Objective To explore the white matter microstructural abnormalities in patients with different subtypes of attention-deficit/hyperactivity disorder (ADHD) and establish a diagnostic classification model. Methods Patients with ADHD admitted to West China Hospital of Sichuan University between January 2019 and September 2021 and healthy controls recruited through advertisement were prospectively selected. All participants underwent diffusion tensor imaging scanning. The whole brain voxel-based analysis was used to compare the diffusion parameter maps of fractional anisotropy (FA) among patients with combined subtype of ADHD (ADHD-C), patients with inattentive subtype of ADHD (ADHD-I) and healthy controls. The support vector machine classifier and feature selection method were used to construct the individual ADHD diagnostic classification model and efficiency was evaluated between each two groups of the ADHD patients and healthy controls. Results A total of 26 ADHD-C patients, 24 ADHD-I patients and 26 healthy controls were included. The three groups showed significant differences in FA values in the bilateral sagittal stratum of temporal lobe (ADHD-C<ADHD-I<healthy controls) and the isthmus of corpus callosum (ADHD-C>ADHD-I>healthy controls) (P<0.005). The direct comparison between the two subtypes of ADHD showed that ADHD-C had higher FA than ADHD-I in the right middle frontal gyrus. The classification model differentiating ADHD-C and ADHD-I showed the highest efficiency, with a total accuracy of 76.0%, sensitivity of 88.5%, and specificity of 70.8%. Conclusions There is both commonality and heterogeneity in white matter microstructural alterations in the two subtypes of patients with ADHD. The white matter damage of the sagittal stratum of temporal lobe and the corpus callosum may be the intrinsic pathophysiological basis of ADHD, while the anomalies of frontal brain region may be the differential point between different subtypes of patients.
Habitual snoring can occur in both children and adults. If it is physiological snoring, it usually does not require special intervention. If it is pathological snoring, such as snoring caused by central diseases and obstructive diseases, it needs to be treated as soon as possible. Habitual snoring has more harm to children, such as causing sleep structure disorders, slow growth and development. During the snoring process, children’s sleep fragmentation and hypoxia state lead to changes in the transmission of neurochemicals in the brain’s precortex, causing adverse effects on brain function and inducing attention deficit hyperactivity disorder. This article reviews relevant research in recent years to further elucidate the relationship between children’s habitual snoring and attention deficit hyperactivity disorder, and provide a basis for future clinical research and intervention.
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.
ObjectiveTo systematically review the methodological quality of guidelines concerning attention-deficit/hyperactivity disorder (ADHD) in children and adolescents, and to compare differences and similarities of the drugs recommended, in order to provide guidance for clinical practice. MethodsGuidelines concerning ADHD were electronically retrieved in PubMed, EMbase, VIP, WanFang Data, CNKI, NGC (National Guideline Clearinghouse), GIN (Guidelines International Network), NICE (National Institute for Health and Clinical Excellence) from inception to December 2013. The methodological quality of included guidelines were evaluated according to the AGREE Ⅱ instrument, and the differences between recommendations were compared. ResultsA total of 9 guidelines concerning ADHD in children and adolescents were included, with development time ranging from 2004 to 2012. Among 9 guidelines, 4 were made by the USA, 3 in Europe and 2 by UK. The levels of recommendations were Level A for 2 guidelines, and Level B for 7 guidelines. The scores of guidelines according to the domains of AGREE Ⅱ decreased from "clarity of presentations", "scope and purpose", "participants", "applicability", "rigour of development" and "editorial independence". Three evidence-based guidelines scored the top three in the domain of "rigour of development". There were slightly differences in the recommendations of different guidelines. ConclusionThe overall methodological quality of ADHD guidelines is suboptimal in different countries or regions. The 6 domains involving 23 items in AGREE Ⅱ vary with scores, while the scores of evidence-base guidelines are higher than those of non-evidence-based guidelines. The guidelines on ADHD in children and adolescents should be improved in "rigour of development" and "applicability" in future. Conflicts of interest should be addressed. And the guidelines are recommended to be developed on the basis of methods of evidence-based medicine, and best evidence is recommended.
Objective To assess atomoxetine and methylphenidate therapy for attention- deficit/ hyperactivity disorder (ADHD) .Methods We electronically searched the Cochrane Library (Issue 2, 2008), PubMed (1970 to 2008), MEDLINE (1971 to 2008), EMbase (1971 to 2008), Medscape (1990 to 2008), CBM (1978 to 2008), and NRR (1950 to 2008). We also hand-searched some published and unpublished references. Two independent reviewers extracted data. Quality was assessed by the Cochrane Reviewer’s Handbook 4.0. Meta-analysis was conducted by The Cochrane Collaboration’s RevMan 4.2.8 software. Results We finally identified 3 randomized controlled trials that were relevant to the study. Treatment response (reducing ADHD-RS Inattention subscale score) was significantly greater for patients in the methylphenidate group than in the atomoxetine group with WMD= – 1.79 and 95%CI – 2.22 to 1.35 (Plt;0.000 01). There was no statistical difference in other outcome measures between two groups (Pgt;0.05). Conclusions The effectiveness and tolerance of methylphenidate and atomoxetine are similar in treatment of ADHD. Further large randomized, double blind, placebocontrolled trials with end-point outcome measures in long-term safety and efficacy are needed.
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neuro-developmental disorders occurring in childhood, characterized by symptoms of age-inappropriate inattention, hyperactivity/impulsivity, and the prevalence is higher in boys. Although gray matter volume deficits have been frequently reported for ADHD children via structural magnetic resonance imaging, few of them had specifically focused on male patients. The present study aimed to explore the alterations of gray matter volumes in medicated-naive boys with ADHD via a relatively new voxel-based morphometry technique. According to the criteria of DSM-IV-TR, 43 medicated-naive ADHD boys and 44 age-matched healthy boys were recruited. The magnetic resonance image (MRI) scan was performed via a 3T MRI system with three-dimensional (3D) spoiled gradient recalled echo (SPGR) sequence. Voxel-based morphometry with diffeomorphic anatomical registration through exponentiated lie algebra in SPM8 was used to preprocess the 3D T1-weighted images. To identify gray matter volume differences between the ADHD and the controls, voxel-based analysis of whole brain gray matter volumes between two groups were done via two sample t-test in SPM8 with age as covariate, threshold at P＜0.001. Finally, compared to the controls, significantly reduced gray matter volumes were identified in the right orbitofrontal cortex (peak coordinates [-2,52,-25], t=4.01), and bilateral hippocampus (Left: peak coordinates [14,0,-18], t=3.61; Right: peak coordinates [-14,15,-28], t=3.64) of ADHD boys. Our results demonstrated obvious reduction of whole brain gray matter volumes in right orbitofrontal cortex and bilateral hippocampus in boys with ADHD. This suggests that the abnormalities of prefrontal-hippocam-pus circuit may be the underlying cause of the cognitive dysfunction and abnormal behavioral inhibition in medicated-naive boys with ADHD.
This study aims to explore the differences of event related potential (ERP) between attention deficit hyperactivity disorder (ADHD) and normal children, so that these differences provide scientific basis for the diagnosis of ADHD. Eight children were identified to be ADHD group by the diagnostic criteria of DSM IV (diagnostic and statistical manual of mental disorders IV), and the control group also consisted of 8 normal children. Modified visual continuous performance test (CPT) was used as the experiment paradigm. The experiment included two major conditions, i.e. Go and NoGo. All the 16 subjects participated in the study. A high density EEG acquisition instrument was used to record the EEG signal and processed these EEG data by means of ERP and spectrum analysis. P2 N2 peak peak value and spectral peak around 11 Hz were analyzed between ADHD subjects and those in the control group, and then statistical tests were applied to these two groups. Results showed that: ① Under the condition of Go, ADHD group had a significant lower P2 N2 peak peak value than the values in the control group ( P< 0.05); but under the condition of NoGo there was no significant difference in between. ② Compared with the control group, the ADHD group had significant lower spectral amplitude around 11 Hz under the condition of NoGo ( P< 0.05). However, under the condition of Go the difference was insignificant. In conclusion, there is certain cognitive dysfunction in ADHD children. P2-N2 peak-peak value and spectral peak around 11 Hz could be considered as clinical evaluation indexes of ADHD children′s cognitive function. These two objective indexes provide an early diagnosis and effective treatment of ADHD .
Attention deficit/hyperactivity disorder (ADHD) is a behavioral disorder syndrome found mainly in school-age population. At present, the diagnosis of ADHD mainly depends on the subjective methods, leading to the high rate of misdiagnosis and missed-diagnosis. To solve these problems, we proposed an algorithm for classifying ADHD objectively based on convolutional neural network. At first, preprocessing steps, including skull stripping, Gaussian kernel smoothing, et al., were applied to brain magnetic resonance imaging (MRI). Then, coarse segmentation was used for selecting the right caudate nucleus, left precuneus, and left superior frontal gyrus region. Finally, a 3 level convolutional neural network was used for classification. Experimental results showed that the proposed algorithm was capable of classifying ADHD and normal groups effectively, the classification accuracies obtained by the right caudate nucleus and the left precuneus brain regions were greater than the highest classification accuracy (62.52%) in the ADHD-200 competition, and among 3 brain regions in ADHD and the normal groups, the classification accuracy from the right caudate nucleus was the highest. It is well concluded that the method for classification of ADHD and normal groups proposed in this paper utilizing the coarse segmentation and deep learning is a useful method for the purpose. The classification accuracy of the proposed method is high, and the calculation is simple. And the method is able to extract the unobvious image features better, and can overcome the shortcomings of traditional methods of MRI brain area segmentation, which are time-consuming and highly complicate. The method provides an objective diagnosis approach for ADHD.