Attention-deficit/hyperactivity disorder (ADHD), characterized by age-
inappropriate inattention, impulsiveness, andhyperactivity, is one of the most common neurodevelopmental disorders, affecting 5-11% of school-aged children (Centers for Disease Control and Prevention, 2013). On average, ADHD is also associated with impairments in executive functions (Barkley, 1997), however there is considerable heterogeneity of such deficits (Willcutt et al., 2005; Castellanos et al., 2006). While, numerous functional neuroimaging studies in children and adults have revealed altered patterns of activation in ADHD during the performance of tasks of executive function (reviewed in Cortese et al., 2012; Hart et al., 2013), these studies examined ADHD patients who currently met diagnostic criteria for ADHD. The goal of the present study was to discover whether there are neurobiological differences between adults who persist with an ADHD diagnosis from childhood into adulthood (persistent ADHD) versus adults who had an ADHD diagnosis in childhood but no longer meet diagnostic criteria as adults (remitted ADHD). We investigated the possible distinction between persistence and remittance in ADHD by comparing brain functions among three longitudinally followed groups: (A) Patients with Persistent ADHD diagnoses in both childhood and adulthood; (B) Patients with Remitted ADHD who had met the diagnoses in childhood but no longer met that diagnosis in adulthood; and (C) Control participants documented as not having ADHD in either childhood or adulthood.
Attention-Deficit/Hyperactivity Disorder (ADHD) is highly prevalent in children and adolescents worldwide. Typical symptoms include inattention, impulsivity andhyperactivity. Comorbidity with other disorders, such as Oppositional-Defiant Disorder (ODD) or Conduct Disorder (CD) are very common. Due to child symptoms, families of children with ADHD are prone to impaired family functioning. This includes strained parent-child relationships, higher conflict, more negative emotional experiences and less positive family interactions (Johnston & Mash, 2001; Whalen et al., 2011). Based on the conjecture of physiological under-arousal in children with ADHD (Barkley, 1997), the physiology of individuals with ADHD became a biopsychological research interest. The most frequently investigated biomarker is cortisol, an end product of the Hypothalamus-Pituitary-Adrenal (HPA)-axis, and a principal actor in the bodily stress response. Results regarding diurnal HPA-axis functioning in children with ADHD are largely inconsistent. While some found an effect of ADHD on diurnal cortisol, an equally large body of research did not replicate the claimed difference to non-affected individuals. Again others suggest that differences in HPA-axis functioning are solely related to disruptive comorbid symptoms. HPA-axis functioning is health-relevant and calibrated throughout childhood and adolescence (Boyce & Ellis, 2005; Del Giudice et al., 2011). The axis reacts to everyday experiences (Belsky & Pluess, 2009) and the present thesis aims to inquire, how child symptoms as well as family interactions are connected to cortisol expression.
For Peer Review INTRODUCTION
AttentionDeficit/Hyperactivity Disorder (ADHD) is a common and impairing behavioral condition defined by developmentally inadequate levels of inattention andhyperactivity-impulsivity. Whether or not emotional symptoms should be included in the core ADHD features has been largely debated. Although this option was not chosen in the latest nosographic classification (DSM-5), the relevance of the emotional dimension of ADHD remains the focus of active scientific/clinical research. 25 to 45% of youths with ADHD display disproportionately high levels of emotional symptoms (Shaw, Stringaris, Nigg, & Leibenluft, 2014), which encompass a broad range of clinical features including emotional lability, impulsivity and dysregulation, and the concept of irritability (Faraone et al., 2019; Shaw et al., 2014; Vidal-Ribas, Brotman, Valdivieso, Leibenluft, & Stringaris, 2016). From the clinician’s perspective, irritability represents the most relevant emotional symptom to address. First, irritability is related to frequent requests for help due to the significant burden for the youth and the family. Second, it is clearly defined in the DSM-5 (American Psychiatric Association, 2013), which clarifies its clinical features and makes inter-rater reliability more accurate. Notably, irritability is characterized by frequent temper outbursts typically occurring in response to
Dyslexia andAttentionDeficit/Hyperactivity Disorder (AD) are prevalent neurodevelopmental disorders that are characterized by primary diagnostic symptoms, as well as various secondary cognitive dysfunctions (Pennington, 2006; Willcutt et al., 2010; Willcutt & Pennington, 2000). Research efforts to date have focused on explaining the mechanisms underlying both the primary symptoms associated with each disorder as well as the secondary impairments. For instance, the phonological deficit hypothesis suggests that the primary deficit in dyslexia is a lack of phonological awareness, which has been shown to be a reliable diagnostic marker of the disorder (Snowling, 2001; Vellutino, Fletcher, Snowling, & Scanlon, 2004). It has further been posited that this primary phonological decoding deficit can also explain the presence of secondary impairments, such as working memory problems, through an aberrant phonological loop and diminished verbal span (Ramus, 2001; Wilson & Lesaux, 2001). The mechanisms underlying AD may be harder to identify because of the heterogeneous nature of the disorder (Roberts, Martel, & Nigg, 2013), however, a core frontal lobe dysfunction has been proposed to explain the surfeit of accompanying cognitive difficulties (Dickstein, Bannon, Castellanos, & Milham, 2006; Shue & Douglas, 1992). This latter proposal submits that executive functions, such as attentional control and inhibition which are known to depend on the frontal lobes (Hernandez et al., 2002), are consistently associated with primary deficits in AD and can in turn impact other functions such as the ability to maintain and manipulate information in working memory (Barkley, 1997, 2003). While these theories address a number of questions regarding the mechanisms underlying diagnostic features of each disorder, they fall short in adequately explaining the multitude of secondary symptoms and their significant overlap across dyslexia and AD (Gilger & Kaplan, 2001; Kaplan, Dewey, Crawford, & Wilson, 2001; Pennington, 2006)
3- Poor sustained attention or persistence of effort to tasks. This problem often arises when the individual is assigned boring, tedious, protracted, or repetitive activities that lack intrinsic appeal to the person. They often fail to show the same level of persistence, “stick-to-it-tiveness,” motivation, and will-power of others their age when uninteresting yet important tasks must be performed. They often report becoming easily bored with such tasks and consequently shift from one uncompleted activity to another without completing these activities. Loss of concentration during tedious, boring, or protracted tasks is
The “culture of action” (Klawiter 2004: 848) around ADHD in Ireland, then, is such that key (biomedical) experts (certain clinical psychologists and psychiatrists) have become part of the activist landscape, drawn on and deployed by groups to support them in the media, for example, or to speak at events aimed at parents. As a result of the close alignment between Irish ADHD organisations and the biomedical world, parents’ groups have not been interested in shaping or influencing the nature of research being conducted within this arena. Their knowledge production enterprise is instead more applied, focusing on the provision of services, or providing information to parents of children with ADHD. This has led INCADDS and its member groups to push the boundaries of research by drawing on their own embodied experience to challenge current diagnosis and treatment strategies, while remaining committed to the biomedical understanding of the causes of ADHD. In their quest for a multimodal treatment model, groups have been keen to diversify treatment options beyond medication; they have been highly critical of the lack of provision of services in the Irish public health service, and the singular approach to the treatment of ADHD in Ireland, where children are typically assessed and diagnosed by a consultant psychiatrist in a care context which was described by one interviewee as “very strongly biological, medical model”. In response to this, two ADHD groups commissioned evaluations of different therapeutic modalities, one focusing on a family therapy based programme, the other on neurofeedback. Neurofeedback is a type of biofeedback which focuses on the brain and
impulsivity, inattention, depression and anxiety. Moreover, the scales used to measure inattention andhyperactivity had acceptable but not very strong psychometric properties. The limitations of this assessment tool suggest that more stringent measures of mental health problems should be used in this field of research. Third, findings relevant to female sex may have been underpowered due to the small number of medicated females. This limits the generalizability of our results to other samples of girls with ADHD. Fourth, the continued use of medication could not be assessed in this investigation due to missing data for different individuals at various time points. Nonetheless, group-based trajectory modeling enabled us to identify distinct ADHD medication groups and discern their patterns of use across time. Fifth, as with almost all longitudinal studies there was sample attrition. However, additional analyses with inverse probability weights showed that attrition bias was unlikely. Sixth, ADHD severity was not measured in this study. Existing research indicates that it is related to both medication use and persistence of ADHD later in life (Caye et al. 2016; Kessler et al. 2005; Langley et al. 2010). Hence, future studies should account for the severity of ADHD when examining the relationship between the use of ADHD medication in childhood and long-term mental health outcomes. Seventh, since there was no information on dosage, intensity of treatment and adherence to medication, we were unable to determine the impact of these factors on our results. Eighth, other factors that were not measured in this research, may have influenced the associations between childhood ADHD medication use and symptoms of mental health problems in adolescence. Finally, it is important to acknowledge that the correlational design of the current study prevents us from making any causal inferences.
Finally, factor 3 showed negative coupling relations among the DMN and between DAN nodes. In particular, the posterior subregion of the right TPJ depicted lower functional coupling than the anterior subregion, while no such dissociation was observed in the left TPJ. In contrast, factor 2 showed the inverse coupling pattern, while overall showing more positive associations with ASD than ADHD. Earlier studies found a functional separation of the anterior and posterior rTPJ (37, 54): While the anterior subregion was shown to be closely related to the reorientation of attention, the posterior cluster was functionally associated with Theory-of-Mind and social cognition. Across brain phenotypes, distinct patterns of dysconnectivity in the rTPJ effectively differentiated between ADHD and ASD. We hence suggest that a shared expression of factor 2 and 3 may play a critical role in contributing to the variability of shared deficits seen in both disorders.
Some limitations should be taken into account in interpreting the results of this study. First, the response rate was low (18%) so the sample size was relatively small and at-risk families were underrepresented in the sample. This could have impacted the results by underestimating family situation, parental educational attainment and professional activity as risk factors, as is often the case in epidemiological studies (Reiss, 2013; Russell, Ford, Williams, & Russell, 2016). Indeed, the participants were mostly from families with two parents (90%), high educational attainment (69%) and/or paid professional activity (74%). Second, as the sample was from the general population, psychopathologic symptoms were not very frequent. Indeed, only six children had clinical EL, a number not sufficient to conduct statistical analysis. Third, the aggregation of hyperactivityand impulsivity symptoms is clinically relevant but the association with EL may be related to either one. In fact, two studies have already found a specific link between impulsivity and EL (Melnick & Hinshaw, 2000; Wakschlag et al., 2012). Finally, research suggests that, in older children, EL is linked to the development of depression in ADHD (Seymour, Chronis-Tuscano, Iwamoto, Kurdziel, & MacPherson, 2014). Depression might have been implied in our results, especially the link between EL and anxiety. This question should be addressed in further research.
Also at stake is the potential for scientists to exploit geographic variation in ADHD’s prevalence to yield new information concerning its causes, which currently remain frustratingly unknown. Geographic comparisons are a powerful tool for
investigating the etiology of disorders. In the first epidemiological study ever conducted, John Snow compared rates of cholera across areas of London during the 1854 cholera epidemic. Going against the then-prevalent ―miasma‖ theory, which stipulated that diseases are carried by foul air, Snow surveyed residents about their source of drinking water and, using simple statistics, discovered that cholera cases were clustered around one particular water pump. This discovery lead to the closing of the infected pump and prevented new cases. Moreover, although Snow was unable to identify the water-borne particles that cause cholera, his finding advanced the germ theory of disease (3). Contemporary examples show that when populations vary on their exposure to risk factors, geographic comparisons of disorder rates can generate new hypotheses about etiology. High rates of asthma and allergy in developed Western countries, compared to developing countries, generated new hypotheses about the etiology of respiratory
the role of attention is to optimally allocate resources to maximize perfor- mance, it is known that some involuntary attention mechanisms can actually hinder the correct execution of a task .
Robots vs. humans. The astonishing progress in robotics and computer vision over the last three decades might induce us to ask: how far is robot perception from human performance? Let us approach this question by looking at the state of the art in visual processing for different tasks. Without any claim to be exhaustive, we consider few representative papers (sampled over the last 2 years) and we only look at timing performance. A state-of-the-art approach for object detection  detects objects in a scene in 22ms on a Titan X GPU. A high- performance approach for stereo reconstruction  builds a triangular mesh of a 3D scene in 10-100ms on a single CPU (at resolution 800 × 600). A state-of-the-art vision-based SLAM approach  requires around 400ms for local mapping and motion tracking and more than 1s for global map refinement (CPU, multiple cores). The reader may notice that for each task, in isolation, modern algorithms require more time than what a human needs to parse an entire scene. Arguably, while a merit of the robotics and computer vision communities has been to push performance in each task, we are quite far from a computational model in which all these tasks (pose estimation, geometry reconstruction, scene understanding) are concurrently executed in the blink of an eye.
II. RELATED WORK
This work intersects several lines of research across fields. Attentionand Saliency in Neuroscience and Psychology. Attention is a central topic in human and animal vision research with more than 2500 papers published since the 1980s . While a complete coverage is outside the scope of this work, we review few basic concepts, using the surveys of Carrasco , Borji and Itti , Scholl , and the work of Caduff and Timpf  as main references. Scholl  defines attention as the discrimination of sensory stimuli, and the allo- cation of limited resources to competing attentional demands. Carrasco  identifies three types of attention: spatial, feature- based, and object-based. Spatial attention prioritizes different locations of the scene by moving the eyes towards a specific location (overt attention) or by focusing on relevant locations within the field of view (covert attention). Feature-based atten- tion prioritizes the detection of a specific feature (color, motion direction, orientation) independently on its location. Object- based attention prioritizes specific objects. In this work, we are mainly interested in covert spatial attention: which locations in the field of view are the most informative for navigation? Covert attention in humans is a combination of voluntary and involuntary mechanisms that guide the processing of visual stimuli at given locations in the scene . Empirical evidence shows that attention is task-dependent in both primates and humans , . Borji and Itti  explicitly capture this aspect by distinguishing bottom-up and top-down attention models; in the former the attention is captured by visual cues (stimulus-driven), while in the latter the attention is guided by the goal of the observer. Caduff and Timpf  study landmark saliency in human navigation and conclude that saliency stems from the intertwining of intrinsic property of a landmark (e.g., appearance) and the state of the observer (e.g., prior knowledge, observation pose). Another important aspect, that traces back to the guided search theory of Wolfe  and Spekreijse , is the distinction between pre-attentive and attentive visual processes. Pre-attentive processes handle all incoming sensory data in parallel; then, attentive processes only work on a filtered-out-version of the data, which the brain deems more relevant. General computational models for attention are reviewed in , including Bayesian models, graph-theoretic, and information-theoretic formulations.
Children’s IH/I Behaviors were assessed based on parent-ratings of five hyperactivity- impulsivity items (can’t stand still, is agitated; fidgety; difficulty remaining quiet; impulsivity: impulsive, acts without thinking; difficulty waiting for his/her turn) and two inattention items (is easily distracted, has trouble sticking to any activity; cannot concentrate, cannot pay attention for long). These items relied on the early childhood behavior scale from the Canadian National Longitudinal Study of Children and Youth (Statistics Canada, 1995). The items used were similar to those used in previous investigations from preschool to adolescence (Galéra et al., 2011; Nagin & Tremblay, 1999), which showed good internal consistency (alpha = .85-.89 from age 6 to 15) and predictive validity for parent-rated preschool hyperactivity-impulsivity in regards to teacher’s assessment of IH/I behaviors in first grade (Cohen's d = .83 and .59, respectively, for teacher- rated hyperactivityand inattention; Carbonneau et al., 2016). Notably, only 2.4% of the children in our study were above 10 years old. All items referred to the past year (although, typically, respondents for younger children were advised to rely particularly on the last few months) and were coded on a Likert-type scale (never=0, sometimes=1, often=2). Items were summed to a 14- point indicator of IH/I. Children’s scores were first computed within each age group (i.e., ages 1- 2, 2-5, 5-7, 7-10, and over 10 years), then standardized and aggregated into a global scale. The internal consistency across age groups was respectively 0.56, 0.73, 0.82, 0.84, and 0.84 (alpha; average 0.76). As it is often the case with disruptive behavior scales during the preschool years, coefficients were lower at younger ages (e.g., Basten et al., 2016; Carbonneau et al., 2016; Shaw et al. 2005).
when the regularization vanishes: DP γΩ (θ) → γ→0 LP(θ);
LP γΩ (θ) also satisfies this property. The “if” direction of
the third claim follows by showing that max −γH satisfies
associativity. This recovers known results in the framework of message passing algorithms for probabilistic graphical models (e.g., Wainwright & Jordan , 2008 , Section 4.1.3), with a more algebraic point of view. The key role that the distributive and associative properties play into breaking down large problems into smaller ones has long been noted ( Verdu & Poor , 1987 ; Aji & McEliece , 2000 ). However, the “and only if” part of the claim is new to our knowledge. Its proof shows that max −γH is the only max Ω satisfying
1.2 Toolbox for Studying Pain: The Visual Analogue Scale and Nociceptive Flexion Reflex
Our understanding of the neural mechanisms underlying the pain experience has been greatly advanced by the advent of methods that allow for the study of pain processing at various levels of the nervous system. The sensation of pain is most commonly associated with in changes in autonomic, spinal, and supraspinal activity, and therefore techniques to specifically measure each of these are of fundamental importance in order to advance our understanding of pain as a whole. Of the presently available methods, those most often employed in current research on pain include, but are not restricted to, galvanic skin response (GSR), heart rate (HR), electrocardiogram (ECG), respiratory rate, blood pressure (BP), NFR, subjective pain ratings, electroencephalogram (EEG), positron emission tomography (PET) and fMRI. Despite the inherent limitations of each method, together these tools provide critical information on how pain affects the nervous system and provides vital insight into what is happening at the autonomic, spinal, and supraspinal levels.
Éric Samarut 1,2† , Jessica Nixon 3† , Uday P. Kundap 1 , Pierre Drapeau 1 and Lee D. Ellis 3 *
1 Department of Neurosciences, Research Center of the University of Montreal Hospital Center (CRCHUM), Université de Montréal,
Montréal, QC, Canada, 2 Modelis Inc., Montréal, QC, Canada, 3 National Research Council of Canada, Halifax, NS, Canada
In this study, we aimed to investigate the effect of the two main active cannabinoids extracted from cannabis: Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on two distinct behavioral models of induced neuro-hyperactivity. We have taken advantage of two previously developed zebraish models of neuro-hyperactivity: a chemically induced pentylenetetrazole model and a genetic model caused by loss-of-function mutations in the GABA receptor subunit alpha 1 (GABRA1 −/−). Both CBD and THC have a signiicant effect on the behavioral changes induced by both models. Importantly, we have also shown that when applied together at different ratios of THC to CBD (1:1, 1:5, and 1:10), there was a synergistic effect at a ratio of 1:1. This was particularly important for the genetically induced neuro-hyperactivity as it brought the concentrations of THC and CBD required to oppose the induced behavioral changes to levels that had much less of an effect on baseline larval behavior. The results of this study help to validate the ability of THC and CBD to oppose neuro-hyperactivity linked to seizure modalities. Additionally, it appears that individually, each cannabinoid may be more effective against the chemically induced model than against the GABRA1−/− transgenic model. However, when applied together, the concentration of each compound required to oppose the GABRA1−/− light- induced activity was lowered. This is of particular interest since the use of cannabinoids as therapeutics can be dampened by their side-effect proile. Reducing the level of each cannabinoid required may help to prevent off target effects that lead to side effects. Additionally, this study provides a validation of the complimentary nature of the two zebraish models and sets a platform for future work with cannabinoids, particularly in the context of neuro-hyperactivity disorders such as epilepsy.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional af ﬁliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/ .
mutations of the CaV2.1 Ca 2+ channel and the glial Na + /K + pump, respectively, leading to facilitation
of CSD in mouse models mainly because of increased glutamatergic transmission/extracellular glutamate build-up. FHM type 3 mutations of the SCN1A gene, coding for the voltage gated sodium channel NaV1.1, cause gain of function of the channel and hyperexcitability of GABAergic interneurons. This leads to the counterintuitive hypothesis that intense firing of interneurons can cause CSD ignition. To test this hypothesis in silico, we developed a computational model of an E-I pair (a pyramidal cell and an interneuron), in which the coupling between the cells in not just synaptic, but takes into account also the effects of the accumulation of extracellular potassium caused by the activity of the neurons and of the synapses. In the context of this model, we show that the intense firing of the interneuron can lead to CSD. We have investigated the effect of various biophysical parameters on the transition to CSD, including the levels of glutamate or GABA, frequency of the interneuron firing and the efficacy of the KCC2 co-transporter. The key element for CSD ignition in our model was the frequency of interneuron firing and the related accumulation of extracellular potassium, which induced a depolarization block of the pyramidal cell. Our model can be used to study other types of activities in microcircuits and of couplings between excitatory and inhibitory neurons.
Once calculated, each profile is smoothed across ages using the Friedman (1984) method
recommended in the NTA methodology (UN, 2014, Appendix B, p. 159‐164). Because not all
profiles are available for each year between 1979 and 2005, we interpolate the values for all
ages in missing years using polynomial functions. Specifically, we rely on cubic functions which
inattention-hyperactivity [ 31 ]. Additional research is needed to establish whether the promo- tion of breastfeeding may be beneficial in some groups of children (for instance those born to low-SES families, or those in which the mother experiences depression).
The choice of a dimensional measure (score of inattention-hyperactivity symptoms) rather than a categorical one (diagnosis of ADHD yes/no) may be discussed. ADHD is rarely diag- nosed as early as 3 years of age because behaviors of inattention-hyperactivity leading to the di- agnosis may be transient or reflect normative temperamental variations at this age. However, children with early inattention-hyperactivity symptoms who do not meet all the criteria for ADHD diagnosis are still at risk of academic underachievement in elementary school [ 3 ]. Therefore, predictors of inattention-hyperactivity symptoms in preschoolers deserve to be studied. Furthermore, there is considerable evidence that ADHD is a dimensional trait rather than a categorical disorder [ 32 ] and the dimensional approach affords a number of benefits, in- cluding the preservation of more information, superior reliability, and greater power in statistical analyses.