Reward Processing Deficits During a Spatial Attention Task in Patients With ADHD: An fMRI Study

Connections Between the Middle Frontal Gyrus and the Dorsoventral Attention Network Are Associated With the Development of Attentional Symptoms

BACKGROUND

The right middle frontal gyrus (MFG) has been proposed as a convergence site for the dorsal attention network (DAN) and ventral attention network (VAN), regulating both networks and enabling flexible modulation of attention. However, it is unclear whether the connections between the right MFG and these networks can predict changes in attention-deficit/hyperactivity disorder (ADHD) symptoms.

METHODS

This study used data from the Children School Functions and Brain Development project (N = 713, 56.2% boys). Resting-state functional magnetic resonance imaging was employed to analyze the connections of the right MFG with the DAN/VAN; connectome-based predictive modeling was applied for longitudinal prediction, and ADHD polygenic risk scores were used for genetic analysis.

RESULTS

ADHD symptoms were associated with the connections between the right MFG and DAN subregion, including the frontal eye field, as well as the VAN subregions, namely the inferior parietal lobule and inferior frontal gyrus. Furthermore, these connections of the right MFG with the frontal eye field, the inferior parietal lobule, and the inferior frontal gyrus could significantly predict changes in ADHD symptoms over 1 year and mediate the prediction of ADHD symptom changes by polygenic risk scores for ADHD. Finally, the validation samples confirmed that the functional connectivity between the right MFG and the frontal eye field/inferior parietal lobule in patients with ADHD was significantly weaker than that in typically developing control participants, and this difference disappeared after medication.

CONCLUSIONS

The connection of the right MFG with the DAN and VAN can serve as a predictive indicator for changes in ADHD symptoms over the following year, while also mediating the prediction of ADHD symptom changes by a polygenic risk score for ADHD. These findings hold promise as potential biomarkers for early identification of children who are at risk of developing ADHD.

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

The impact of successful learning of self-regulation on reward processing in children with ADHD using fMRI

Neurofeedback (NF) is a non-pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD) that is targeting self-regulation, is efficacious when standard protocols are used and induces partly specific neurophysiological changes in the inhibitory network. However, its effects on reward processing, which is also considered an important aspect of ADHD and has been linked to neurophysiological deficits, remain unknown. Children with ADHD (N = 15, mean age 11.8, SD 1.52) were randomly assigned to either slow cortical potential NF (n = 8) or EMG biofeedback control training (n = 7) and received 20 sessions of training under comparable conditions. Learning was defined as the slope of successful training runs across all transfer sessions. Whole brain analysis, region-of-interest analysis of anticipatory ventral striatal (VS) activation, and analysis of behavioral data were performed. Clinically, the NF group improved more than the EMG group. Whole brain analysis indicated increased activation in the left superior frontal gyrus in the control group only, and in medial prefrontal cortex and dorsolateral prefrontal gyrus (DLPFC) after treatment across all groups. Only successful learners of self-regulation (n = 8) showed increased left inferior frontal gyrus and DLPFC activation after treatment. Left VS activation was increased after treatment and showed a significant time*medication-status interaction. Specific treatment effects were found in left frontal regions for the control treatment and successful learners. Also, unmedicated participants, irrespective of treatment type or successful learning, showed treatment-induced improvement in reward processing. The results suggest no prominent specific effect of NF on reward processing. However, cautious interpretation is warranted due to the small sample.

Functional coding variation in the presynaptic dopamine transporter associated with neuropsychiatric disorders drives enhanced motivation and context-dependent impulsivity in mice

Recent genetic analyses have provided evidence that clinical commonalities associated with different psychiatric diagnoses often have shared mechanistic underpinnings. The development of animal models expressing functional genetic variation attributed to multiple disorders offers a salient opportunity to capture molecular, circuit and behavioral alterations underlying this hypothesis. In keeping with studies suggesting dopaminergic contributions to attention-deficit hyperactivity disorder (ADHD), bipolar disorder (BPD) and autism spectrum disorder (ASD), subjects with these diagnoses have been found to express a rare, functional coding substitution in the dopamine (DA) transporter (DAT), Ala559Val. We developed DAT Val559 knock-in mice as a construct valid model of dopaminergic alterations that drive multiple clinical phenotypes, and here evaluate the impact of lifelong expression of the variant on impulsivity and motivation utilizing the 5- choice serial reaction time task (5-CSRTT) and Go/NoGo as well as tests of time estimation (peak interval analysis), reward salience (sucrose preference), and motivation (progressive ratio test). Our findings indicate that the DAT Val559 variant induces impulsivity behaviors that are dependent upon the reward context, with increased impulsive action observed when mice are required to delay responding for a reward, whereas mice are able to withhold responding if there is a probability of reward for a correct rejection. Utilizing peak interval and progressive ratio tests, we provide evidence that impulsivity is likely driven by an enhanced motivational phenotype that also may drive faster task acquisition in operant tasks. These data provide critical validation that DAT, and more generally, DA signaling perturbations can drive impulsivity that can manifest in specific contexts and not others, and may rely on motivational alterations, which may also drive increased maladaptive reward seeking.