoidance of implicit verbal rehearsal86. The presence of such strategy was not measure and its potential impact can’t be ruled out. Future studies should consider this in their design to directly address this issue. Second, ADHD is a complex, multisystem and highly heterogenic condition. Although inattention is probably its most consistent characteristic, the presence of the diagnosis cannot be equated to a constant or stable deficit. There is large intra and inter subject’s variability. The inferences regarding the relations between WM, binding and Attention should be further investigated using different experimental designs and larger sample sizes. Third, future studies should separate more systematically attention and working memory and also should address the potential impact of stimulant medication on WM deficits in ADHD, as well as, different ADHD subtypes.The current study has limitations that should be addressed in future studies. First, current and previous studies use no nameable geometric shapes and non-primary colors trying to avoid phonological coding of non-verbal materialThis work opens a new agenda investigating the role of inter-coupling among attention and WM process and networks in ADHD and other neuropsychiatric conditions that impact on these cognitive abilities. In sum, studying the interaction between attentional guided dynamic prioritization and WM in ADHD could be a promising approach to understand the pathophysiology of the condition and to refine understanding of models of memory.


DSM-based ADHD phenotypes; 2) the association of ADHD PRSs with phenotypes beyond ADHD that share its liability and have implications for outcome; and 3) the extent to which youth with high ADHD PRSs manifest a distinctive clinical profile.Genomic discoveries should be investigated in generalizable child psychiatric samples in order to justify and inform studies that will evaluate their use for specific clinical purposes. In youth consecutively referred for neuropsychiatric evaluation, we examined 1) the convergent and discriminant validity of attention-deficit/hyperactivity disorder (ADHD) polygenic risk scores (PRSs) in relation to Participants were 433 youth, ages 7–18 years, from the Longitudinal Study of Genetic Influences on Cognition. We used logistic/linear regression and mixed effects models to examine associations with ADHD-related polygenic variation from the largest ADHD genome-wide association study to date. We replicated key findings in 5,140 adult patients from a local health system biobank.R2 values ∼2%–3%), as well as cross-diagnostic variation in aggression and working memory. In adults, ADHD PRSs were associated with ADHD and phenotypes beyond the condition that have public health implications. Finally, youth with a high ADHD polygenic burden showed a more severe clinical profile than youth with a low burden (β coefficients ∼.2).Among referred youth, ADHD PRSs were associated with ADHD diagnoses, cross-diagnostic ADHD symptoms and academic impairment (odds ratios ∼1.4; Among child and adolescent outpatients, ADHD polygenic risk was associated with ADHD and related phenotypes as well as clinical severity. These results extend the scientific foundation for studies of ADHD polygenic risk in the clinical setting and highlight directions for further research. Given progress in the identification of genetic variants that increase risk for neuropsychiatric illness,Although the pathophysiology underlying these discoveries is not resolved, this polygenic signal is itself of clinical interest as an objective index of ADHD susceptibility. Given a lack of biomarkers, child psychiatric diagnoses are based on caregiver and teacher ratings and behavioral observations. While these information sources show construct validity,et al.17Practically, moving from genetic discoveries to patient care requires intermediate phases of clinical research. As Green To our knowledge, a generalizable child and adolescent clinical sample suitable for translating emerging genomic discoveries has not been available. Thus, we have initiated the Longitudinal Study of Genetic Influences on Cognition (LOGIC), which ascertains youths consecutively referred for a neuropsychiatric evaluation. In this article, we used this cohort to address the following three questions: 1) Do ADHD PRSs show convergent and discriminant validity with key ADHD phenotypes in referred youths with a range of psychopathology? 2) In such a sample, are ADHD PRSs associated with phenotypes beyond ADHD that share its liability and relate to functional outcome? 3) Do outpatients with a high ADHD polygenic burden show a distinctive clinical profile? We also examined the themes of questions 1 and 2 in adults from the same health care system to provide a conceptual replication of key questions in patients and to extend our inquiry to adulthood. Given prior studies, we expected ADHD PRSs to associate with ADHD and related phenotypes as well as to clinical severity in youth psychiatric outpatients. If so, such data would justify and inform further research leveraging ADHD PRSs as objective risk indicators and tools for risk stratification in the child psychiatric setting.Participants were youth who were referred to a neuropsychiatric assessment clinic and who agreed to enroll in research. The clinic, housed within the Psychiatry Department of an academic hospital, provides evaluations to assist with differential diagnosis and/or treatment/educational planning. The study recruits consecutive referrals before evaluation. To enroll, youth must provide their clinical data. They are also asked to provide a DNA sample and supplemental assessments to create a uniform phenotype battery.DSM-IV, Full Scale IQ <55). The inclusion criteria were met by 433 unrelated youth. Their mean age ± SD was 11.5 ± 3.1 years, 62.8% were male, and mean Full Scale IQ ± SD was 100.1 ± 14.5 .The study is ongoing. Here, we report a planned analysis of the first wave of genotyped youth. We included unrelated subjects who were 7–18 years of age; had been genotyped by the time of the analysis; and were of European ancestry (based on genomic data), in line with current best practice given the composition of the discovery GWAS. We excluded (n = 4) youth with moderate to severe intellectual disability  included the following: Child Behavior Checklist (CBCL)/6–18: Attention Deficit Hyperactivity Problems (.84), Aggressive Behavior (.94), Somatic Complaints (.78), and Anxiety Problems (.72); Child Symptom Inventory (CSI)-IVChildren completed psychometric tests, which were administered using published instructions. Full Scale IQ and WM were operationalized via index scores from the Wechsler Intelligence Scale for Children–Fourth EditionNo constraints regarding prior treatment were placed on enrollment, with 59.7% of participants having received prior psychotherapy and/or psychotropic medications. Parent reports  with the strongest relationship to the diagnosis in the discovery meta-analysis, with more relaxed thresholds incorporating increasing numbers of risk-conferring SNPs. PRSs were standardized within the cohort. See DNA was collected via blood venipuncture or by Oragene saliva kits  and was extracted at the Broad Institute of MIT and Harvard. Using the Infinium PsychChip v1.0 Psych array , we genotyped all youths with DNA available at the time. We used standard filters and quality control procedures . We selected the CBCL ADH scale to represent dimensional ADHD symptoms owing to its prior evidence of heritabilityDSM-defined ADHD, we related the ADHD PRS to the CBCL Aggressive Behavior scale and the Wechsler WM index.Regarding convergent validity, we examined the association between ADHD PRSs and 2 test, and an odds ratio (OR) reflected the effect size. For the linear regressions, model significance was determined by an F test and R2 values, and β coefficients reflect effect sizes. For question 1, our Bonferroni-corrected threshold was .0083 . Sensitivity analyses were conducted in all significant analyses by determining whether associations remained after controlling for (broad) ADHD.We used hierarchical logistic regression for the dichotomous ADHD diagnosis and hierarchical linear regression for dimensional traits. In step 1, we controlled for age, sex, and the first five principal ancestry components. For WM and achievement, we also controlled for medication use, given that medications could impact test performance. Because nonstimulant medications can be prescribed for ADHD and because patients are not always aware of the symptoms their medications are intended to address, we controlled for any medication use. In step 2, we entered the ADHD PRS. For the logistic regression, model significance was determined by Wald’s χWe used multivariate mixed modeling to determine whether the profile of scores across key psychopathology domains differed in youths with a low (bottom 30%), medium (middle 40%), and high (top 30%) ADHD polygenic burden. Eight domains  were standardized based on the mean and SD of the current sample. Models sought to determine a main effect for risk group  as well as an interaction between risk group and psychopathology domain .2 (76) = 104.93, p = .02), covariate-dependent missingness in relation to age and sex did yield a satisfying test statistic (χ2 (228) = 213.75, p = .74), indicating that mixed modeling was appropriate with age and sex as covariates.Mixed effects modeling is an extension of regular regression that is appropriate when data are hierarchically structured . The technique does not require the data to be balanced, presuming data are missing at random.p value thresholds). Linkage-disequilibrium pruning of the SNP list was done by applying the clump function from PLINK 1.9, with a 250-kb window and a minimum r2 that was set at 0.2.We used data on patients from the same hospital’s biobank to conceptually replicate and extend our findings. The biobank enrolls adults from the Partners Healthcare system on a continuous basis.We obtained diagnoses and demographics from electronic health records. First, we compared patients with ADHD (ICD-10 code F90) with all other patients. Secondary analyses compared patients with ADHD only with patients with mental, behavioral, and neurodevelopmental disorders (ICD-10 codes F1–F98 excluding F90). Regarding educational attainment, we dichotomized adults who had and had not completed college by age 23 years. This age cutoff was used to capture participants who worked or took a gap year between high school and college. Additionally, we examined ADHD PRSs in relation to the presence or absence of a substance use disorder (SUD) history (ICD-10 codes F10–F19), given associations of SUDs with ADHD liabilityWe used logistic regression to examine whether ADHD PRS was associated with an elevated relative risk for the three outcomes. We controlled for age, sex, the first five ancestry components, and biobank genotyping wave. As in youths, we used permutation testing to generate the null distribution of the test statistic from the sample. We used a Bonferroni-corrected α of .0167 (.05/3 tests). For outcomes other than ADHD, we ran sensitivity analyses controlling for ADHD status.Among referred youth, multinomial logistic regression of the ADHD diagnosis variable  showed that variation in ADHD PRSs distinguished between levels of the diagnosis at eight discovery sample thresholds. These results were driven by significant differences between youth with full diagnoses and no diagnoses of ADHD, whereas only the most stringent threshold differed between full and subthreshold ADHD  . For numerical operations, three significant associations were found . Controlling for ADHD did not reduce the number of significant findings for reading, and one significant threshold remained for math achievement . For WM, three significant associations were found , though at the three most inclusive thresholds. Significant associations remained for aggression at two of the three significant thresholds after controlling for ADHD status, with slightly less explained variance . Associations with WM were unaffected  . No significant differences were found between the medium-risk and low-risk groups.Based on the three most stringent discovery thresholds, youth with a high ADHD polygenic burden, on average, manifested a distinctive clinical profile compared with youths with a medium or low burden (p < 1.0 × 10−4). Although analyses did not yield a significant interaction between domain and risk group, this question may benefit from a larger sample with greater power to detect interactions. Post hoc contrasts suggested that youths with a high ADHD polygenic burden showed impairment on hyperactivity/impulsivity and aggression symptoms rather than all domains .DSM-defined ADHD construct (including WM and aggression in youths and SUDs in adults) that share its liability and have implications for outcome. Finally, youth with a high ADHD polygenic burden manifested a more severe clinical profile compared with youths with a lower burden. These findings justify and inform further studies aiming to leverage ADHD PRSs in child psychiatric practice.This study provides what, to our knowledge, is the first translation of ADHD PRSs to a generalizable child and adolescent outpatient cohort. Results support the convergent and discriminant validity of ADHD PRSs with ADHD-related clinical phenotypes among youths referred for neuropsychiatric evaluation. Conceptual replication in adults confirmed a relationship with clinical ADHD diagnoses even versus other neuropsychiatric conditions. In youths and adults, ADHD PRSs were also associated with phenotypes beyond the ADHD PRSs in these analyses reflect the common genetic variation that collectively distinguished ADHD cases from non-ADHD controls in a GWAS of approximately 55,000 individuals.40Analyses in adults conceptually replicated these findings. Our adult sample derived from the same catchment area as our youth cohort and similarly examined real-world clinical diagnoses, which are relevant to clinical translation. In adult patients, ADHD PRSs were associated with having an ADHD diagnosis, even versus having another neuropsychiatric condition. Results in adults also substantiated the relationship between ADHD PRSs and educational impairment, extending implications beyond grade school achievement to educational attainment in adulthood. Finally, in adults, ADHD PRSs were associated with SUD, which typically has onset later than the age of our youth sample and was not present in that cohort but which has a putative relationship to ADHD risk.DSM diagnoses is not necessary. Eventually, risk scores are expected to be refined to reflect more specific biological processes with implications for treatment, and these may link to a range of potential outcomes.DSM-based GWASs represent a useful anchor, as major categories of illness will likely contribute to the organizing principles of this evolving framework, given some specificity in the genomics and psychopharmacologic literatures.et al.For genomic information to have clinical utility as an objective risk indicator, precise tracking with Finally, our data extend the empirical justification for evaluating ADHD PRSs as tools for clinical risk stratification. In the literature, impaired executive cognition has been associated with academic difficultiesTo confirm the potential value of genomic data for this purpose, studies will need to document the longitudinal outcomes of referred youth with high ADHD polygenic burden and clarify the age of penetrance and potential mediators of associated phenotypes. For example, though ADHD onsets primarily in youth, prevention and intervention strategies will depend on whether being at high risk is associated with early and severe ADHD symptoms versus their gradual unfolding. Moreover, the fact that the majority of associations to phenotypes beyond ADHD remained after controlling for the diagnosis suggests that simply treating ADHD will not be sufficient for improving outcomes in high-risk youth. Rather, genetically informed prevention/early intervention programs that address specific outcomes should be considered, and such efforts must be appropriately timed. For instance, cost-effective targeting of high-risk youth for educational supports and substance prevention programs could be beneficial, but longitudinal confirmation of our data would suggest that increased academic support is needed during an earlier developmental window.Limitations of our study should be acknowledged. First, the trade-off for collecting a large clinical cohort in a cost-efficient manner was a lack of structured diagnostic interviews. Yet, several factors support the integrity of our diagnoses. Our youth clinic is a training site at a teaching hospital where attention is given to differential diagnosis. Furthermore, κ values from blinded ratings showed high interclinician agreement, and we validated the ADHD diagnosis, once made, with structured interviews in a subset of patients. Furthermore, clinical diagnoses, though imprecise, are the criterion most relevant to clinical translation,et al.et al.Second, we cannot rule out the possibility that medication masked some variation in symptoms and test performance, even after adjusting for covariates. Whereas a medication-naïve sample would have been preferable, we did not limit enrollment to preserve the generalizability of findings. Similarly, we note that slightly more than half of the youth in our sample had previously received treatment via psychotherapy or medication. A source clinic such as ours, which provides comprehensive neuropsychiatric evaluations for purposes of differential diagnosis and school and treatment planning, represents a setting in which genomic data would be highly relevant. Thus, we included all consecutive referrals to the clinic, regardless of treatment history. Third, we used ASD PRS to address specificity/discriminant validity because it related to a childhood-onset condition for which most of our youth have passed through the age of risk. Nonetheless, the discovery GWAS for ASD is small, and associations between ASD PRS and ADHD in our sample should be revisited when the size of that GWAS increases. Fourth, as discussed, we may have lacked statistical power to detect interactions in our mixed effects models and thus cannot speak to whether a high polygenic burden is characterized by greater symptoms in specific psychopathology domains versus greater symptoms more generally. Finally, we note that the current analyses were limited to participants of European ancestries. Translation to more racially and ethnically representative samples is critical and will benefit from both statistical advances (eg, see Seldin Despite these issues, our results provide novel evidence that ADHD PRSs are relevant to phenotypes that reflect the broad ADHD construct among child psychiatric outpatients. Additionally, a relatively high PRS burden in outpatients is associated with greater clinical severity. It has long been hoped that advances in genetics would have a positive impact on child psychiatry,


Evidence suggests appearance of socioeconomic gradient in the probability of low birth weight (LBW). Such evidence, however, is scanty in Ethiopia. The study aimed to examine the prevalence of and socioeconomic gradient in LBW in Ethiopia.Data for the study were drawn from the Ethiopia Demographic and Health Survey conducted in 2016. The 2016 EDHS is the fourth wave in the series of nationally representative household surveys carried out in the country to deliver up-to-date health and demographic indicators for the Ethiopian population. Women aged 15 to 49 years were the main focus of the survey, with data also gathered from men aged 15 to 59 years and under five children. The study pursued complex sampling strategy to draw samples representative at national as well as at urban and rural levels. The data are available to the public domain and were accessed from the MEASURE DHS following registration. Multivariable logistic regression model and marginal standardization were used to estimate socioeconomic gradient in the probability of LBW. We performed sensitivity analysis to evaluate variation of LBW according to different categories of socioeconomic position. Maternal education and household wealth were used as measures of the socioeconomic position in the study.13.2%  of births were complicated by LBW. The findings showed that socioeconomic gradient was evident between maternal education and LBW; as education increases from no education to secondary education, the probability of occurrence of LBW consistently declined. However, no gradient in LBW was detected for household wealth.We have identified education gradient in LBW, with the highest burden of LBW occurring among the non-educated women. To redress the observed education disparity in LBW, targeted interventions need to be implemented with greater emphasis placed on illiterate women. An estimated 15 million babies are born preterm (born before 37 completed weeks of gestation) annually worldwide . Not onlAccording to the World Health organization (WHO), LBW is defined as weight at birth of less than 2500 g . AccordiLBW remains a substantial public health challenge worldwide  as it amDespite LBW being a pervasive problem worldwide, highest prevalence is reported in the poverty stricken areas of the world, Sub-Saharan Africa and Southern Asia , suggestThis paper has been prepared using data derived from the most recent wave of the Ethiopia Demographic and Health survey conducted in 2016 (2016 EDHS). The 2016 EDHS is the fourth round in the series of nationally representative cross-sectional surveys after the 2000, 2005 and 2011 EDHSs. The dataset were accessed from the MEASURE DHS and children’s file has been used for this study. Implementation of the survey was carried out under the auspices of the Central Statistical Agency (CSA) of Ethiopia, with ICF provided technical aid. The United States Agency for International Development (USAID) and other international organizations delivered financial support for the survey. The survey covered all nine regions and two city administrations in the country to produce data representative nationally as well as for urban and rural settings and nine subnational regions and two city administrations. Conduct of the survey was motivated by the need to provide decision makers with up-to-date evidence on various public health indicators such as child morbidity and mortality, maternal health services, fertility preference and domestic violence, to mention just a few.The sampling procedure and methodology followed in the survey is available elsewhere . The surThe 2016 survey used five questionnaires to capture information on a wide range of health issues: household, women’s, men’s, biomarker and health facility questionnaire. Since women in the reproductive age group remain the major focus of the survey, women’s questionnaire was the principal source of data and was applied to gather data on issues specific to women and their children such as maternity care, family planning, infant feeding practices, vaccinations, childhood illness and low birth weight. The questionnaires were adapted from standard DHS questionnaires to better reflect the contexts and requirements of the country..The outcome variable for this study is LBW. In the survey, birth weight information was collected for children who were born 5 years prior to the interview date. In total, there were 10,641 births born 5 years preceding the survey. However, birth weight information was available for 2110 births only, comprising nearly 14% (weighted) of the sampled births in the survey. In the questionnaire used to collect the DHS data, women were asked two questions that help to collect information on LBW: 1) was (NAME) weighed at birth? and 2) How much did (NAME) weigh?. For the first question, she replied either “yes” or “no”. Women who replied “yes” were then asked to answer the second question, where there are three choices about birth weight information: weight from medical record, from mothers recall and “do not know”. So, in this study, information on birth weight was based on either written records from medical charts or recall of the mother. LBW was used as a binary variable where 1 represents children born low weight at birth (less than 2500 g) and 0 if the child weigh 2500 g or more. Multiple pregnancies have not been an issue for the study since about 97% of the babies sampled and analyzed in the study were singletonWealth index and educational status of the mother were used as the SES measures for this analysis. Wealth index is a composite summary measure intended to reflect the economic rank of a household and is produced using a statistical procedure called Principal Component Analysis (PCA) , 31. It To measure the net influence on LBW of the socioeconomic variables, we controlled for the influence on LBW of other factors in our statistical model. Such other factors are known as confounding factors. A confounding variable associates with both a cause (SES in this study) and response variable (LBW in this study). The choice of the potential confounding variables for our study was informed by literature. Mother’s age at birth of child, ethnicity, and maternal occupation , 33, 34 P-value < 0.05. Further, marginal standardization was performed to find out predictive probability of LBW across the entire subgroups of the SES. It has been shown that this approach is suitable method to reliably estimate predictive probabilities after fitting cofounder adjusted logistic regression [We examined the socioeconomic gradient involved in LBW using regression model. We run multivariable logistic regression to produce results that are statistically significant net of effect of confounding variables discussed above. Statistical significance was measured through Odds Ratio (OR), 95% confidence interval (CI) and gression . The SESgression . Since d. Table A total of 2110 live births in the 5 years before the survey were included in the analysisTable p-value> 0.05 for each of the four categories compared to the poorest category). This is supported by the probability curve depicted in Fig. p-value = 0.005). This finding is also evident in the probability curve and results shown in Fig. Table p-value = 0.009) and highest (17%) among ‘poorer’ mothers . The probability of having a LBW baby was highest (18%) among mothers with no history of formal education, followed by 10.6 and 8.4% respectively among primary and secondary complete mothers, resulted in graded association until secondary education, as indicated by the steady decline in LBW probability as we move down from no education to secondary education. However, we did not find wealth gradient in LBW. See Table Holding all variables in the logistic regression model at their observed values, the probability of delivering a LBW baby was lowest (10.5%) among mothers in the poorest quintile . In support of findings obtained from the regression model above, we found significant pp changes in LBW probability in relation to maternal education. If all mothers who currently fell into the ‘no education’ group moved to primary education group, the probability of giving birth to LBW baby decreased by 7.5 pp .Table The study attempted to investigate the socioeconomic gradient involved in the occurrence of probability of LBW using the nationally representative data extracted from the 2016 EDHS. We showed that, one in approximately eight babies was born with LBW, making it one of the major public health problems to deal with.Given its prominent role in morbidity and mortality of children, LBW remains a big obstacle to attain the neonatal mortality rate target set out for the 2030 international goals . From thThe WHO has called the member states to slash LBW prevalence by 30% by 2025 . AccordiInteresting findings emerge with respect to social gradients of LBW. The study revealed maternal education gradient in LBW in the confounder adjusted regression model. The odds of LBW were highest among illiterate, followed by ‘primary’ and ‘secondary’ education subgroups. The odds of LBW among mothers who completed primary and secondary education, respectively, were nearly 50 and 60% lower than among women without formal education, suggesting pattern of increasing returns (in terms of decreasing LBW) until secondary education, after which no reduction in LBW was observed. The pattern we found in the present study looks roughly like curvilinear graph typically found in studies involving SES gradient for health where a decreasing health gain was observed after a certain SES threshold . AlthougOur sensitivity analysis on maternal education gradient for LBW supported the result we obtained from the regression analysis. The probability of occurrence of LBW varies with subcategory of mother’s education. Holding illiterate women as a reference category, the probability of LBW significantly dropped as we move down to the ‘primary’ education level from the ‘illiterate’ subgroup. Similarly, an even significantly higher drop in LBW was observed as the educational status of the mother changes from ‘no education’ to ‘secondary’ education Table . This grHowever, given substantial inconsistencies in the literature around this issue, more context specific studies are still needed to provide conclusive evidence on the nature of relationship between SES and LBW. In contrast to findings elsewhere , 24, 28,The study has a few limitations. Wealth index was computed based on the current (at the time the survey was conducted) household possessions and durable materials, but mothers were asked to provide information on past histories, i.e., LBW incidence over the past 5 years prior to the survey. Over the 5 years period, however, household wealth level may have changed and conclusions drawn might likewise be biased. We also did not differentiate the mechanisms that lead to LBW and to intra uterine growth restriction owing to absence of data on gestational age in the EDHS.Our observed findings stem from cross sectional study where we cannot figure out the cause-effect relationship between education and LBW. Since our analyses were confined to 14% of the birth sample in the survey, findings should be interpreted with this limitation in mind. However, although the sample we base our analysis on was small compared with the total live birth observations in the DHS data, the sample is not necessarily too small to be used for research. In a resource limited countries like Ethiopia, DHS is the main source of birth weight information, where weight is either not properly recorded in the child’s medical card or mothers may not recall it. This results in small samples with complete birth weight information. More than half of the birth weight sample analyzed came from the richest wealth quintile; the greater representation of participants from well to do family might have affected our findings. However, women were invited to provide response on birth weight if they recalled their babies’ birth weight, and their participation was not dependent on whether they were poor or rich. Therefore, it is likely to be by chance that most women in our analysis were from the richest subgroup. Finally, accuracy of birth weight reports based on mother’s estimate is contingent on her recall capability, and the findings presented in this paper should be taken with caution. However, only those birth weight responses the mother recalled were included in the analysis and the risk that mothers could report on birth weight that they did not remember could be minimal.Important policy and research relevant findings stem from the study. Child health interventions in Ethiopia need to take account of the appearance of maternal education gradient in LBW to effectively combat the problem. Since the probability of giving birth to LBW neonate occur among illiterate, primary and secondary complete women differently in decreasing order, interventions need to be designed with this disparity in mind in order to substantially reduce the national burden of the problem. That means, women who are ‘illiterate’ need to be policy-targeted the most, followed by ‘primary’ and ‘secondary’ complete mothers. The existence of a statistically significant maternal educational gradient in LBW helps to propose the idea that a targeted approach would be an effective policy choice to maximize gains against LBW; with the ‘whole population’ approach still functioning to disseminate interventions to the entire female population. However, the fact that our findings being drawn from cross sectional survey may affect usefulness for practice and users of the information contained in the paper and therefore the findings should be used along with other sources on this same topic. We highlight that our evidence was drawn from a relatively small samples and the small sample effect might be there. Finally, our findings would be benefited much from future studies to unpack socioeconomic gradient in LBW using data collected objectively.


However, the statistical analyses therein raised several questions. First, ecological variables are not justified enough to adjust potential confounding. Second, the spatiotemporal regression model does not consider temporal reduction in radiation dose rate. Third, dose-response plot between dose rates and odds ratios overestimates R Dear EditorRegarding with a recent study on Cs-137 exposure with low birth weight (LBW) in infants in Japan by Scherb and Hayashi [Firstly, LBW can be influenced by various individual factors such as body weight change of mothers during pregnancy, smoking, etc. The authors included spatiotemporal effects in logistic regression model (but details of modeling were not provided), and analyzed data without individual factors. Authors included ecological variables, but they are not enough to control individual variations for LBW. How can annual population, number of physicians adjust the potential risk factors for LBW? In addition, if triple disasters affected those background factors, OR jump could be observed. Hence, individual risk factors are essential for the analysis because the observed OR for Cs-137 is relatively small and easily influenced by confounding.Secondly, Figure 4 of the article claimed ionizing radiation increased the prevalence of LBW. The logistic model assumed the level-shift after 2011 persists to 2018 at same magnitudes. According to previously study [2 and underestimation of p value of the regression in Figure 5 of the article. Authors mixed data of 37 prefectures into one point, and made variations of them small. As shown in Tables 2 and 3 of the article, even in non-contaminated regions showed high odds ratios over 1.1. Furthermore, authors analyzed them with restricted linear regression model (intercept was fixed to 1), but we examined them in unrestricted regression that showed R2 of 0.37 and p = 0.046. The R2 was further decreased to 0.11 in the analysis of original 47 records (Fig. 2. In addition, there was no rationale to employ restricted linear regression analysis.Thirdly, we found an overestimation of RAccording to explanations above, we suggest that authors will clarify the assumptions and rationale in the study.


This study assesses whether low birthweight/preterm (LBW/PT) adolescents with persistent inattention (PIA) have neuropsychological deficits that distinguish them from adolescents with school age limited inattention (SAL) and those largely unaffected (UA).Three latent classes , derived from an earlier analysis of a LBW/PT birth cohort were compared on non-executive and executive functioning measures assessed at age 16.The PIA class displayed the poorest performance on executive functioning, which was exaggerated in the context of lower IQ. The PIA and the SAL classes had poorer performance on non-executive functioning relative to the UA class. Both types of functioning mediated the relationship of class to school service use and grade retention.Neuropsychological impairment characterizes children and adolescents with inattention problems. Problems in executive functioning characterize the subset whose inattention persists through adolescence. Subsequent research can examine the potential for remediating these deficits to address academic and social problems. Attention Deficit Hyperactivity Disorder (ADHD), a clinical diagnosis typically first made in childhood, is defined as the presence of a minimum number of inattentive (IA) and hyperactive-impulsive , accompanied by cross-situational impairment. ADHD is a public health concern: it affects 9% of school-age children , 2, and This heterogeneity in the presentation and trajectory of IA and HI behaviors has been a major challenge for the development of treatments for ADHD. Until now, much effort has focused on medications (primarily stimulants) and classroom and parenting management techniques that target ADHD behaviors–particularly HI behaviors. These have demonstrable short-term benefits –10 but qPrevious research suggests that deficits in neuropsychological functions are present for children with ADHD , 25 and The present study leverages information from a regional birth cohort of low birthweight/preterm infants assessed for individual ADHD behaviors at ages 6, 9 and 16. Previous research on this cohort has found the presentation and trajectory of their ADHD behaviors to be similar to those described for general population and term samples . Latent The analyses presented here examine non-executive and executive functions in the aforementioned regional birth cohort. Our objective is to: (1) describe the neuropsychological profiles of each of the three latent classes  in terms of executive  and non-executive  functions; (2) determine the similarities and differences between the SAL and PIA groups in terms of non-executive and executive functions; (3) examine the extent to which IQ moderates the effect of inattention latent class profiles on executive functions; and (4) examine whether differences in performance on non-executive and executive across the three groups explain the elevated rates of educational difficulties in the PIA and SAL classes.The Neonatal Brain Hemorrhage Study (NBHS) birth cohort (n = 1105) included 90% of births <1500 grams and 85% of births <2000 grams in three New Jersey counties. The counties were demographically representative of the US as a whole , 31, froThe study sample consists of the 387 participants for whom data from all three psychiatric assessment points  was available, and who did not have a major disability age 16 . Earlier latent class analysis of ADHD criteria from the three assessments resulted in the PIA (n = 66), SAL (n = 150), and UA (n = 171) classes.ADHD criteria were assessed using the Diagnostic Interview Schedule for Children (DISC) parent version . The agesustained attention: Integrated Continuous Performance Test (IVA), visual and auditory Attention Quotients (AQ) and Hyperactivity Scale; visual memory: WMS-III Visual Immediate and Delayed Memory; and auditory memory: Wechsler Memory Scale–Third Edition (WMS-III), Auditory Immediate and Delayed Memory. The executive functions include measures of working memory: WMS-III Working Memory Index; cognitive flexibility: Trail Making Test- Part B (TMT-B); impulsivity: IVA, visual and auditory Response Control Quotients (RCQ); inhibition: the Stroop Color Word Interference Test (Stroop Interference); and selective attention: Test of Everyday Attention for Children (TEA-Ch), Map Mission subtest. The non-executive functions measures include tests of Intellectual function of LBW/PT status has the potential to be modify the relationship between ADHD symptoms patterns over time and our executive functions measures . For thiWe examine the extent to which differences in neuropsychological performance mediate the relationship between the latent classes and indicators of educational difficulties, including use of special education services and repeating a grade. Association of these outcomes with the latent classes was documented in earlier analyses .F) of the non-executive and executive measures. When warranted, post-hoc pairwise comparisons were conducted. The Type I error rate was set at 0.05 throughout. To account for multiple testing, we also report the Bonferroni correction for the analyses of neuropsychological measures .The main analyses for this study examined the associations of the latent classes with indicators of non-executive and executive functions at 16. The inferential statistics for these analyses involved omnibus tests of latent class differences in mean level . The rates of impairment in each of the domains was compared across the three latent classes using chi-squared tests.In line with previous research  demonstrTo assess the role of neuropsychological impairment in mediating the association of the latent classes to educational outcomes, a set of logistic regression models regressed lifetime use of school services and ever repeating a grade on the latent classes and, subsequently, the executive and non-executive functioning tests, first in separate blocks and then together.The statistical models are adjusted for the child’s sex, gestational age at birth, and small for gestational age. The adjusted models are presented in the In addition to mean differences by latent classes on the neuropsychological test scores, we also examined differences in rates of neuropsychological impairment (defined as two standards deviations or greater from the mean) across the classes . The PIAFor the TMT-B z-score and the WMS-III Working Memory tests, lower IQ greatly exacerbated the association between the PIA and the SAL classes and study outcomes, relative to the relationships within the other two IQ categories . These iThe picture is somewhat different for repeating a grade. For this outcome the mediating role of the neuropsychiatric tests is substantial for the PIA group–particularly with respect to the executive functioning tests. Nearly 80% of the elevation for the PIA group is mediated by executive functioning, all of the tests together mediate 87%. In contrast, the mediation effects for the SAL group are quite small for both types of test. As shown in The analyses in this paper employed one of few extant datasets having longitudinal data on DSM ADHD criteria, as well as clinical characteristics and functional outcomes, in a prospective LBW/PT birth cohort , 30. We Consistent with a few other studies , 28, we Our findings are broadly consistent with the Halperin and Schulz neurodevelopmental model of ADHD recovery . Both laThe fact that the SAL class has far fewer deficits in executive functions—which rely on late developing cortical brain regions such as the prefrontal cortex–could be in-line with three alternative perspectives. First, remittance of inattention problems could support the improvement of these executive function abilities in adolescence. Second, the emergence of executive function abilities could enable adolescents to overcome their inattention behaviors. Finally, the rapid development of the prefrontal cortex that typical occurs in adolescence  enhancesWe further examined executive and non-executive functioning as potential mediators of elevated rates of educational problems in the PIA and SAL classes. Neuropsychological functioning, in particular executive functioning, is a stronger mediator of the elevated rates educational problems in the PIA class. The two domains of functioning have similar mediation effects for the SAL class and, in combination, are not as strong as the mediation for PIA. Indeed, elevated rate of repeating a grade in the SAL class is largely unchanged when functioning is included in the model. This may be because members of the SAL class are likely to have repeated a grade earlier in childhood during the period when they had high levels of inattention. If this were the case, their (likely improved) neuropsychological functioning are age 16 would be less strongly associated with that outcome.This study advances our understanding of the neuropsychological profiles of children with differing trajectories of inattention problems through childhood and adolescence. Nonetheless, it should be noted that the classes of subjects identified in this study may have been different had children with full-term gestation and typical birth weight been included. Further, the associations of ADHD symptom patterns with cognitive functioning measures might not be generalizable to the general population. It should also be noted that data on other deficits that have been associated with inattention problems, such as deficits in planning , were noThe current study expands the existing literature aiming to differentiate children with varying durations of inattention problems throughout adolescence. Using a unique LBW/PT sample, enriched for the occurrence of these problems , 44, we S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6–12 years . We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score , as well as four control variables , provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders Barkley, , with anBecause early behavioral and developmental interventions for ADHD could improve outcomes , which are clinically judged and therefore are subjective to clinician and reporter’s bias  is one of the most popular objective measures of ADHD-related inattention and impulsivity  is a rapidly emerging field that has allowed the exploitation of large datasets to generate predictive models. In “supervised learning,” machines develop ways of linking a target outcome from a set of predictors (“features”) in existing data. Such models may generalize to novel predictor data. In contrast to traditional statistical approaches, ML focuses on prediction rather than explanation . However, the rate of boys was significantly higher in the ADHD group than in the non-ADHD group .Participants were 458 children aged 6–12 years , 267 were boys (59%) and 191 girls (41%). Of them, 213 children were diagnosed with ADHD, and 245 were typically developed, children. No age differences were found between the ADHD and the non-ADHD groups  the child scored below the clinical cut off point for ADHD symptoms on ADHD DSM Scales  of Hadassah-Hebrew University Medical Center Jerusalem, Israel. Participants were not compensated for their participation in the study.CPT performance—the current study used the MOXO-CPTThe test consisted of eight stages (levels). Each level consisted of 53 trials (33 target and 20 non-target stimuli) and lasted 114.15 s. The total duration of the test was 15.2 min. In each trial, a stimulus (target or non-target) was presented in the middle of the computer screen for durations of 0.5, 1, or 3 s and was followed by a “void” of the same duration . This meBoth target and non-target stimuli were cartoon pictures free of letters or numbers. Also, the test included six different environmental distractors, each of them could appear as pure visual , pure auditory , or as a combination of visual and auditory stimuli (birds moving their wings and singing simultaneously). Each distractor was presented on the screen for a different duration ranging from 3.5 to 14.8 s, with a constant interval of 0.5 s between two distractors.For each child, four CPT indices were recorded: attention , Timeliness (correct responses to target stimuli conducted on accurate timing), Hyperactivity (a measure of motor activity) and Impulsiveness . Test administration time, namely, the day of the week (DoW) and time of day (ToD), was also recorded. A full description of the MOXO-CPT test is provided in Data analysis included four stages: (a) data exploration and processing; (b) data partition and manipulation of training; (c) model fitting; and (d) ADHD prediction and evaluation.(1)Data exploration and processing—following the descriptive data analysis outlined in the method section, two sources of inherent biases were evident, which we denoted: across the level and within level imbalances.F = 8.26, p < 0.01).Across level imbalances—demographic and test time variables were imbalanced. For example, the total number of children that performed the MOXO-CPT during Mondays was significantly higher than the number of children that performed the test during any other DoW. The lowest number of tests was conducted on Saturdays. Imbalanced designs concerning the number of observations per level were shown to be highly susceptible to statistical biases, such as heteroscedasticity . Moreover, for some factor levels, such as DoW and ToD, the data included only children from one diagnostic group, but not the other; administration of the MOXO-CPT during the weekend was evident only among the non-ADHD group. Also, all children in the ADHD group (with one exception) performed the MOXO-CPT during the morning hours.Within level imbalance—diagnostic class within demographic and test time levels were also shown to be imbalanced. For example, the ADHD group significantly differed from the non-ADHD group in their gender distribution, with more boys in the ADHD group than in the non-ADHD group , and the fraction of children with ADHD i(ADHD)p . Groups in which the fraction of children with ADHD equaled to either 0 or 1, were removed from the dataset, as the impact ADHD on test performance within these groups was unquantifiable. The processed data contained 445 observations (97% of the unprocessed data).To quantify the statistical biases in our data, we conducted a retrospective factorial fitting Data partition and manipulation of training—as customary in predictive analysis Shmueli, , we partÑ = 5,000) using bootstrap sampling with repetition, following the principles of the Synthetic Minority Oversampling Technique (SMOTE). SMOTE algorithm is a kind of random oversampling algorithm which generates new synthetic samples by analyzing neighbors of minority samples  and diagnosis [1 − pi(ADHD)] for children with ADHD, and i(ADHD)p for children without ADHD. This sampling procedure generated a training data set that was: (1) synthetically large enough to allow the use of robust ML techniques that operate on large amounts of data; and (2) balanced within- and across- levels, thus was free of statistical biases. The holdout set remained untouched, to allow a fair evaluation of the prediction model.We manipulated the training set to correct the inherent biases. Specifically, we bootstrapped the inflated number of training records ((3)f to the training set that mapped all or a subset of the test performance measures  and additional control variables  to the diagnosis class:Model fitting—in this stage, we trained an ML prediction model f in our analysis is either a random forest ADHD prediction and evaluation—in this stage, we used the training model to predict the detection of ADHD in holdout records. That is, we computed the accuracy, sensitivity, and specificity of our ML model comparing to clinical diagnosis. We repeated steps 3 and 4  100 times, generating a different training-validation random split as each repetition, to compute conference intervals for the accuracy measures. The detection procedure is summarized in We examined different subsets of MOXO-CPT performance indices  and control variables  as ADHD predictors in children who were diagnosed with ADHD, using the “gold standard” clinical criteria. The gold standard of ADHD diagnosis was based on the DSM-V criteria for ADHD  on a training set derived from the unprocessed  data. Since the unprocessed data contained inherent statistical biases caused by level-imbalance (within and across), it was impossible to use the control variables as reliable predictors in the benchmark model.As a benchmark, we trained function t-tests analyses for paired samples revealed that the differences between the benchmark model and the proposed models (in both random forest and NN techniques) were significant (p < 0.05). However, no significant differences were found between the results of the random forest technique and the NN technique (p > 0.05). As seen in the table, NN provides, on average, higher rates of accuracy and sensitivity to ADHD, compared to the random forest technique. However, the confidence intervals in the NN model are higher, indicating non-robust cases and higher risk prediction errors. Given the increased robustness of the random forest technique and the fact that it was not significantly different from NN in all observed measures , it was chosen as the preferred method.The results of the random forest and the Neural Network (NN) techniques are presented in As shown in the table, the best performance of the random forest technique  was achieved by applying the proposed method to predict ADHD, using the four CPT performance indices and four controls as predictors. This performance was 34% higher than the best-achieved accuracy of the benchmarkAs an alternative to the SMOTE rebalancing technique, we also examined the impact of the under-sampling technique to balance between the control and the ADHD groups. This rebalancing technique revealed an overall accuracy of 73% (%95 CI = 63 to 83%), a sensitivity rate of 79% (%95 CI = 57 to 100%), and a specificity rate of 66% (%95 CI = 42 to 90%). As seen, there are considerable differences between the results of the two rebalancing techniques. While both techniques addressed the four potentially confounding variables  in the same manner , they differ in their ability to capture information about the majority class. Specifically, the disadvantage of the under-sampling technique is that removing modules may cause the training data to lose important information related to the majority class. The SMOTE technique was proposed to combat this disadvantage by creating artificial data based on the feature space (rather than the data space) similarities from the minority modules , and all four control variables , provided the most salient information for discriminating between children with ADHD and their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. Using this model increased the performance by 34% compared to the benchmark approach.The improvement in the model’s prediction accuracy after quantifying for cross-level and within-level imbalances suggests that such statistical biases may affect the discriminative validity of the CPT. These findings are in line with previous research that pointed to the influence of age  may improve clinicians’ ability and confidence in ruling out ADHD. Excluding ADHD when it is not present is very important given the complicated, time-consuming, and expensive process of ADHD diagnosis . Future ML research in ADHD prediction should expand this examination to include a broader range of clinical, behavioral, and demographic variables, including education, ethnicity, socio-economic status, psychiatric-co-morbidities, and medication use. Finally, while the predictive accuracy of ML models can be satisfactory, a naive implementation of ML without careful validation may have adverse consequences. An ML algorithm may replicate past decisions, including biases around ethnicity and gender, that may have affected the clinical judgment. Therefore, model extrapolation should be avoided until such biases are corrected  of Hadassah Hebrew University Medical Center, Jerusalem, Israel. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.IB recruited the patients and performed clinical evaluations. IY performed data analyses. OS, IY, and IB contributed equally to the development of study design, integration of findings, and writing the manuscript.OS and IB have previously served on the scientific advisory board of NeuroTech Solutions Limited.The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.