A novel approach to intra-individual performance variability in ADHD

Variable, sometimes absent, but never negative: Applying multilevel models of variability to the backward crosstalk effect to find theoretical constraints

When performing two tasks at the same time, the congruency of the second task's features influences performance in the first task. This is called the backward crosstalk effect (BCE), a phenomenon that influences both theories of binding and of dual-task capacity limitations. The question of whether the BCE is found in all participants at all times is relevant for understanding the basis of the effect. For example, if the BCE is based on strategic choices, it can be variable, but if it is automatic and involuntary, it should never vary in whether it is present or not. Variability in observed BCE sizes was already documented and discussed when the group average effect was first reported (Hommel, 1998). Yet the theories discussed at the time did not motivate a more direct analysis of this variability, nor did the readily available statistical tools permit it. Some statistical approaches recently applied in cognitive psychology allow such a variability-focused analysis and some more recent theoretical debates would benefit from this as well. We assessed the variability of the BCE as well as a BCE-like free-choice congruency effect by applying a Bayesian multilevel modeling approach to the data from a dual-tasking experiment. Trials consisted of a two- and a four-response task. We manipulated which task was presented first and whether the response to the four-choice task was free or forced choice. RT data were best predicted by a model in which the BCE is zero in part of the population and drawn from a normal distribution truncated at zero (and thus always positive) in the rest of the population. Choice congruency bias data were best predicted by a model assuming this effect to be drawn from a normal distribution truncated at zero (but, in contrast to the RT data, without the subset of the population where it is zero). The BCE is not an inflexible and universal phenomenon that is directly linked to an inherent structural trait of human cognition. We discuss theoretical constraints implied by these results with a focus on what we can infer about the traits of the factors that influence BCE size. We suggest that future research might add further major constraints by using multi-session experiments to distinguish between-person and within-person variability. Our results show that the BCE is variable. The next step is understanding along which axes it is variable and why it varies.

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

OBJECTIVE

Interindividual similarity refers to how similarly individuals respond when receiving the same stimulus or intervention. In this study, we aimed to examine interindividual similarity in adults with ADHD.

METHOD

We used the cosine similarity index of ex-Gaussian reaction time (RT) vectors of mu, sigma, and tau parameters during a Stroop task.

RESULTS

Our results demonstrate that the ADHD group exhibits a reduced interindividual similarity index in their ex-Gaussian RT vectors for congruent stimuli compared to the healthy control group. Importantly, we did not find significant differences in the interindividual similarity index to incongruent stimuli between both groups, thus suggesting that this reduced index was selective for congruent stimuli.

CONCLUSION

Our findings highlight that ADHD adults exhibit more significant interindividual differences in cognitive functioning when processing congruent stimuli than healthy controls. These results provide new insights into the selective mechanisms underlying ADHD and may contribute to developing new targeted interventions for this disorder.

Time-Perception Deficits in Attention-Deficit/Hyperactivity Disorder: A Systematic Review and Meta-Analysis

The present meta-analysis quantified the deficit in time perception in Attention-Deficit/Hyperactivity Disorder (ADHD) throughout the lifespan and examined potential moderators of this deficit. Our sample of 824 effect sizes showed a mean g of 0.688 that was moderated by the age of the sample and working memory. Separate moderator analyses for samples below or above the age of 18 showed that the link with working memory only applied to the samples below the age of 18, whereas an effect of ADHD subtype only applied to samples 18 and above. The discussion highlights the implications for remediation and avenues for future research.

Psychomotor intervention to improve handwriting skills in children with ADHD: A single-case experimental design with direct inter-subject and systematic replications

Dysgraphia is highly prevalent in children with attention deficit hyperactivity disorder (ADHD) and adversely affects academic and developmental trajectories. To date, no study has rigorously examined the effects of a training program on handwriting quality in this specific population. Our objective was thus to develop an innovative program - we entitled PRO-PEN - and to evaluate its effects. We planned a multiple-baseline design across participants from grade 3-5, with direct inter-subject and systematic replications. Children of Group 1 (n = 4) were diagnosed with ADHD. Systematic replication was conducted in a second group of participants (Group 2, n = 4) with a diagnosis of developmental coordination disorder in addition to ADHD. The primary assessment focused on quality of handwriting. Generalization measures evaluated diverse neuropsychological and behavioural domains. In Group 1, effect sizes regarding handwriting quality were large (Taus > .60). Improvement was also observed for children of Group 2 (Taus > .50). Importantly, the positive effects persisted three months after the end of the training. Generalization effects extended beyond handwriting sphere. Therefore, PRO-PEN can be considered a promising training program for improving handwriting quality in ADHD, with a possible impact on wide cerebral regulation loops underpinning both handwriting and other neuropsychological and behavioural domains.

Resting-state BOLD signal variability is associated with individual differences in metacontrol

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Global slowness and increased intra-individual variability are key features of attentional deficits and cognitive fluctuations in post COVID-19 patients

Fatigue, attentional deficits and cognitive fluctuations are the most characterizing symptoms of neurological involvement in Post COVID-19 syndrome (PCS). As the intraindividual variability (IIV) in cognitive performances has been recognized as a hallmark of brain-related disorders associated with cognitive deficits, it could be an interesting measure to elucidate the mechanisms subtending both the attentive impairment and the cognitive fluctuations in these patients. By referring to IIV analysis of Reaction Times (RTs), the present study aims to define the attentive impairment and its relation to fluctuations and fatigue, in patients suffering from Post COVID-19 neurological symptoms. 74 patients were enrolled. They underwent an extensive clinical and neuropsychological assessments, as well as computerized Sustained Attention and Stroop tasks. For studying IIV, RTs distributions of performances in computerized tasks were fitted with ex-Gaussian distribution, for obtaining the τ values. Finally, the Resting Motor Threshold (RMT) was also collected to estimate cortical excitability. 29 healthy volunteers served as controls. Patients showed poorer scores in Montreal Cognitive Assessment and higher RMT, in comparison with controls. In Sustained Attention Task, Mean, µ, σ and τ values were significantly higher in PCS patients (p value =  < 0.0001; 0.001; 0.018 and < 0.0001, respectively). Repeated measures ANOVA comparing the RTs mean in Stroop task within-subject and between-subjects revealed significant condition and group effect (p < 0.0001 both) and significant interaction (p = 0.005), indicating worst performances in patients. The mean of the derived interference value was significantly higher in PCS patients than in controls (p = 0.036). Patients suffering from PCS show deficits in attention, both in the sustained and executive components. Both high RTs means and high IIV subtend these deficits and could explain the often-complained cognitive fluctuations in this population.

Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records

The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1%. SVM using single features showed slight differences between features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-world clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.

The metacontrol hypothesis as diagnostic framework of OCD and ADHD: A dimensional approach based on shared neurobiological vulnerability

Obsessive-compulsive disorder (OCD) and attention-deficit hyperactivity disorder (ADHD) are multi-faceted neuropsychiatric conditions that in many aspects appear to be each other's antipodes. We suggest a dimensional approach, according to which these partially opposing disorders fall onto a continuum that reflects variability regarding alterations of cortico-striato-thalamo-cortical (CSTC) circuits and of the processing of neural noise during cognition. By using theoretical accounts of human cognitive metacontrol, we develop a framework according to which OCD can be characterized by a chronic bias towards exaggerated cognitive persistence, equivalent to a high signal-to-noise ratio (SNR)-which facilitates perseverative behaviour but impairs mental flexibility. In contrast, ADHD is characterized by a chronic bias towards inflated cognitive flexibility, equivalent to a low SNR-which increases behavioural variability but impairs the focusing on one goal and on relevant information. We argue that, when pharmacology is not feasible, novel treatments of these disorders may involve methods to manipulate the signal-to-noise ratio via non-invasive brain stimulation techniques, in order to normalize the situational imbalance between cognitive persistence and cognitive flexibility.