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

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

Diffusion kurtosis imaging reveals abnormal gray matter and white matter development in some brain regions of children with attention-deficit/hyperactivity disorder

In this study, we explored the application of diffusion kurtosis imaging (DKI) technology in the brains of children with attention-deficit/hyperactivity disorder (ADHD). Seventy-two children with ADHD and 79 age- and sex-matched healthy controls were included in the study. All children were examined by means of 3D T1-weighted image, DKI, and conventional sequence scanning. The volume and DKI parameters of each brain region were obtained by software postprocessing (GE ADW 4.6 workstation) and compared between the two groups of children to determine the imaging characteristics of children with ADHD. The result showed the total brain volume was lower in children with ADHD than in healthy children (p < .05). The gray and white matter volumes in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, putamen, globus pallidus, and other brain regions were lower in children with ADHD than in healthy children (p < .05). The axial kurtosis (Ka), mean kurtosis (MK), fractional anisotropy (FA), and radial kurtosis(Kr) values in the frontal lobe, temporal lobe, and caudate nucleus of children with ADHD were lower than those of healthy children, while the mean diffusivity(MD) and fractional anisotropy of kurtosis (FAK) values were higher than those of healthy children (p < .05). Additionally, the Ka, MK, FA, and Kr values in the frontal lobe, caudate nucleus, and temporal lobe could be used to distinguish children with ADHD (AUC > .05, p < .05). In conclusion, DKI showed abnormal gray matter and white matter development in some brain regions of children with ADHD.

Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing framework

Objective. The diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is important for the refined treatment of ADHD children. Although automated diagnosis methods based on machine learning are performed with structural and functional magnetic resonance imaging (sMRI and fMRI) data which have full observation of brains, they are not satisfactory with the accuracy of less than80%for the ADHD subtype diagnosis.Approach. To improve the accuracy and obtain the biomarker of ADHD subtypes, we proposed a hierarchical binary hypothesis testing (H-BHT) framework by using brain functional connectivity (FC) as input bio-signals. The framework includes a two-stage procedure with a decision tree strategy and thus becomes suitable for the subtype classification. Also, typical FC is extracted in both two stages of identifying ADHD subtypes. That means the important FC is found out for the subtype recognition.Main results. We apply the proposed H-BHT framework to resting state fMRI datasets from ADHD-200 consortium. The results are achieved with the average accuracy97.1%and an average kappa score 0.947. Discriminative FC between ADHD subtypes is found by comparing the P-values of typical FC.Significance. The proposed framework not only is an effective structure for ADHD subtype classification, but also provides useful reference for multiclass classification of mental disease subtypes.

Alterations in the Glycan Composition of Serum Glycoproteins in Attention-Deficit Hyperactivity Disorder

Changes in protein glycosylation are associated with most biological processes, and the importance of glycomic analysis in the research of disorders is constantly increasing, including in the neurodevelopmental field. We glycoprofiled sera in 10 children with attention-deficit hyperactivity disorder (ADHD) and 10 matching healthy controls for 3 types of samples: whole serum, sera after depletion of abundant proteins (albumin and IgG), and isolated IgG. The analytical methods used were a lectin-based glycoprotein microarray enabling high-throughput glycan analysis and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) as a standard method for the identification of glycan structures. For microarray analysis, the samples printed on microarray slides were incubated with biotinylated lectins and detected using the fluorescent conjugate of streptavidin by a microarray scanner. In the ADHD patient samples, we found increased antennary fucosylation, decreased di-/triantennary N-glycans with bisecting N-acetylglucosamine (GlcNAc), and decreased α2-3 sialylation. The results obtained by both independent methods were consistent. The study's sample size and design do not allow far-reaching conclusions to be drawn. In any case, there is a strong demand for a better and more comprehensive diagnosis of ADHD, and the obtained results emphasize that the presented approach brings new horizons to studying functional associations of glycan alterations in 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.

Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review

In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.

Three-dimensional pseudocontinuous arterial spin labeling perfusion imaging shows cerebral blood flow perfusion decline in attention-deficit/hyperactivity disorder children

PURPOSE

To investigate the feasibility of three-dimensional pseudocontinuous arterial spin labeling (3D-pcASL) perfusion imaging in the brain of children with Attention-deficit/hyperactivity disorder (ADHD).

METHODS

A total of 78 ADHD children aged 5-13 years were prospectively selected as the study group, and 89 healthy children matched in age and sex were selected as the control group. All children underwent MRI conventional sequence, 3D-pcASL, and 3D-T1 sequence scans. The brain gray and white matter volume and cerebral blood flow (CBF) perfusion values were obtained by software post-processing, and were compared and analyzed in the two groups to find out their characteristics in the brain of ADHD children.

RESULTS

The total brain volume and total CBF values were lower in ADHD children than in healthy children (P < 0.05); the gray and white matter volumes in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, putamen, globus pallidus and other brain regions were lower in ADHD children than in healthy children (P < 0.05); the gray matter CBF values in the frontal lobe, temporal lobe, hippocampus, caudate nucleus, putamen, globus pallidus and other brain regions were lower in ADHD children than in healthy children (P < 0.05); the differences between the white matter CBF values of white matter in the said brain regions of ADHD children and healthy children were not statistically significant (P > 0.05); and the CBF values in frontal lobe and caudate nuclei could distinguish ADHD children (AUC > 0.05, P < 0.05).

CONCLUSION

The 3D-pcASL technique showed reduced cerebral perfusion in some brain regions of ADHD children.

Effects of temporally regular versus irregular distractors on goal-directed cognition and behavior

Human environments comprise plenty of task-irrelevant sensory inputs, which are potentially distracting. Auditory distractors often possess an inherent temporal structure. However, it is largely unknown whether and how the temporal regularity of distractors interferes with goal-directed cognitive processes, such as working memory. Here, we tested a total sample of N = 90 participants across four working memory tasks with sequences of temporally regular versus irregular distractors. Temporal irregularity was operationalized by a final tone onset time that violated an otherwise regular tone sequence (Experiment 1), by a sequence of tones with irregular onset-to-onset delays (Experiment 2), and by sequences of speech items with irregular onset-to-onset delays (Experiments 3 and 4). Across all experiments, temporal regularity of distractors did not modulate participants’ primary performance metric, that is, accuracy in recalling items from working memory. Instead, temporal regularity of distractors modulated secondary performance metrics: for regular versus irregular distractors, recall of the first item from memory was faster (Experiment 3) and the response bias was more conservative (Experiment 4). Taken together, the present results provide evidence that the temporal regularity of task-irrelevant input does not inevitably affect the precision of memory representations (reflected in the primary performance metric accuracy) but rather the response behavior (reflected in secondary performance metrics like response speed and bias). Our findings emphasize that a comprehensive understanding of auditory distraction requires that existing models of attention include often-neglected secondary performance metrics to understand how different features of auditory distraction reach awareness and impact cognition and behavior.

Quantitative susceptibility mapping shows lower brain iron content in children with attention-deficit hyperactivity disorder

To investigate the feasibility of quantitative susceptibility mapping in children with attention‐deficit hyperactivity disorder (ADHD), 53 children with ADHD aged 5–16 years were prospectively selected as the study group and 49 healthy children matched with age and gender were selected as the control group. All children underwent magnetic resonance imaging conventional sequence, 3D‐T1, and enhanced T2*‐weighted magnetic resonance angiography (ESWAN) sequence scanning. The iron content of brain regions was obtained through software postprocessing, and the iron content of brain regions of children with ADHD and healthy children was compared and analyzed to find out the characteristics of the iron content of brain regions of children with ADHD. The iron content in frontal lobe, globus pallidus, caudate nucleus, substantia nigra, putamen, and hippocampus of children with ADHD was lower than that of healthy children (p < .05). There was no significant difference in the content of iron in the left and right brain regions of children with ADHD (p > .05). The volume of frontal lobe and hippocampus of children with ADHD was lower than that of healthy children (p < .05). Iron content in brain areas such as globus pallidus, caudate nucleus, hippocampus, and putamen could distinguish children with ADHD (Area under curve [AUC] > 0.5, p < .05). Quantitative susceptibility mapping showed decreased iron content in some brain regions of children with ADHD.