Biomarker Of Attention-deficit/hyperactivity Disorder Research Articles

EEG and eye-fixation related potentials in children with ADHD

Attention deficit hyperactivity disorder (ADHD) is common and difficult to diagnose. It involves symptoms that impair social, academic, and/or occupational functioning, such as difficulty sustaining attention, forgeting and frequent losingthings, impulsive decisions and excessive motor activity. These symptoms can lead to serious deterioration of everyday functioning. Diagnosis of ADHD may be problematic, subjective, and ambiguous due to gender(more common in boys than in girls) and age (more common in younger than older children) biases. Therefore,candidates for neuromarkers in children with ADHD have been sought for a long time. To date, structural (magnetic resonance imaging, MRI) and functional (functional magnetic resonance imaging, fMRI; electroencephalography, EEG) neuromarkers have been searched for – mostly without success. This is due, among other things, to different statistical methods used to analyse the results, different tasks, methods, and study groups, and the disregardof participants’ fatigue levels during the study. Therefore, incorporating objective neurophysiological measures recorded during an attentional task in the diagnosis of ADHD can be helpful in overcoming these problems. Electroencephalography (EEG)is particularly worth mentioning as it isa readilyavailable and no less promising tool that has already been extensively used in the diagnosis of ADHD. Equally important seems to be oculography, which has also come quite close to being recognisedas a biomarker of ADHD. Combining these measurements during a cognitive task could yealdnew insight into neurocognitive functions in ADHD and possible bioelectrical neuromarkers. We propose and discuss a set of hypotheses regarding EEG in children with ADHD; the hypotheses will be tested using the flanker task because it provides a measures of processing inhibition and interference control and is generally considered a factor ofinattention. We will propose and discuss in detail the research hypotheses to be implemented investigating the neurocognitive correlates of ADHD.

No evidence of structural abnormality of the substantia nigra in adult attention-deficit/hyperactivity disorder: a pilot cross-sectional cohort study.

Abnormal expansion of the echogenic substantia nigra (SN+) is a common observation in Parkinson's disease (PD) and considered a potential trait marker within this context. However, SN+ was also frequently detected in children diagnosed with attention-deficit/hyperactivity disorder (ADHD), where it has been discussed as a biomarker of maturational dopaminergic dysfunction. Interestingly, ADHD was recently linked to an elevated risk of PD in epidemiological studies, particularly among individuals treated with psychostimulants. Here, we investigated the potential of SN echogenicity as a disease biomarker in adults with ADHD and its relation to psychostimulant treatment. In an exploratory cross-sectional cohort study, we performed transcranial sonography of the SN in 30 adults (mean age 33.3 ± 7.6 years, 19 males/11 females) diagnosed with ADHD according to DSM-V criteria. In this pilot study, we observed no evidence of structural abnormalities of the SN among adults diagnosed with ADHD, thus questioning the potential of SN+ as a biomarker for ADHD in this population. Moreover, we found no evidence of treatment-related SN echogenicity changes that would link therapeutic psychostimulant use to alterations in the structural integrity of the SN.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study.

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

Altered Urine Microbiome in Male Children and Adolescents with Attention-Deficit Hyperactivity Disorder.

While interest in developing the human microbiome as a biomarker for attention-deficit hyperactivity disorder (ADHD) is increasing, there has been limited exploration in utilizing urine samples. In this study, we analysed urine microbiome profiles by extracting 16S ribosomal DNA from purified bacteria-derived extracellular membrane vesicles obtained from urine samples. Sequencing libraries were constructed by amplifying V3-V4 hypervariable regions sequenced using Illumina MiSeq. Profiles of male Korean children and adolescents with ADHD (n = 33) were compared with healthy sex-matched controls (n = 39). Statistically controlling for age, we found decreased alpha diversity in the urine bacteria of the ADHD group, as evidenced by reduced Shannon and Simpson indices (p < 0.05), and significant differences in beta diversity between the two groups (p < 0.001). The phyla Firmicutes and Actinobacteriota, as well as the genera Ralstonia and Afipia, were relatively more abundant in the ADHD group. The phylum Proteobacteria and the genera Corynebacterium and Peptoniphilus were more abundant in the control group. Notably, the genus Afipia exhibited significant correlations with the Child Behavior Checklist Attention Problems score and DSM-oriented ADHD subscale. This study is the first to propose the urine microbiome as a potential biomarker for pediatric ADHD.

Effects of the SNAP-25 Mnll variant on hippocampal functional connectivity in children with attention deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most widespread and highly heritable neurodevelopmental disorders affecting children worldwide. Although synaptosomal-associated protein 25 (SNAP-25) is a possible gene hypothesized to be associated with working memory deficits in ADHD, little is known about its specific impact on the hippocampus. The goal of the current study was to determine how variations in ADHD's SNAP-25 Mnll polymorphism (rs3746544) affect hippocampal functional connectivity (FC). A total of 88 boys between the ages of 7 and 10 years were recruited for the study, including 60 patients with ADHD and 28 healthy controls (HCs). Data from resting-state functional magnetic resonance imaging (rs-fMRI) and clinical information were acquired and assessed. Two single nucleotide polymorphisms (SNP) in the SNAP-25 gene were genotyped, according to which the study's findings separated ADHD patients into two groups: TT homozygotes (TT = 35) and G-allele carriers (TG = 25). Based on the rs-fMRI data, the FC of the right hippocampus and left frontal gyrus was evaluated using group-based comparisons. The corresponding sensitivities and specificities were assessed. Following comparisons between the patient groups, different hippocampal FCs were identified. When compared to TT patients, children with TG had a lower FC between the right precuneus and the right hippocampus, and a higher FC between the right hippocampus and the left middle frontal gyrus. The fundamental neurological pathways connecting the SNAP-25 Mnll polymorphism with ADHD via the FC of the hippocampus were newly revealed in this study. As a result, the hippocampal FC may further serve as an imaging biomarker for ADHD.

Understanding the ADHD-Gut Axis by Metabolic Network Analysis.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient's development level. The fact that people with ADHD frequently experience gastrointestinal (GI) dysfunction highlights the possibility that the gut microbiome may play a role in this condition. The proposed research aims to determine a biomarker for ADHD by reconstructing a model of the gut-microbial community. Genome-scale metabolic models (GEM) considering the relationship between gene-protein-reaction associations are used to simulate metabolic activities in organisms of gut. The production rates of dopamine and serotonin precursors and the key short chain fatty acids which affect the health status are determined under three diets (Western, Atkins', Vegan) and compared with those of healthy people. Elasticities are calculated to understand the sensitivity of exchange fluxes to changes in diet and bacterial abundance at the species level. The presence of Bacillota (genus Coprococcus and Subdoligranulum), Actinobacteria (genus Collinsella), Bacteroidetes (genus Bacteroides), and Bacteroidota (genus Alistipes) may be possible gut microbiota indicators of ADHD. This type of modeling approach taking microbial genome-environment interactions into account helps us understand the gastrointestinal mechanisms behind ADHD, and establish a path to improve the quality of life of ADHD patients.

Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functionalmagnetic resonance imaging: A radiomics analysis.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve=0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD.

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms "ADHD," and "biomarker" combined with one of "protein," "blood/serum," "gene," and "neuro" were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies.

ADHD classification combining biomarker detection with attention auto-encoding neural network

Attention Deficit Hyperactivity Disorder (ADHD) is one of most prevalent neurodevelopmental disorders in children. In decades, various neurobiological diagnosis methods have been well developed, yielding ADHD classification accuracy significantly improved. To our knowledge, deep-learning-based approaches attract more interest nowadays, since they effectively learn the potential features indicating ADHD disease. However, these learned features cannot directly project to certain biomarkers that have neurobiological meanings, which greatly hinders the neurobiological findings on ADHD. To ameliorate this problem, we proposed an attention auto-encoding network (Att-AENet) under an existing binary hypothesis testing (BHT) framework. By using brain functional connectivities (FCs) as input materials, an attention mechanism was employed to calculate the weights of FCs and measure the FC significance on ADHD. Therefore, the FC biomarkers of ADHD were conveniently identified from these weights. In detail, we built two attention subnetworks with dense and two-dimension convolution structures, respectively, and then evaluated the effect of these subnetworks from the viewpoint of ADHD classification. Sequentially, we took the biomarker detection analysis on the proposed dense-based attention subnetwork for its higher classification accuracy. On the test of ADHD-200 database, it shows the Att-AENet achieves a remarkable classification result with the average accuracy of 98.9%, especially 99.9% on some large-size datasets. More importantly, the FC biomarkers, corresponding to the discriminative weights learned by our attention subnetwork, are in accord with existing reported results. It demonstrates the reliability of these biomarkers. As a result, the proposed Att-AENet well fulfills the task of ADHD biomarker detection.

ADHD symptoms and diurnal cortisol in adolescents: The importance of comorbidities

BackgroundAltered regulation of diurnal cortisol has been associated with both dimensional symptoms and clinical diagnoses of attention deficit-hyperactivity disorder (ADHD). Indeed, a recent meta-analysis suggests that lower diurnal cortisol output may be a biomarker of attention deficit-hyperactivity disorder (ADHD); importantly, however, the influence of psychiatric comorbidities on this association has not been characterized. Approximately two-thirds of children with ADHD have at least one co-occurring neuropsychiatric condition, and altered HPA-axis function has been implicated in many of these conditions. Using dimensional measures of psychopathology, we examined whether comorbid symptoms influence the association of ADHD symptoms with daily cortisol output. Methods138 adolescents (ages 11–15 years) completed measures of symptoms of psychopathology and provided saliva samples over two days. We analyzed whether ADHD symptoms were related to morning, afternoon, and evening cortisol, the cortisol awakening response (CAR) and cumulative daily cortisol (area under the curve with respect to ground [AUCg]) while accounting for symptoms of three psychiatric disorders that are commonly comorbid with ADHD: conduct disorder (CD), anxiety, and depression. In sensitivity analyses, we included symptoms of oppositional defiant disorder (ODD) in place of CD symptoms. FindingsAfter controlling for symptoms of CD, anxiety, and depression, ADHD symptoms were associated significantly with higher cumulative diurnal cortisol (AUCg), morning cortisol, and afternoon cortisol. Symptoms of CD, anxiety and depression were not associated significantly with any cortisol metrics; however, in sensitivity analyses, ODD symptoms were associated with lower AUCg and morning cortisol. DiscussionOur findings highlight the distinct influence of ADHD and externalizing symptoms on cortisol output. Further work is needed to examine the specificity of altered HPA-axis activity as a biomarker of ADHD and to elucidate whether symptoms of ADHD differ in their association with diurnal cortisol as a function of their severity.

A rule-based exploratory analysis for discovery of multimodal biomarkers of ADHD using eye movement and EEG data

Developing biomarkers for a complex neurodevelopmental disorder such as the attention deficit hyperactivity disorder (ADHD) is a challenging task since it is a multifactorial and multi-faceted condition. Researchers have been employing different sensing modalities to acquire measurements of the condition, however, there has been a lack of approaches that can adequately combine the multimodal data and detect interactions among the modalities. To demonstrate the concept and benefit of multimodal biomarker discovery, we conducted a multimodal data collection targeting the ADHD condition and demonstrated how a rule-based exploratory analysis approach could be used to analyze the data. To the best of our knowledge, our work is the first attempt to explore and identify interesting interactions among two modalities of data, eye movement data and the EEG signal, for multimodal biomarker discovery for ADHD. The detection of these interactions would help us better understand the condition and develop better prediction models and intervention strategies.

Absence of dynamic neural oscillatory response to environmental conditions marks childhood attention deficit hyperactivity disorder.

Prior research suggests that symptoms of attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental disorders may derive from alterations in the brain's ability to flexibly tune the balance between information integration and segregation and global versus local processing. This balance allows the brain to optimally filter salient stimuli in the environment and can be measured with electroencephalography (EEG) via calculation of the aperiodic spectral slope. A steeper aperiodic slope increases the capacity of global neural networks to process low-salience stimuli, while a flatter aperiodic slope reflects an emphasis on local neural networks that respond preferentially to high-salience input. Although aperiodic slope differences have been reported in ADHD, prior studies have not accounted for differing levels of stimulus input in experimental paradigms. There is evidence to suggest that dynamic shifts in neural oscillation patterns in response to changing environmental conditions could be critical for attention regulation. Using high-density resting EEG, we measured aperiodic spectral slope during low contrast (lights off) and high contrast (lights on) environmental conditions in a sample of 88 7-11-year-old children diagnosed with ADHD and 29 controls (30% female). While controls showed a flatter aperiodic slope during the high contrast (lights on) as compared to low contrast (lights off) environmental condition, children with ADHD did not show any change in aperiodic slope across conditions. This study presents a novel etiological model of biological mechanisms associated with ADHD. Children with ADHD show suboptimal modulation of intrinsic neural activity in response to changing environmental input. The dynamic spectral slope is a promising candidate biomarker for ADHD. The possibility that dynamic spectral slope is associated with cognitive-behavioral regulation more broadly merits further investigation.

Phase-Amplitude Coupling Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder.

In cognitive neuroscience, there is an increasing interest in identifying and understanding the synchronization of distinct neural oscillations with different frequencies that might support dynamic communication within the brain. This study explored the cross-frequency phase-amplitude coupling brain network characteristics of resting-state electroencephalograms between 30 children with attention-deficit/hyperactivity disorder (ADHD) and 30 age-matched typically developing children. Compared with control group, children with ADHD show increased coupling intensity and altered distribution patterns of dominant paired channels, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. Regarding graph theory properties, the characteristic path length, the mean clustering coefficient, the global efficiency, and the mean local efficiency significant difference in many cross-frequency coupling networks, especially in the δ-γH, θ-γH, α-γH, βL-γH, and βH-γH coupling networks. The area under the receiver operating characteristic curve (AUC) in low-frequency coupling with a high-gamma frequency was larger than that in coupling with low-gamma frequency (AUC values of δ-γL, θ-γL, α-γL, βL-γL, βH-γL, δ-γH, θ-γH, α-γH, βL-γH, and βH-γH were 0.794, 0.722, 0.666, 0.570, 0.881, 0.992, 0.998, 0.998, 0.989, and 0.974, respectively). These findings demonstrate altered coupling intensity and disrupted topological organization of coupling networks, support the altered brain network theory in children with ADHD. The coupling intensity and graph theory properties of low-frequency coupling with high-gamma frequency were promising resting-state electroencephalogram biomarkers of ADHD in children.

MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. This study aimed to examine whether miRNA expression abundance in total white blood cells (WBCs) facilitated the identification of ADHD and reflected its response to treatment. Furthermore, whether miRNA markers facilitated the growth of the human cortical neuronal (HCN-2) cells was also investigated. Total WBC samples were collected from 145 patients and 83 controls, followed by RNA extraction and qPCR assays. Subsequently, WBC samples were also collected at the endpoint from ADHD patients who had undergone 12 months of methylphenidate treatment. The determined ΔCt values of 12 miRNAs were applied to develop an ADHD prediction model and to estimate the correlation with treatment response. The prediction model applying the ΔCt values of 12 examined miRNAs (using machine learning algorithm) demonstrated good validity in discriminating ADHD patients from controls (sensitivity: 96%; specificity: 94.2%). Among the 92 ADHD patients completing the 12-month follow-up, miR-140-3p, miR-27a-3p, miR-486-5p, and miR-151-5p showed differential trends of ΔCt values between treatment responders and non-responders. In addition, the in vitro cell model revealed that miR-140-3p and miR-126-5p promoted the differentiation of HCN-2 cells by enhancing the length of neurons and the number of junctions. Microarray and flow cytometry assays confirmed that this promotion was achieved by repressing apoptosis and/or necrosis. The findings of this study suggest that the expression levels of miRNAs have the potential to serve as both diagnostic and therapeutic biomarkers for ADHD. The possible biological mechanisms of these biomarker miRNAs in ADHD pathophysiology were also clarified.

Electroencephalographic Microstates as Novel Functional Biomarkers for Adult Attention-Deficit/Hyperactivity Disorder

BackgroundResearch on the electroencephalographic (EEG) signatures of attention-deficit/hyperactivity disorder (ADHD) has historically concentrated on its frequency spectrum or event-related evoked potentials. In this work, we investigate EEG microstates (MSs), an alternative framework defined by the clustering of recurring topographical patterns, as a novel approach for examining large-scale cortical dynamics in ADHD. MethodsUsing k-means clustering, we studied the spatiotemporal dynamics of ADHD during the rest condition by comparing the MS segmentations between adult patients with ADHD and neurotypical control subjects across two independent datasets: the first dataset consisted of 66 patients with ADHD and 66 control subjects, and the second dataset comprised 22 patients with ADHD and 22 control subjects and was used for out-of-sample validation. ResultsSpatially, patients with ADHD and control subjects displayed equivalent MS topographies (canonical maps), indicating the preservation of prototypical EEG generators in patients with ADHD. However, this concordance was accompanied by significant differences in temporal dynamics. At the group level, and across both datasets, ADHD diagnosis was associated with longer mean durations of a frontocentral topography (MS D), indicating that its electrocortical generator(s) could be acting as pronounced attractors of global cortical dynamics. In addition, its spatiotemporal metrics were correlated with sleep disturbance, the latter being known to have a strong relationship with ADHD. Finally, in the first (larger) dataset, we also found evidence of decreased time coverage and mean duration of a left-right diagonal topography (MS A), which inversely correlated with ADHD scores. ConclusionsOverall, our study underlines the value of EEG MSs as promising functional biomarkers for ADHD, offering an additional lens through which to examine its neurophysiological mechanisms.

Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is diagnosed subjectively based on an individual's behaviour and performance. The clinical community has no objective biomarker to inform the diagnosis and subtyping of ADHD. This study aimed to explore the potential diagnostic biomarkers of ADHD among surface values, volumetric metrics and radiomic features that were extracted from structural MRI images. Public data of New York University and Peking University were downloaded from the ADHD-200 Consortium. MRI T1-weighted images were pre-processed using CAT12. We calculated surface values based on the Desikan-Killiany atlas. The volumetric metrics (mean grey matter volume and mean white matter volume) and radiomic features within each automated anatomical labelling (AAL) brain area were calculated using DPABI and IBEX, respectively. The differences among three groups of participants were tested using ANOVA or Kruskal-Wallis test depending on the normality of the data. We selected discriminative features and classified typically developing controls (TDCs) and ADHD patients as well as two ADHD subtypes using least absolute shrinkage and selection operator and support vector machine algorithms. Our results showed that the radiomics-based model outperformed the others in discriminating ADHD from TDC and classifying ADHD subtypes (area under the curve [AUC]: 0.78 and 0.94 in training test; 0.79 and 0.85 in testing set). Combining grey matter volumes, surface values and clinical factors with radiomic features can improve the performance for classifying ADHD patients and TDCs with training and testing AUCs of 0.82 and 0.83, respectively. This study demonstrates that MRI T1-weighted features, especially radiomic features, are potential diagnostic biomarkers of ADHD.

Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review.

Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD. The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD. This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review. Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available.

Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets.

Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t=2.1, p=.04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t=2.7, p=.01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Precision Medicine Care in ADHD: The Case for Neural Excitation and Inhibition

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that has become increasingly prevalent worldwide. Its core symptoms, including difficulties regulating attention, activity level, and impulses, appear in early childhood and can persist throughout the lifespan. Current pharmacological options targeting catecholamine neurotransmissions have effectively alleviated symptoms in some, but not all affected individuals, leaving clinicians to implement trial-and-error approach to treatment. In this review, we discuss recent experimental evidence from both preclinical and human studies that suggest imbalance of excitation/inhibition (E/I) in the fronto-striatal circuitry during early development may lead to enduring neuroanatomical abnormality of the circuitry, causing persistence of ADHD symptoms in adulthood. We propose a model of precision medicine care that includes E/I balance as a candidate biomarker for ADHD, development of GABA-modulating medications, and use of magnetic resonance spectroscopy and scalp electrophysiology methods to monitor the effects of treatments on shifting E/I balance throughout the lifespan.

L-Cystine is associated with the dysconnectivity of the default-mode network and salience network in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Distributed dysconnectivity within both the default-mode network (DMN) and the salience network (SN) has been observed in ADHD. L-cystine may serve as a neuroprotective molecule and signaling pathway, as well as a biomarker of ADHD. The purpose of this study was to explore whether differential brain network connectivity is associated with peripheral L-cystine levels in ADHD patients. We recruited a total of 31 drug-naïve patients with ADHD (mean age: 10.4 years) and 29 healthy controls (mean age: 10.3 years) that underwent resting state functional magnetic resonance imaging scans. Functional connectomes were generated for each subject, and we examined the cross-sectional group difference in functional connectivity (FC) within and between DMN and SN. L-cystine plasma levels were determined using high-performance chemical isotope labeling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Compared to the control group, the ADHD group showed decreased FC of dorsal DMN (p = 0.031), as well as decreased FC of precuneus-post SN (p = 0.006) and ventral DMN-post SN (p = 0.001). The plasma L-cystine levels of the ADHD group were significantly higher than in the control group (p = 0.002). Furthermore, L-cystine levels were negatively correlated with FC of precuneus-post SN (r = −0.404, p = 0.045) and ventral DMN-post SN (r = −0.540, p = 0.007). The findings suggest that decreased synergies of DMN and SN may serve as neurobiomarkers for ADHD, while L-cystine may be involved in the pathophysiology of network dysconnectivity. Future studies on the molecular mechanism of the cystine-glutamate system in brain network connectivity are warranted.