Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent, inheritable, and heterogeneous neurodevelopmental disorder. Children with a family history of ADHD are at elevated risk of having ADHD and persisting its symptoms into adulthood. The objective of this study was to investigate the influence of having or not having positive family risk factor in the neuroanatomy of the brain in children with ADHD. Cortical thickness-, surface area-, and volume-based measures were extracted and compared in a total of 606 participants, including 132, 165, and 309 in groups of familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), and typically developed children, respectively. Compared to controls, ADHD probands showed significantly reduced gray matter surface area in the left cuneus. Among the ADHD subgroups, ADHD-F showed significantly increased gray matter volume in the right thalamus and significantly thinner cortical thickness in the right pars orbitalis. Among ADHD-F, an increased volume of the right thalamus was significantly correlated with a reduced DSM-oriented t-score for ADHD problems. The findings of this study may suggest that a positive family history of ADHD is associated with the structural abnormalities in the thalamus and inferior frontal gyrus; these anatomical abnormalities may significantly contribute to the emergence of ADHD symptoms.  Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent and persistent neurodevelopmental disorders that affects approximately 5% of children worldwide ,2. ThereExisting structural neuroimaging studies in ADHD have shown widely inconsistent results. Several studies reported a lower cortical thickness in the pars opercularis, fusiform, parahippocampal, precentral gyri, temporal pole, and cingulate cortex ,19,20, aWhile there is a large quantity of literature on neural correlation of ADHD symptoms in samples of children/adults with ADHD compared to healthy control groups, there are notably less neuroanatomical imaging studies focused on the familial risk factor that may represent biologically distinct subgroups of ADHD ,38,39. SIn the present study, we proposed to investigate the neuroanatomical substrates and their clinical/behavioral linkages associated with ADHD-F vs. ADHD-NF in a large sample of three independent groups of children . On the basis of findings from our existing neuroimaging and clinical studies ,50,51,52All subjects were obtained from the baseline data in the Adolescent Brain Cognitive Development (ABCD) Study Release 4.0, a large study that took part in a major collaboration between multiple sites across the United States. All participants in the ABCD study were recruited from schools with diverse demographic factors, with the initial recruitment resulting in 11,875 children. All the parents provided written informed consent and the informed assent was obtained from children. The data were downloaded from the National Institute of Mental Health Data Archive. Participants were included in this study if they were in age ranged 9–10 and had Intelligence Quotient (IQ) higher than 80.  was used to assess the ADHD symptomatology in the ABCD study ,58. In tThe short version of the Edinburgh handedness inventory was used to assess a handedness score of “right”, “both”, or “left” based on 4 questions about preferred hand using for writing, throwing, using a spoon, and using a toothbrush . Due to 3; flip angle = 8°; repetition time/echo time for Siemens, Philips, and GE scanners were 2500/2.88, 6.31/2.9, and 2500/2, respectively; field of view = 256 × 256 mm2 for Siemens and GE scanners and 256 × 240 mm2 for Philips scanner. The ABCD neuroimaging data acquisition protocols and parameters have been previously published in detail . Brieflyhttps://surfer.nmr.mgh.harvard.edu (accessed on 13 July 2021) [All participants who had T1-weighted structural images with moderate to severe motion or visible artifacts were excluded from further analyses after careful visual inspection. Three-dimensional cortical reconstruction and segmentation for thickness and area estimations using T1-weighted data were performed utilizing Freesurfer version 7.2.0 (ly 2021) ,67. IndiAfter applying brain masks, each voxel was categorized as white matter or non-white matter based on intensity and estimated bias field. Next, in the white surface formation stage, Freesurfer refined generated surfaces for hemispheres by following intensity gradients between white matter and gray matter. Then, at the pial surface formation, Freesurfer nudged white surface to follow the intensity gradients between gray matter and cerebrospinal fluid. The cortical thickness in each of 68 bilateral cortical regions was then estimated based on the distance between white and pial surfaces at each location. The border surface between gray matter and white matter was inflated into a sphere, and then surface-based labeling was used to segment both left and right hemispheres into different sub-regions in each of 68 bilateral cortical regions. Thirty-seven subcortical structures/nuclei regions’ volumetric segmentations were performed at several stages including affine registration, initial volumetric labeling, inhomogeneities correction, volumetric alignment to the MNI305 atlas, and final segmentation.t-tests were used for continuous variables (displayed as the mean and standard deviation). Chi-square tests were used for categorical variables (displayed as number and frequencies).SPSS  was used to compare clinical, neurocognitive, and demographic measures of the participants between groups of controls and ADHD probands, as well as between the two ADHD subgroups (ADHD-F and ADHD-NF). Independent sample p-values and control potential false positive results of these group comparisons. Additionally, full model analyses were conducted  was used to compare the structural T1 neuroimaging measures including regional cortical thickness and surface area of 68 cortical regions and volume of 37 subcortical structures/nuclei between the groups of TDC and ADHD probands and further between the subgroups of ADHD-F and ADHD-NF. Age and sex were used as covariates; we also added estimated total intracranial volume (ETIV) as covariate for volume and surface area because they scale with size of the head . Bonferrshown in  to invesp-values and control potential false positive results of these comparisons.Pearson correlation was performed to analyze whether the neuroanatomical brain measures that showed between-group differences were correlated with the clinical symptom measures (the raw scores for inattentive and hyperactivity–impulsivity symptoms score collected from KSADS-5 and DSM-oriented t-scores collected from CBCL). Again, Bonferroni correction method was used to adjust The final sample used in the main analysis comprised a total of 606 participants including 309, 165, and 132 in groups of TDC, ADHD-NF, and ADHD-F, respectively. Participants’ demographic information and corresponding group statistical comparisons are shown in p = 0.038) and significantly increased surface areas in the right middle temporal . There were no significant findings for cortical thickness or volume between groups of TDC and ADHD ; significantly increased surface area in the left inferior temporal  and left middle temporal  lobes; significantly greater volume in the right thalamus   .2 = 0.039, p = 0.023)  . No signHere, we present a highly powered study of cortical and subcortical structural brain differences between the subgroups of familial and non-familial of ADHD probands in children, as well as between ADHD and TDC groups. Compared to controls, the ADHD group had a significantly reduced surface area of the cuneus in the left hemisphere. The cuneus is one of the critical regions in processing visual information and has been suggested to be part of the default mode network ,72,73. SWithin the probands, our findings showed significantly increased gray matter volume of the right thalamus in the ADHD-F group compared to the ADHD-NF group. Furthermore, the increased volume of the right thalamus was significantly correlated with the reduced t-score of ADHD problems from DSM-oriented CBCL scales. Thalamus is one of the critical components of the Cortico-Striato-Thalamo-Cortical (CSTC) loops which subserve attention and cognitive processing ,85,86. IFurthermore, within the ADHD probands, we found that children with ADHD-F had significantly thinner cortical thicknesses in the right pars orbitalis compared to children with ADHD-NF. Right pars orbitalis, as a part of the right inferior frontal gyrus (rIFG), plays a key role in behavioral responses inhibition ,95. It hThe present study has a few limitations. One limitation of the study is that the ABCD study only included the ADHD assessment for one of the parents. To minimize the confounding effects in the ADHD-NF group, we further excluded subjects that had any potentially ADHD-related family histories based on the collected family history questions. Another limitation is that the parametric statistical models used in this study have a limited capacity to investigate multiple variables and their interactions simultaneously. Future studies using machine learning methods may effectively extract the high-dimensional information and translate the complex neuroimaging patterns into clinical relevance. Finally, the cross-sectional study design of this study only focused on understanding the anatomical alterations and their interactions with the ADHD symptoms in children with ADHD-F vs. ADHD-NF at 9–10 years of age. Future research with a longitudinal study design will facilitate the understanding of how these common and distinct neuroanatomical features in children with ADHD-F vs. ADHD-NF grow along with age and whether the ADHD-F-specific childhood neural markers  in affected individuals have an important predictive value for future clinical outcomes associated with the persistence/remission of ADHD symptoms.In conclusion, the current study found that relative to group-matched controls, school-aged children with both ADHD-F and ADHD-NF had common gray matter structural abnormalities associated with the cuneus and temporal lobe. Whereas, within the ADHD probands, children with ADHD-F showed a significantly increased volume of the right thalamus and decreased cortical thickness of the right IFG when compared to ADHD-NF; the volume of the right thalamus showed significant negative correlation with the severity of ADHD symptoms  in children with ADHD-F, but not in those with ADHD-NF. These findings resulted from analyses of a big sample of children from community-based recruitment. Therefore, findings reported in our study are statistically much more powerful than those reported in studies with small sample sizes. Meanwhile, this is the first study in the field investigating neuroanatomical substrates of ADHD-F vs. ADHD-NF in two large independent subgroups of ADHD in children. The results suggest that ADHD-F may represent a biologically more homogeneous subgroup of ADHD. These findings have the potential in guiding future studies with longitudinal designs to understand the developmental trajectories of these ADHD-F-specific neural markers and their roles in clinical outcomes associated with the persistence/remission of ADHD symptoms in affected individuals.


Attention deficit hyperactivity disorder (ADHD) patients show, as one of the main symptoms, an attentional impairment. Selective attention in the hearing process is the ability to understand speech in a noisy environment, which can be evaluated by several methods. One of the main approaches is the functioning of the Medial Olivocochlear Efferent System, which can be accessed by Transient-Evoked Otoacoustic Emissions (TOAE).This study aimed at evaluating the suppression effect of contralateral noise on TOAE in ADHD (study group) and normal subjects (control group). Study Design: Case-control study.A study with 20 children distributed in two, age- and gender-matched groups. Results: No differences were found in TOAE responses between the two groups, with and without noise.We conclude that there were no functional differences in the Medial Olivocochlear Efferent System in the two groups analyzed. American Psychiatric Association - APAThe Attention Deficit/Hyperactivity Disorder (ADHD) is described by the Children with ADHD have attention deficit as one of their main symptoms. Attention plays a role in the selection of some stimuli, providing the better processing of them. As far as hearing is concerned, selective attention is the ability to understand speech in noisy environments, and it can be assessed in different ways – one of them is the workings of the Medial Olivocochlear System Efferent.The Medial Olivocochlear System Efferent has been studied especially because of its role in selective attention. This system can be assessed by the Otoacoustic Emissions Test, which involves low intensity sounds captured in the external acoustic meatus, associated with the mobility and mechanical ability of the external hair cells, apparently managed by the efferent pathways of the olivocochlear system. Otoacoustic emissions may be evoked by transient stimuli (click) which, because of having a broad array of frequencies, stimulates the entire cochlea (TEOAE).Contralateral noise affects the transient evoked otoacoustic emissions (TEOAE) and it activates the efferent paths of the olivocochlear emissions, impacting the cochlear process responsible for the generation of the TEOAE, which represents suppression. This suppression effect was noticed by numerous authors such as the reduction in the response level of spontaneous and evoked otoacoustic emissions with the use of contralateral acoustic stimulationRyan et al.Some functions of the medial olivocochlear efferent system involve its role in the efficiency of signal capture in the presence of simultaneous noise, in the protection against lesions caused by high noise, in the control of the cochlear mechanical status and in auditory attentionPillsbury et al.Since there is a possibility of the medial olivocochlear efferent system be related to the ability of selective attention there was an interest in studying the function of such system in children diagnosed with ADHD. Moreover, there is a scarcity of papers associating this suppression effect in ADHD patients.The present study aimed at comparing, by means of TEOAE, the suppression effect of the medial olivocochlear efferent system by contralateral auditory stimulation in normal children and in those with ADHD.We recruited 30 children, of whom 20 participated in the study. They were divided in two groups, one made up of 10 children with ADHD and another made up of 10 children without ADHD (Control). The study was of the case-control type. The two groups were paired by gender and age. Each group was made up by three female and seven male children. The mean age of the children in the group was 9.8 years (Standard Deviation (SD) = 1.8 years) and that of the control group was 9.7 years (SD = 1.9 years). The study was carried out in the university clinic, after its approval by the Ethics in Research Committee of the Institution (Protocol # 0398/03) and the Informed Consent Form was signed by the guardians of the children from both groups.As main complaints, the participants had difficulties in paying attention and, after having been diagnosed by a pediatric neurologist as having ADHD using the diagnostic criteria of DSM IV, they were referred for assessment by TEOAE. The group inclusion criteria were: otoscopy, immittance, normal bilateral tonal audiometry and the presence of TEOAE. The inclusion criteria for the ADHD group were the same inclusion criteria used for the Control group; however, its participants had a diagnosis of ADHD without the use of medication to treat the disorder. Exclusion criteria for both groups were: changes found in otoscopy, immittance measures, tonal audiometry and no TEOAE.TEOAE measures were carried out using the ILO 96 cochlear emissions analyzer, from OTODYNAMICS, version 5.60, with the help of a probe fit to the external acoustic meatus of the subject, in an acoustic booth with sound insulation. We used a non-linear click stimulus in dBSPL, with regular 80 µs pulses, of rarefaction polarity, at a frequency of 50 Hertz (Hz). The stimulus intensity was 75 to 83 dBSPL, in sync with the procedures used by other authorsThe response acceptance criteria were chosen according to the criteria adopted in the current literature, and were 70% higher in percentage of reproducibility valuesWe used TEOAE, in the absence and presence of contralateral white noise, in both ears of all individuals in the sample. The responses were considered present when the signal/noise ratio was higher than 3 dB in the frequencies of 1000 Hz, 1500 Hz, 2000 Hz, 3000 Hz and 4000 Hz. In order to observe the suppression effect, we recorded the TEOAE with and without noise and we assessed the occurrence of changes in the TEOAE response level. We considered the presence of the suppression effect when there was a reduction greater than or equal to 1 dB from the response level with contralateral white noise.For statistical purposes, we used the Wilcoxon, Mann Whitney Y and Equality of Two Proportions tests, adopting a significance level of 0.05%.In order to better understand the results obtained, their descriptions were organized in two parts, first checking the ear effect and, following, the group effect and stimulation settings.The result shows the comparison between the Right (RE) and Left (LE) ears for the study  and contSince we found no statistically significant difference between the ears in relation to the variables, the analysis started to be done together for both ears.At this stage, we tried to see whether there was any difference in TEOAE responses without and with noise between the children of the control and those of the study group. There were no statistically significant differences between the groups. The results from this analysis are depicted on Following that, we tried to see if TEOAE differences without and with noise were different between the Study and Control groups. There were statistically significant differences between the groups. The results from this analysis are depicted on We tried to investigate the quantification of the suppression effect for the right ear, left ear, and for both ears, from the study and control groups. There was no statistically significant difference between the groups. The results from these analyses are presented on The present study included ten children with ADHD - seven (70%) were boys, pointing to a greater prevalence of this disorder among malesInitially, we tried to check whether the TEOAE without and with noise and the differences obtained without and with noise varied in relation to ear side (RE x LE), in both groups. There was no statistically significant difference in the Study group  or in thWe investigated if the TEOAE amplitudes without and with noise varied between the two groups . The resFollowing that, we noticed that the values of the differences obtained without and with noise varied between the groups. There were no statistically significant differences between the groups , corroboThe quantification of the suppression effect was studied for both ears separately. The statistical analysis carried out for the Right Ear (RE), Left Ear (LE) and both ears (RE+LE) did not show statistically significant differences between the groups studied . AlthougAccording to the results fond in our study, they suggest there were no significant difference in the workings of the medial olivocochlear efferent system in the group with ADHD children, when compared to children of the same age and without such diagnosis. With our findings we can infer that the priority attentional involvement in ADHD patients are not of auditory sensorial mode; however, in order to confirm this hypothesis it would be important to broaden the number of cases studied.We may also question whether auditory attention complaints in ADHD patients are associated with the existence of comorbid disorders, such as, for example, Central Auditory Processing Disorder, having that in many patients with ADHD this comorbidity was presentCurrent literature shows a straight relationship of the superior olivary complex with the ability of sound location in spaceWe did the present study starting from the principle that one of the ways used to assess Auditory Selective Attention happens through the analysis of the Medial Olivocochlear efferent system and that children with ADHD had important loss in attention.As we studied the functioning of this system in ADHD children and in normal children, we could conclude that the TEOAE and the differences obtained without and with noise were similar in both groups studied. Thus, it has been suggested that there are no differences in the functioning of the medial olivocochlear efferent system in the children with attention deficit hyperactivity disorder, when compared to normal children; however, there is a need to go more in depth in this hypothesis by means of other methods capable of assessing the auditory function of ADHD patients such as, for example, the use of behavioral measures.The suppression effect which assesses the functioning of the medial olivocochlear efferent system by TEOAE with and without noise in children with ADHD did not show any difference when compared to the Control group in the present study. We did not find evidence, in this sample, that the medial olivocochlear efferent system is impacted in the presence of ADHD.


Eight species became progressively attenuated or enriched when comparing the compositions of HCs to those of I-ADHD and C-ADHD; in particular, the abundance of Bacteroides ovatus was depleted in patients with C-ADHD. In turn, Bacteroides ovatus supplementation ameliorated spatial working memory deficits and reversed θ electroencephalogram rhythm alterations in ADHD rats. In addition, Bacteroides ovatus induced enhanced neuronal activation in the hippocampal CA1 subregion. These findings indicate that gut microbial characteristics that are unique to patients with C-ADHD may be masked when considering a more heterogeneous group of patients. We link the gut microbiota to brain function in an ADHD animal model, suggesting the relevance of testing a potential bacteria-based intervention for some aspects of ADHD.Attention-deficit/hyperactivity disorder (ADHD) is a highly heterogeneous psychiatric disorder that can have three phenotypical presentations: inattentive (I-ADHD), hyperactive-impulsive (HI-ADHD), and combined (C-ADHD). Environmental factors correlated with the gut microbiota community have been implicated in the development of ADHD. However, whether different ADHD symptomatic presentations are associated with distinct microbiota compositions and whether patients could benefit from the correction of aberrant bacterial colonization are still largely unclear. We carried out metagenomic shotgun analysis with 207 human fecal samples to characterize the gut microbial profiles of patients with ADHD grouped according to their phenotypical presentation. Then, we transplanted the candidate low-abundance bacteria identified in patient subgroups into ADHD rats and evaluated ADHD-associated behaviors and neuronal activation in these rats. Patients with C-ADHD had a different gut microbial composition from that of healthy controls (HCs) ( Since there are highly heterogeneous neurobehavioral deficits among patients with different ADHD symptomatic profiles, relevant features may be missed when trying to distinguish the bacterial taxa between a mixed and heterogeneous group of patients and HCs. In addition, the 16S rRNA gene amplicon sequencing approach may omit some key information owing to the limited taxonomic and functional resolution level.Studies based on 16S rRNA gene amplicon sequencing have preliminarily identified that the compositions of the gut microbiota in patients with ADHD and healthy controls (HCs) are different.14 There are also some probiotic interventions that are effective for reducing the risk of ADHD.27 However, the identity and functionality of the specific gut bacteria responsible for alleviating the abnormal behaviors of the host are still largely unknown.In addition, it was reported that fecal transplantation of the gut microbiota from patients with ADHD induces alterations in brain structure and function in germ-free recipient mice.Bacteroides ovatus, that was identified in patients with C-ADHD into spontaneously hypertensive rats (SHRs), an animal model of ADHD, to determine its effects on complex host behaviors and general brain function.Therefore, in this study, shotgun metagenomics sequencing was performed to assess the differences in the composition and function of the gut microbiota between ADHD patient subgroups. We also transplanted an attenuated bacterial species, A total of 207 Chinese children and adolescents, consisting of 98 patients with ADHD  and 109 HCs , were recruited. All case and control samples were collected at Xijing Hospital, Shaanxi, China, between March 20, 2018, and February 27, 2020. Patients were diagnosed and grouped by two experienced child psychiatrists using a structured diagnostic interview conducted according to the criteria of the clinical Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) and the guidelines of the Chinese classification of mental disorders (CCMD-3). The numbers of participants in the three different subgroups were 38 I-ADHD patients, 53 C-ADHD patients and 7 HI-ADHD patients. Patients with ADHD and HCs had to meet the following criteria: (1) age between 6 and 15 years; (2) no use of any antibiotic treatment for at least three months before sample collection; and (3) no history of treatment with any medication for ADHD. Participants who had other psychiatric or neurological diseases and any gastrointestinal or metabolic disorders were excluded. Children and adolescents whose IQs were below 70 according to the Wechsler Intelligence Scale were also excluded. All participants provided written informed consent, and the study was approved by the ethics committee of Xijing Hospital, Fourth Military Medical University (ID: KY20182002-1). The present study was registered at ClinicalTrials.gov (ID: NCT03447223).28 After transport on dry ice, fecal samples were stored at −80°C until DNA extraction. Total bacterial DNA from each fecal sample was extracted from ~200 mg of stool with the NucleoSpin® Soil kit  according to the manufacturer’s instructions. A Qubit  and 1% agarose gel electrophoresis were used to analyze the quality of DNA. The details of DNA library construction were as follows: 1 μg genomic DNA was randomly fragmented to approximately 200–500 bp by sonication , and the fragmented DNA was assessed via gel-electrophotometry and then purified with an AxyPrep Mag PCR Clean-up kit . The fragmented DNA was combined with end repair mix, incubated at 20°C for 30 min and then purified. The repaired DNAs were combined with A-tailing mix and incubated at 37°C for 30 min. Illumina adapters were ligated to the adenylated 3’ ends of the DNA fragments, which were then incubated at 16°C for 16 h and purified. Several rounds of PCR amplification with PCR primer cocktail and PCR master mix were performed to enrich for the adapter-ligated DNA fragments. The final DNA libraries were assessed for the average insert size using an Agilent 2100 Bioanalyzer  and quantified by an ABI StepOnePlus Real-Time PCR system .Fresh feces samples (approximately 0.5 g) were immediately transferred into a sterilized collection tube with a sterilized wooden stick from a clean toilet by the guardians of patients and HCs. The preservation method included the use of a reagent containing imidazolium-based ionic liquid.29 and 1,486.5 Gb of high-quality PE reads were acquired for the 207 samples, with an average of 7.18 Gb per sample. To remove contamination from human host DNA, reads were aligned to the human genome reference (hg19) by SOAPaligner ,30 and the mapping reads were discarded. The average rate of host contamination was 1.25 ± 5.23%.Samples were sequenced on the Illumina HiSeq X Ten platform with an insert size of 300 bp . Before further bioinformatic analysis, raw reads containing adapter sequences, low-quality reads (lower Q-score 20 rate more than 50%) and ambiguous bases (N base rate more than 5%) were filtered out with SOAPnuke,31 which uses ~1 M unique clade-specific marker genes  to estimate the relative abundances of bacterial taxa. The parameters of MetaPhlAn2 were set as ‘–nproc 10 – stat avg_g – ignore_viruses – ignore_eukaryotes – ignore_archaea’. Then, all sample profiles were merged using merge_metaphlan_tables.py.The profile of the microbial composition for each sample was calculated using MetaPhlAn2 (v2.0),32 In brief, HUMAnN2 was used to rapidly identify the known microbial species in the samples with MetaPhlAn2 and then to establish a customized pangenome database in which all genomes were preestablished and functionally annotated. Sample reads were mapped to this database with Bowtie2,33 with the unmapped reads translated and mapped to a protein database (UniRef90)34 with Diamond.35 Finally, all mapping reads were used to estimate gene family abundance and then annotated to metabolic enzymes to reconstruct and quantify metabolic pathways (MetaCyc).36 HUMAnN2 was run by the default parameters. All sample profiles were merged and renormalized using humann2_join_tables and humann2_renorm_table, respectively.Functional profiling for each sample was performed using HUMAnN2 (v0.11.2).The metagenomic shotgun sequencing data for all samples have been deposited in the CNGB Nucleotide Sequence Archive (CNSA) under accession code CNP0000729.37To select biomarkers that could be used to discriminate subgroup patients and HCs, 5 trials of the 10-fold cross-validation were performed on a random forest model (randomForest 4.6–14 package) using the relative abundance profiles of all bacterial species (399 species), and the result was assessed by receiver operating characteristic (ROC) curves using the pROC package in R. The area under the curve (AUC) is a convenient tool for comparing and validating classifiers, and values of 0.90–1.00 are excellent, 0.80–0.89 are good, 0.70–0.79 are fair, and < 0.70 are poor.Bacteroides ovatus (B. ovatus) ATCC 8483 was obtained from BeNa Culture Collection  and cultured anaerobically with liquid thioglycolate medium  at 37°C. Escherichia coli (E. coli) AM12-30 was isolated from fecal samples of healthy volunteers by Beijing Genomics Institute  and cultured with Luria-Bertani medium under anaerobic conditions at 37°C.38 The OD600 was measured each day to assess the log growth phase of the bacterial strain. Then, the medium was centrifuged at 8000 rpm for 5 min to collect the bacteria, and sterile PBS was used to wash the bacteria twice. Subsequently, the prepared bacterial suspension was diluted with saline to 3 × 109 cfu/ml, and 10 μl/g (body weight of rats) of oral gavage was administered with 40–50 ml/rat/day of drinking water.Male SHRs (4 weeks old) were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. and allowed to adapt to the standard housing environment  with a standard animal diet administered for a week.B. ovatus. Rats in the ABX+Saline and ABX+B. ovatus groups were treated with an antibiotic cocktail for the first 10 days and then saline or B. ovatus for the next 14 days via oral gavage. Rats in the saline group received a gavage with saline for a total of 24 days. The specific dosage of antibiotic was determined according to a previous study.39 In the first three days, rats were treated with amphotericin-B (1 mg/kg) daily by oral gavage. Then, an antibiotic cocktail  was given daily by oral gavage for 7 days, and ampicillin (1 g/L) was supplemented in drinking water. The gavage volume was 4 ml/kg (body weight of rats). All antibiotics were purchased from Beijing Solarbio Science & Technology Co., Ltd. . We prepared a fresh antibiotic mixture for gavage and drinking water every day. After 48 h, fresh saline or B. ovatus was given at 3 × 109 cfu/ml and 10 μl/g (body weight of rats) to remodel the gut microbiota once a day for 14 consecutive days. During the behavioral experiments, saline or B. ovatus was further given in drinking water to maintain effective colonization. Rats were weighed daily to assess their health and calculate the gavage volume.For the behavioral experiments, the rats were randomly divided into three groups : Saline, ABX+Saline, and ABX+B. ovatus. Then, electrodes were implanted in the brain of each rat for future EEG detection. After one week of recovery, rats were treated with an antibiotic cocktail for 10 days and then saline or B. ovatus for the next 14 days via oral gavage. Saline or B. ovatus was further given in drinking water to maintain effective colonization during the EEG recording period.For the electroencephalogram (EEG) rhythm recording, rats were divided into two groups : saline and The rat experiments were approved by the Animal Care Committee of Fourth Military Medical University (ID: 20171201). We made all efforts to minimize the number of rats used and their suffering.Rats were handled each day during oral gavage to familiarize them with the operator and relax them. All rats were placed on the same rack. Behavioral testing was performed 24 h after the last gavage. Data were analyzed by professional analysis software .In the open field test, the rats were quickly placed in the open field (100 cm x 100 cm x 50 cm). The locomotor activity track of each rat was then recorded for 30 min, and the total movement distance and time spent in the central area were analyzed. Before the next assessment, the feces and urine were removed from the field, and the site was wiped with 75% ethanol to remove olfactory cues.For the novel object recognition test, two identical objects (A1 and A2) were placed in the open field apparatus (100 cm x 100 cm x 50 cm). Then, the rat was placed in the open field with its back to the two objects. Each rat was allowed to explore the objects for 10 min and then was placed back into its original cage. After 24 h, the test period of the experiment was carried out. An old object (A1) and a new object (B) were placed in the target area, and each rat was allowed to explore the objects for 10 min. The time spent exploring the old object and new object was recorded, and the recognition index was calculated. The recognition index equals the time spent exploring object B/(time spent exploring object A1 + time spent exploring object B).In the marble burying test, the rat was placed in a cage (25 cm x 40 cm x 18 cm) with a fresh pad of shavings (depth 5 cm) for 30 min. Then, the feces and urine were removed from the cage. The black marbles  were placed neatly on the padding in the same cage. The rat was allowed to explore the cage for another 30 min. Finally, two observers who were blinded to the groups were invited to count the number of buried marbles .In the Y Maze assessment, a Y-shaped apparatus with three black plastic arms, which marked A/B/C at a 120° angle from each other, was used, and the size of each arm was 50 cm x 16 cm x 42 cm. The rat was placed at the end of any arm and allowed to freely explore for 8 min. The camera system recorded the total number of entries and alternations between the three arms. When a rat entered three different arms consecutively without revisiting the same arm, it was defined as one complete spontaneous alternation. Finally, the alternation percentage  *100%) was calculated by SMART 3.0 software .In the elevated plus maze assessment, the rat was placed in the center of the elevated plus maze facing the open arm and allowed to freely explore for 5 min. The time spent on different arms and the number of entries into different arms were recorded and calculated by SMART 3.0 software .The rats were anesthetized with isoflurane (3% for induction and 1% for maintenance) and then fixed in the stereotaxic apparatus. After removing the skin and connective tissues of the head, we implanted Type B electrodes  into the parietal cortices of rats , and electromyography (EMG) electrode wires were implanted under the neck muscles. Then, the rats were allowed to recover for one week after the operation. The EEG recording was performed 24 h after the last oral gavage treatment. The EEG rhythms (50 Hz filtering) were recorded by Medusa  for 2 h in each rat. Frequency bands of θ (4–7.75 Hz) and sleep were further quantified. The EEG data were analyzed by investigators who were blinded to the groups.Ninety minutes after the Y maze test, the rats were euthanized and quickly sacrificed. The whole brain was fixed in 4% paraformaldehyde overnight, which was then replaced with 30% sucrose solution for dehydration. After 3–4 days, the brain sank to the bottom of the tube. The rat brain was then embedded with compound embedding agent  at an optimally low temperature used for cutting and stored at −80°C or immediately sliced.Coronal brain sections (30 μm) were prepared using a freezing microtome . Brain slices were washed 3 times with 1x PBS for 5 min each. Triton X-100 (0.3%) was added to the slices to induce membrane breakage at 37°C for 30 min. Then, the slices were blocked in 4% goat serum for 2 h at room temperature. The slices were incubated with the primary antibody  at 4°C overnight and washed with PBS 3 times for 5 min each. Subsequently, the slices were incubated with the secondary antibody  for 2 h at room temperature and washed with PBS 3 times for 5 min each. Images were obtained with an Olympus BX53 microscope (Japan).Before and after antibiotic cocktail treatment and bacterial transplantation, the fecal samples of each rat were collected at 9 AM, placed into sterile EP tubes, and stored at −80°C immediately. Then, the samples were transported to a company for sequencing. First, the microbial community genomic DNA was extracted with an E.Z.N.A.® soil DNA Kit , and the quality was determined by a NanoDrop 2000 UV–vis spectrophotometer  and agarose gel electrophoresis. The hypervariable V3-V4 region of the bacterial 16S rRNA gene was amplified with the primer pairs 338 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806 R (5’-GGACTACHVGGGTWTCTAAT-3’). Next, the purified amplicons were pooled for library construction and sequenced on an Illumina MiSeq PE300/NovaSeq PE250 platform  according to standard protocols. The sequencing data were processed and analyzed by QIIME2 and R packages. The sequencing analyses were performed by Wefind Biotechnology Co., Ltd. , and further data analysis was completed at PTM Biolabs Inc. .p value was obtained by 9,999 permutations. Supervised analysis with spare partial least-squares discriminant analysis (PLS-DA) was performed using the mixOmics package in R. Differential relative abundances of taxa and pathways were detected by the Wilcoxon rank-sum test and linear discriminant analysis (LDA) effect size (LEfSe),40 and the significance levels were set at a p value < .05 and LDA effect size > 2. All data from animal experiments are displayed as the mean ± SEM, and differences were considered significant when p < .05. The differences among groups were identified by 2-tailed Student’s t test or one-way ANOVA followed by the Tukey–Kramer post hoc test (GraphPad Prism 6.0 software).The alpha diversity of samples was estimated by the Shannon diversity index at the gene family level. Beta diversity between samples was estimated by the Bray–Curtis distance at the gene family level via the ‘vegdist’ function in the vegan R package. PERMANOVA based on the Bray–Curtis distance matrix was performed via the ‘adonis’ function from the R package vegan, and the permuted p = .007). The proportion of only children among the C-ADHD subgroup was lower than that among HCs . The percentage of cesarean deliveries among patients with I-ADHD was lower than that among HCs . Furthermore, as expected, low birth weight occurred more often in the C-ADHD subgroup than in HCs , and patients in the I-ADHD and C-ADHD groups had lower scores on the intelligence quotient (IQ) (p < .001). Predictably, the Diagnostic and Statistical Manual of Mental Disorders_Attention Deficits (DSM_AD) scores and DSM_Hyperactivity/Impulsivity Deficits (DSM_HD) scores were higher in patients with I-ADHD and C-ADHD than in HCs. Since the host characteristics were partly matched between subgroups, PERMANOVA was performed to explore the influence of these characteristics on gut microbiota (Table S1).A total of 207 children and adolescents, consisting of 98 patients with ADHD  and 109 HCs, were recruited. As the number of subjects with HI-ADHD was too low to permit the comparison of these patients with those of other subgroups and HCs, 38 I-ADHD, 53 C-ADHD and 109 HCs were ultimately included in the subgroup analysis. The general demographic characteristics of the recruited subjects are displayed in The general dietary and defecation habits among the I-ADHD, C-ADHD and HC groups are listed in Table S2 and Table S3. We found no significant differences among the three groups in any of the dietary or defecation habits, including infant feeding; the preference for side dishes, staple foods, yogurt, and other fermented food; and frequency, smoothness, and shape of bowel movements.p= .042), although the gut microbiota richness was similar between the two groups  belonged to the genus Bacteroides. Species in Bifidobacterium (breve and bifidum) and Prevotella  were more abundant in patients with ADHD than in HCs. A cladogram of significantly different taxa is shown in Metagenomic sequencing analysis (LEfSe) identified 12 bacterial taxa that were enriched in patients and 33 that were enriched in HCs . At the Moreover, we found a small number of viruses and eukaryotes in some individuals, but archaea were not detected in any individuals. There were no significant differences between the amounts of viruses and eukaryotes (Table S5).p = .44) than between patients with C-ADHD and HCs  or between patients with I-ADHD and C-ADHD (p = .519).The analysis of gene numbers in patient subgroups indicated that there was lower dissimilarity between patients with I-ADHD and HCs  identified 13 bacterial taxa that were enriched in patients with I-ADHD and 11 that were enriched in HCs when the two groups were contrasted . In addip = .057) and gut microbiota richness  in males were similar between the total ADHD patient cohort and HCs . The results of the comparison of the gene numbers (p = .05) and gut microbiota richness  in the male subjects of the C-ADHD and HCs were similar to the results obtained in the analyses performed with both sexes . PERMANOVA results showed a more significant difference in male samples between C-ADHD and HCs  than between the total ADHD cohort and HCs . These data suggest that unmatched sex did not alter the outcome of the analysis, in which greater variation in the gut microbiota between C-ADHD patients and HCs was demonstrated.Of note, there was a higher proportion of male subjects in the C-ADHD subgroup than in HCs. To address whether the differences in the gut microbiota exhibited in Rhizobiales, Oscillospiraceae, Bilophila, Oscillibacter, Subdoligranulum, Bacteroides cellulosilyticus, Bacteroides fluxus, Bacteroides nordii, Bacteroides ovatus, Lachnospiraceae bacterium, Bilophila wadsworthia, Oscillibacter unclassified, and Subdoligranulum unclassified. The other 5 bacterial taxa  were more abundant in patients with C-ADHD than in patients with I-ADHD or HCs. Together, these data provide preliminary evidence of greater dissimilarity in the gut microbial composition between patients with C-ADHD and HCs than between patients with I-ADHD and HCs. Notably, patients with C-ADHD can be distinguished from patients with I-ADHD by different gut microbiota profiles.We further compared the numbers of significantly different bacterial taxa among the three groups: C-ADHD vs. HCs (70), I-ADHD vs. C-ADHD (34), and I-ADHD vs. HCs (24) . The patPrevotella copri, Prevotella buccae and Bifidobacterium breve were progressively enriched from HCs to patients with I-ADHD and C-ADHD, while progressively reduced enrichment of the species ovatus, thetaiotaomicron, intestinalis, cellulosilyticus, and fluxus belonging to the genus Bacteroides was also identified. These results suggest that the progressively increased or reduced enrichment of microbial taxa may be associated with the different ADHD symptomatic profiles.In addition, the Jonckheere-Terpstra test identified a progressive prevalence or scarcity from HCs to patients with I-ADHD and C-ADHD . We founPrevotella buccae, Bifidobacterium breve, and Bifidobacterium bifidum, were enriched and positively associated with both DSM_HD and DSM_AD scores. Several species belonging to the genus Bacteroides that were abundant in HCs were negatively correlated with DSM_HD and/or DSM_AD scores. We found that increased relative abundances of Bacteroides nordii, Bacteroides cellulosilyticus and Bacteroides intestinalis were associated with fewer symptoms of both hyperactivity/impulsivity (DSM_HD scores) and inattention (DSM_AD scores). Bacteroides thetaiotaomicron and Bacteroides ovatus were negatively associated only with DSM_AD scores. Moreover, we repeated this analysis, excluding the healthy controls, to verify that the results of these clinical indices used to assess the correlation of the microbiome with clinical features would persist. The clinical index used to perform microbiome analysis in patients with ADHD suggested that a high abundance of Bacteroides was associated with reduced DSM scores. Alternatively, a high relative abundance of Prevotella correlated with increased DSM scores . These findings were very similar to the results of the initial analysis that included all groups, suggesting the significant role that Bacteroides and Prevotella play in ADHD.The associations between the composition of the gut microbiota and clinical symptoms in ADHD were also assessed. Spearman’s rank correlation analyses showed significant correlations of bacterial species with symptom severity scores . The speRandom forest (RF) classifications were established based on these differentiated gut bacterial species. The relative abundances of 6 bacterial species  distinguished the total ADHD patient cohort from the HCs  with an p < .05, LDA score > 2; Fig. S4A). LEfSe results revealed that only bacterial xylose degradation IV was significantly enriched in patients with ADHD, while the activation of bacterial pathways for inosine-5’-phosphate biosynthesis II, flavin biosynthesis III (fungi), L-phenylalanine degradation IV, adenine and adenosine salvage III, starch degradation V, hydrogen production VIII, purine ribonucleoside degradation and L-rhamnose degradation I was significantly reduced in patients with ADHD.Microbial metagenomic sequencing data were used to predict the discrepancies in the functional metabolic pathways in the total ADHD patient cohort and in patient subgroups. Altogether, 362 pathways from the MetaCyc metabolic pathway database that were present in more than 10% of samples were identified and analyzed. A total of 9 pathways were significantly different between the total ADHD patient cohort and HCs . The analysis also predicted that the I-ADHD subgroup exhibited higher activation of bacterial pathways of L-lysine, L-threonine and L-methionine biosynthesis I, anhydromuropeptide recycling, tetrahydrofolate biosynthesis and salvage and heme biosynthesis from glycine, and lower activation of pathways of pyrimidine ribonucleotide de novo biosynthesis, purine ribonucleoside degradation, GDP-mannose biosynthesis and stachyose degradation than those in the HCs. Of note, the Venn diagram illustrates that there is zero overlap in the different pathway profiles observed in C-ADHD vs. HCs and I-ADHD vs. HCs .16 Previous studies have shown that SHRs exhibit the core symptoms of ADHD, including but not limited to hyperactivity, attention deficits, and impulsivity.41 Here, we found that the abundance of Bacteroides ovatus was remarkably lower in patients with ADHD or C-ADHD than in HCs. Bacteroides ovatus was identified as the species that principally promoted the production of gut secretory IgA and maintained gut homeostasis.42Bacteroides ovatus was also found to consume gut tryptophan and produce indole-3-acetic acid, an important tryptophan metabolite,43 which drives hippocampal neurogenesis in adult mice.44 Therefore, we next wondered whether exogenous Bacteroides ovatus supplementation could affect brain function and ADHD-like behaviors in SHRs.Early life stage event-associated environmental factors may account for the diverse composition of the gut microbiota and the development of many psychiatric disorders via the microbiota-gut-brain axis.Bacteroides ovatus by oral gavage after administration of broad-spectrum antibiotic cocktail (ABX) . The ABX+B. ovatus-treated and ABX+Saline-treated SHRs showed restored Shannon diversity  and microbiome structure  unlike the saline-treated SHRs. The abundance of gut Bacteroides ovatus in SHRs was measured by amplifying its 16S rRNA gene . We found that Bacteroides ovatus treatment markedly rescued the spatial working memory and inattention of SHRs in the Y maze test unlike with saline treatment . Together, these results suggest that Bacteroides ovatus specifically modulates spatial working memory instead of recognition memory and that memory improvement is not dependent on altered locomotion or anxiety.Four-week-old SHRs were exposed to il (ABX) . We founted SHRs . Notablyted SHRs  and anxiBacteroides ovatus-mediated amelioration of spatial working memory in SHRs. EEG rhythms, representing gross brain activity, were recorded in SHRs after administration of Bacteroides ovatus  subregions after Bacteroides ovatus treatment  belonging to the genus Bacteroides in the total ADHD patient cohort. Members of the genus Bacteroides are usually beneficial for gut function and are correlated with neurodevelopment.50 In addition, species in Bifidobacterium (breve and bifidum) and Prevotella  were more abundant in the total ADHD patient cohort than in the HCs. Although we did not find considerable overlap with the previously reported microbial signature of ADHD, the increased abundance of Bifidobacterium breve and Bifidobacterium bifidum agreed with the results of a Dutch cohort study.21Notably, the bacterial taxa that are reported to be distinct between patients with ADHD and HCs were highly inconsistent in previous studies.51 Interestingly, the enrichment of the species Prevotella copri, Prevotella buccae and Bifidobacterium breve progressively increased, while that of ovatus, thetaiotaomicron, intestinalis, cellulosilyticus and fluxus belonging to the genus Bacteroides progressively decreased from HCs to patients with I-ADHD and C-ADHD. Moreover, Prevotella, Bifidobacterium and Bacteroides were also associated with hyperactivity/impulsivity and/or inattention symptoms. Taken together, these findings might indicate that there were distinguishable gut microbial patterns in the I-ADHD and C-ADHD subgroups. We can obtain more accurate information on the gut microbiota from subgroup analysis, which is potentially helpful in the diagnosis of ADHD subtypes. Notably, however, an increased abundance of Bacteroides ovatus was found in a general ADHD patient cohort in a previous small-sized study.24 The proportion of patients with C-ADHD in the general patient cohort may be involved in the discrepancy in Bacteroides ovatus abundance observed between the present study and the previous one.Regarding the ADHD subgroups, the number of bacteria taxa that were different between C-ADHD and HC was the largest, followed by that between I-ADHD and C-ADHD and that between I-ADHD and HC. We found a slight difference in the bacterial populations between I-ADHD patients and HCs, and we speculate that the relatively minor symptoms of I-ADHD may be involved in this difference. Thus, we identified several progressively enriched microbial taxa from HCs to patients with I-ADHD and C-ADHD, which is consistent with the clinical severity of ADHD.53 Here, the dietary patterns of participants do not significantly affect the composition of gut microbiota. However, the recording used to assess the food classes consumed by the patients in the present study was at a very rough level, which indicates a dietary preference for vegetables or whole grains.Some studies have analyzed the association between dietary habits and ADHD. The common finding is that unhealthy dietary patterns  are associated with ADHD.54 The starch degradation, hydrogen production and rhamnose degradation pathways were also predicted to exhibit decreased activation in patients, and these metabolic pathways were associated with host gut functions.55–57 Moreover, relatively enriched xylose degradation and reduced phenylalanine degradation pathways in patients with ADHD are particularly striking. The disturbance of xylose metabolism is implicated in Drosophila hyperactivity behavior.58 Phenylalanine is the precursor of dopamine, and the latter is well studied and known as a dominant neurotransmitter that is deficient in ADHD pathophysiology.60 Although very few functional analyses predicted bacterial profiles in previous studies, the increased levels of cyclohexadienyl dehydratase, responsible for phenylalanine synthesis, predicted in the study of the Dutch cohort21 and the current data together suggest a critical role of abnormal phenylalanine metabolism in patients with ADHD. Moreover, the genus Bifidobacterium has been reported to affect the level of cyclohexadienyl dehydratase.21 Microbial products and metabolites can signal through enteroendocrine cells and enterochromaffin cells to modulate the secretion of neuropeptides, neuromodulators, and neurotransmitters.61 Therefore, insufficient dopamine signals in the brain may induce potentially compensated precursor production through the microbiota-gut-brain axis.The functional metabolic pathways predicted from fecal metagenomic analysis exhibited additional divergence in patients with ADHD. We found that the activation of functions correlated with energy regulation in host metabolism, including inosine-5’-phosphate biosynthesis, flavin biosynthesis, adenine and adenosine salvage and purine ribonucleoside degradation, was reduced in ADHD patients. However, it is unclear whether these alterations contribute to abnormal host symptoms, although aberrant brain metabolism is involved in some psychiatric disorders.63 Pyridoxal 5’-phosphate is a dominant vitamin B6 active type, and serum vitamin B6 levels were found to be decreased in ADHD patients.64 Moreover, there was no overlap in the pathways that were different between C-ADHD and HCs and between I-ADHD and HCs, suggesting distinct gut bacterial functions in ADHD subgroups.The functional analysis performed in subgroups showed some shared and distinct differential metabolic pathways in patients with C-ADHD and the total patient cohort. Notably, the activation of arginine and pyridoxal 5’-phosphate biosynthesis pathways were specifically reduced in the C-ADHD subgroup. Arginine, a precursor of nitric oxide, is related to better memory and improved intestinal inflammation.65–67 the evidence from mechanistically supporting experiments to confirm the proposed link was weak, especially considering the results of host-based bacterial transplantation experiments. Interestingly, anxiety-like behavior was elicited in mice colonized with the fecal microbiota of patients with ADHD.14 Here, we identified 33 total ADHD-depleted and 53 C-ADHD-depleted gut bacteria. We focused on these microbes with decreased abundances and aimed to restore their abundances in ADHD rats. The abundance of Bacteroides ovatus was significantly lower in patients with ADHD or C-ADHD than in HCs. Bacteroides ovatus was identified as the species that produces gut secretory IgA and limits mucosal inflammation.42Bacteroides ovatus was also found to consume gut tryptophan and produce indole-3-acetic acid, and the latter drives adult hippocampal neurogenesis in mice.44 Therefore, Bacteroides ovatus colonization may benefit the regulation of circulatory inflammation and cerebral function, although this requires further experiments to assess. Bacteroides ovatus-monocolonized ADHD rats exhibited significantly increased spontaneous alternations in the Y maze task, indicating an improvement in spatial working memory and inattention. Of note, the abundance of Bacteroides ovatus was negatively associated with DSM_AD scores but not DSM_HD scores, which may underlie the unaltered locomotor activity in ADHD rats receiving Bacteroides ovatus colonization. Moreover, Bacteroides ovatus monotherapy was found to be superior to traditional fecal transplantation and multistrain bacteriotherapy  in a murine colitis model.68Although additional microbial sequencing data have been reported to discriminate gut bacterial alterations in patients with ADHD,Bacteroides ovatus colonization can normalize EEG rhythms and specifically activate hippocampal neurons in ADHD rats. Interestingly, the hippocampal volumes in patients with ADHD were observed to be lower than those in HCs, which has been repeatedly reported by brain image analysis;70 this finding suggests that Bacteroides ovatus supplementation contributed to ameliorating hippocampal function and hippocampus-associated spatial working memory in these ADHD rats.Here, we also found that ovatus, fragilis, thetaiotaomicron, intestinalis, cellulosilyticus, salyersiae, fluxus, and nordii) belonging to the genus Bacteroides in patients with ADHD. The different cocktail treatments incorporating these Bacteroides spp. may produce different effects on ADHD rats, which deserves further study.In addition, we found underrepresentation of 8 species (Bacteroides ovatus-mediated improvements in brain function deserve further study.The main strengths of the present study include the use of metagenomic sequencing, subgroup analyses and a larger sample size than those in previous studies. Second, only medication-naive patients with ADHD were recruited in the current study to exclude the effects of medication on the gut microbiota. Additionally, patients with psychiatric or gastrointestinal comorbidities were also excluded. Given that the gut microbiome composition is highly correlated with dietary habits, a questionnaire assessing general diet and defecation habits was collected from individual participants to assess dietary differences between groups. Third, to our knowledge, this is the first study using single bacterial species transplantation to link the gut-brain axis mechanism in ADHD. There are also some limitations of this study. First, distinct gut profile information for patients with HI-ADHD could not be obtained in our study due to an insufficient number of patients. Second, in line with a real-world scenario, the groups were not completely matched by sample size, sex, only child status, percentage of cesarean section and low birth weight, although we found that unmatched sex did not alter the outcome of the greater variation in gut microbiota observed between C-ADHD and HCs. Third, we performed a cross-sectional study, and further longitudinal work should be conducted to further assess age- or medication-associated variations in gut microbiota. Finally, the specific molecular and metabolic mechanisms underlying gut Bacteroides ovatus transplantation in ADHD rats rescued spatial working memory and attention-associated recognition function. The current study provides new evidence supporting that the microbiota-gut-brain axis is associated with ADHD.71In conclusion, our study characterized the distinct gut microbiota panel in patients with ADHD and its subgroups. We found more gut microbial alterations in patients with C-ADHD than in patients with I-ADHD. We also identified several progressively enriched or decreased microbial taxa from HC to I-ADHD and C-ADHD, which is consistent with the clinical severity of ADHD. Click here for additional data file.


Specifically, greater activity was exhibited in BA 31, precuneus, parietal lobe  at 156 ms post stimulation. No significant differences were found for any other comparisons. Increased activity within BA 31 in those with ADHD at post-FMT measures may reflect increased activation within the default mode network (DMN) or attentional changes, suggesting a unique neural response to the sensory processing of force and proprioceptive afferent input in those with ADHD when performing motor skills. This may have important functional implications for motor tasks dependent on similar proprioceptive afferent input.Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, where differences are often present relating to the performance of motor skills. Our previous work elucidated unique event-related potential patterns of neural activity in those with ADHD when performing visuomotor and force-matching motor paradigms. The purpose of the current study was to identify whether there were unique neural sources related to somatosensory function and motor performance in those with ADHD. Source localization (sLORETA) software identified areas where neural activity differed between those with ADHD and neurotypical controls when performing a visuomotor tracing task and force-matching task. Median nerve somatosensory evoked potentials (SEPs) were elicited, while whole-head electroencephalography (EEG) was performed. sLORETA localized greater neural activity post-FMT in those with ADHD, when compared with their baseline activity ( Attention-Deficit/Hyperactivity Disorder (ADHD) is classically defined as a neurodevelopmental disorder, with the most common behavioural characteristics being hyperactivity, impulsivity, and inattention ,2. AlthoADHD is common in occurrence, with approximately 11% of children in the United States being diagnosed with ADHD . CurrentAlthough there is notably limited literature assessing adult ADHD, the existing literature suggests that somatosensory function and sensorimotor integration (SMI) are altered in this population ,37,38,39Our previous study established the presence of an increased N18 and reduced N30 somatosensory evoked potential (SEP) peak when utilizing a visuomotor tracing (MTT) paradigm in adults with ADHD when compared with neurotypical controls . FurtherSource localization is a neural technique that pairs collected EEG datasets with a standardized magnetic resonance imaging (MRI) head model. This makes it possible to localize specific neural generators with high spatial acuity. sLORETA offers a non-invasive and cost-efficient technique to analyze neural activity within neural generators . sLORETAThe rationale and purpose for the current study was to further improve our understanding of the neural characteristics and substrates in those with ADHD, particularly those related to motor learning and sensorimotor processing. This can be achieved by applying a form of neural assessment with a high level of spatial acuity, such as sLORETA. The research question addressed within the current study, is: Are there differences in neural activity source locations during visuomotor and/or force modulation tasks in young adults aged 18–35 years old with ADHD? The current study aimed to answer this question for two motor acquisition paradigms, one that is highly dependent on visuomotor processing, and the other that is more so dependent on force modulation and proprioception, thus allowing for an assessment of neural sources involved in motor paradigms that utilize differing sensory pathways. We hypothesize that adults with ADHD will exhibit differences in the source of neural activity after learning novel motor paradigms when compared with neurotypical controls. Specifically, differences may be present within networks heavily involved in somatosensory processing and SMI, such as the primary somatosensory cortex (S1) within parietal regions and those related to the cerebellum due to their involvement in motor performance.This study received ethical approval from the Ontario Tech University Research Ethics Board . All participants gave written informed consent before they participated in this study. This study was performed according to the principles set out by the Declaration of Helsinki for the use of humans in experimental research.n = 15; 9 females) in the FMT group had a mean age of 22.00 ± 2.51, while controls  had an average age of 20.80 ± 1.97. Participants in the ADHD group  for the MTT had a mean age of 21.5 ± 1.93, while controls  had a mean age of 20.81 ± 2.46.Participants were students from the Ontario Tech University campus. All participants were between 18–35 years of age. Two motor paradigms were assessed in this study, and each paradigm had one group of young adults with ADHD and one group of neurotypical controls. Participants were the same as those included in our previous studies looking at cortical activity using evoked potentials ,21. ThosAll participants reported that they were right-handed, and their handedness was further confirmed using the Edinburgh Handedness Inventory (EHI). Additionally, participants completed questionnaires as part of pre-screening before participating, to ensure they did not have a history within the past five years of concussion, brain injury, epilepsy, or stroke that could have inadvertently affected the EEG, SEPs, and sLORETA results. All participants completed the Adult ADHD Self-Report scale (AASRS-v1.1) pre-screening questionnaire prior to participation. The AASRS-v1.1 quantifies symptomology related to ADHD. The AASRS is made up of 18 questions that are strongly correlated to ADHD diagnostic criteria set out by the DSM-IV . All queThe SEPs stimulation frequency was set at 2.47 Hz. SEPs were delivered via stimulation of the median nerve just proximal to the right wrist, which was approximately 2 cm proximal to the distal crease of the wrist. The stimulation intensity was set to the motor threshold of the abductor pollicis brevis (APB), being the lowest intensity at which a visible thumb twitch of 1 cm in amplitude occurred. The noted muscle contraction occurs due to stimulation of the median nerve, which is a mixed nerve. This will ensure that the 1a afferents are stimulated, which is a fundamental part of eliciting the short-latency SEP peaks, as a result of their cortical projections . The anoSignal4 software  was used to record the peripheral SEP peaks, including the N9 which was recorded over the ipsilateral brachial plexus, or Erb’s point . The N9 Cortical electrical activity was recorded using surface EEG in the form of a Waveguard™ 64-electrode EEG cap . The Waveguard™ cap was connected to the TMSi REFA-8 amplifier  with 64 EEG channels, 4 bipolar channels, and 4 auxiliary channels. EEG data were collected using Advanced Source Analysis Lab™ (ANT Neuro) software, and all EEG signals were collected at a 2048 Hz sampling frequency. Each electrode had an impedance below 10 kΩ.The novel MTT was delivered via a custom Leap Motion software tool , which was launched using Unity™ software developed for gaming. This paradigm consisted of sinusoidal waveform patterns, with four unique traces that varied in intensity and frequency, thus allowing for variability in how difficult each trace was. This variation allows for an unpredictable task, potentially enabling learning to occur . Traces The novel FMT was delivered in several blocks, including 4 pre-acquisition blocks, 12 acquisition blocks, and 4 post-acquisition blocks. A block is defined as a group of trials, and each trial consisted of 3–5 traces. This task required force-modulation of the right thumb via adduction and abduction, enabling participants to trace a waveform on the screen. This can be seen in The task was delivered using LabVIEW custom programming . The force transducer utilized was calibrated using a 50 kg load cell. Each trace varied from 2% to 12% of each individual’s APB MVC. The intended trace was a series of white dots, and participants saw their force output on the computer monitor via a yellow line. Two horizontal red bars were placed on each side of the trace (white line), and these were placed 0.5% ± the white line, acting as a guide or boundary within which participants aimed to stay. While completing the behavioural paradigm, the participant’s hand was pronated with their thumb resting against the transducer. SEPs were collected immediately prior to and after completion of this novel FMT paradigm.EEG data processing occurred offline, using ANT 4.10.1 software, in order to remove artifacts, such as those from blinking, from the EEG signal. A band-pass filter with a low cut-off of 0.2 Hz and a high cut-off of 1000 Hz, and a slope of 24 dB/octave, was used. This process was performed for all datasets. All EEG datasets were then averaged and epochs were created starting from −10 ms and to 200 ms, making for a total epoch duration of 210 ms. This allowed for the assessment of all short-latency SEP peaks. Each participant’s data had two averages, one from the “pre” or baseline stimulation prior to the motor paradigm, and one from the “post” stimulation measures that occurred directly after completion of the motor paradigm.sLORETA software was used to perform the source localization analyses ,68,69. s(1)Baseline ADHD vs. baseline control, to assess potential group differences at baseline measures.Post ADHD vs. post control, assessing group differences in source activity after the acquisition of the motor paradigm.Between groups (ADHD vs. control) at both baseline and post measures. Comparisons were performed for both the MTT and FMT.(2)ADHD baseline vs. ADHD post.Control baseline vs. control post.Within groups (pre-measures vs. post-measures) for both the ADHD and control group. Similarly, comparisons were performed for both tasks, the MTT and FMT. This comparison was performed to assess whether locations of source activity differed within each group after acquisition of either of the motor paradigms.(3)Baseline MTT vs. baseline FMT.Post MTT vs. post FMT.ADHDBaseline MTT vs. baseline FMT.Post MTT vs. post FMT.ControlFinally, comparing between tasks, to assess whether somatosensory neural processing differed significantly between the two task conditions (MTT vs. FMT), discerning neural sources were activated in response to visuomotor vs. force-matching demands of each task, respectively.This analysis was performed in the time-domain, for the following comparisons:p = 0.05. All statistical tests were performed within sLORETA’s statistical tool in the time-domain [t-test, depending on the comparison being performed. First, this converts all of the EEG data values into t-values for each of the time frames. This process was completed on 430 time frames, as the epoch was 210 ms in duration and was collected at a 2048 Hz sampling frequency. The software provides a t-critical value in the form of a two-tailed t-value threshold. The SnPM adjusted for multiple comparisons by utilizing 5000 randomized permutations [p-value) associated with the difference. Thus, this illustrated whether the differences noted were statistically significant or not. Furthermore, an ASA Lab™ (ANT Neuro) sLORETA source localization tool was utilized to generate individual graphical images of neural activity for all groups, where sLORETA had established a significant change during the time-domain analysis described above. This was performed on the latency at which the sLORETA analysis had elucidated the presence of neural areas where there was significantly different activation between tested groups, and produced an enhanced visual representation of activation sources for each group separately.All tests had statistical significance set at e-domain ,74. Statutations . Once thp < 0.05). Increased activity at BA 31 occurred at approximately 156 ms post median nerve stimulation. ADHD Pre vs. ADHD Post, FMT Analysis: the results indicated that those with ADHD had increased neural activity at post-SEP measures (or reduced at baseline/pre), after performing the novel FMT motor paradigm, when compared with their baseline SEP measures. Significantly greater activity was present in BA 31, precuneus, parietal lobe . Results from the MTT comparisons can be seen in p > 0.05).All other comparisons are outlined in the methods: all other statistical tests, including within and between groups for both the MTT and FMT, yielded non-significant differences  in those with ADHD. Alternatively, this may relate to self-perceived difficulties with the task, because if participants found the FMT difficult or boring, they may have experienced difficulty maintaining focus during the motor acquisition paradigm. One way to account for this in the future may be to ask participants to rate their mental state/attention before, during, and after performing a task. This would allow for a qualitative assessment of attentional levels at the different stages of the task, and then could aid in the interpretation of the neurophysiological results.Potential limitations include the participants being limited to young university-aged adults with ADHD; therefore, it is unknown whether these results can be generalized to ADHD in childhood or older adulthood. Additionally, although sLORETA in conjunction with high-density EEG is a valid and cost-efficient form of neural assessment, in the future, incorporating neurological techniques, such as functional MRI (fMRI), for each participant would further enhance these findings. The sample sizes for both the FMT and MTT protocols were modest, and although all samples met the sample size goal defined upon study inception, future work may benefit from increased sample sizes in order to ensure that possible additional differences are not missed due to a type II error.This study demonstrated that young adults with ADHD exhibit increased activation within BA 31 after performing a motor learning paradigm dependent on force modulation; this increased activity was localized to the precuneus, parietal lobe. The increased activity in BA 31 may reflect up-regulation in the DMN at post measures, in addition to alterations to selective spatial attention after such motor tasks. These findings are specific to motor tasks dependent on force, and were absent when assessing changes after the visuomotor task. Furthermore, these findings are in line with the neurophysiological characteristics associated with ADHD, such as unique functioning of the DMN and precuneus, while adding important contextual insight into the role that these networks play in motor acquisition and learning in adult ADHD. Overall, greater neural activity has a focal point at BA 31 after force-modulation motor tasks in young adults with ADHD, and this provides further insight into the neural functioning relevant to daily motor skills that are heavily dependent on this form of sensory processing.


Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two neurodevelopmental conditions with neuropsychological, social, emotional, and psychopathological similarities. Both are characterized by executive dysfunction, emotion dysregulation (ED), and psychiatric comorbidities. By focusing on emotions and embodied cognition, this study aims to improve the understanding of overlapping symptoms between ADHD and ASD through the use of verbal fluency tasks.Fifty-two adults with ADHD, 13 adults with ADHD + ASD and 24 neurotypical (NT) participants were recruited in this study. A neuropsychological evaluation, including different verbal fluency conditions , was proposed. Subjects also completed several self-report questionnaires, such as scales measuring symptoms of ED.Compared to NT controls, adults with ADHD + ASD produced fewer anger-related emotions. Symptoms of emotion dysregulation were associated with an increased number of actions verbs and emotions produced in ADHD.The association between affective language of adults with ADHD and symptoms of emotion dysregulation may reflect their social maladjustment. Moreover, the addition of ADHD + ASD conditions may reflect more severe affective dysfunction. Neurodevelopmental disorders affect 5–15% of children  and ofteAttention deficit hyperactivity disorder is characterized by a triad of symptoms: i.e., inattention, hyperactivity, and impulsivity. In children, the prevalence is estimated between 7.2 and 9.4% . MoreoveAutism spectrum disorder is characterized by social communication peculiarities and the presence of restricted, repetitive behaviors . ASD affFrom a neuropsychological standpoint, emotional regulation is defined as the process of initiating, avoiding, inhibiting, maintaining, or modulating emotional states to accomplish individual goals or social adaptation . In neurOne way to study cold and hot executive functions is to use verbal fluency tasks (VFT). VFT, particularly letter and semantic conditions , have long been used to assess the executive and language functioning of patients e.g., . VFT tapvia clustering and switching. Clustering corresponds to the production of words within a semantic or phonemic category, while switching reflects the ability to shift efficiently to a new category. Moreover, VFT are also influenced by parameters of imaginability, and concreteness, as well as linguistic parameters . Indeed, access to the lexical stock is facilitated for more frequent and concrete words  and to NT subjects (p < 0.001). All participants were native French speakers. This study was approved by ethics committees .Fifty-two adults with ADHD aged 18–57 , and 13 adults with co-occurring ADHD + ASD aged 19–67  without intellectual disability, communication difficulties, nor difference in gender distribution see  were recAll subjects, with the exception of the ASD group, participated in a neuropsychological assessment and completed self-report questionnaires. Autistic adults only completed the VFT conditions.Attention deficit hyperactivity disorder and ASD symptoms were, respectively measured by the Wender-Reimherr Adult Attention Deficit Disorder Scale (WRAADDS);  and the Depressive and anxiety symptoms were measured by the Beck Depression Inventory (BDI); , and theEmotion dysregulation was evaluated by the cyclothymic dimension of the Temperament Evaluation of Memphis, Pisa, Paris and San Diego-autoquestionnaire version (TEMPS-A);  and the via the matrix and the vocabulary subtests of the Wechsler Adult Intelligence Scale–Third Edition (WAIS-III); ; , whereasIS-III);  were useExecutive functions: verbal working memory was assessed by the digit-span task (WAIS-III); . The “GoEmotional Word Fluency Test (EWFT); : the EWFAction Verb Fluency Test (AVFT); : This taValence, arousal, dominance: Based on Osgood et al. , we analpost hoc Tukey were conducted to compare demographic and clinical variables between groups. One-way ANOVA and post hoc Tukey were also used for each VFT variables . Assumptions for ANOVA were verified by the Shapiro-Wilk test for normality and by Levene’s test for equality of variances. When the conditions could not be met, Kruskall-Wallis and Mann–Whitney U tests were used to compare the results between groups. The alpha level was adjusted for multiple post hoc comparisons of VFT characteristics between groups using the false discovery rate (FDR) method  method . Statist2 (2) = 25.780; p < 0.001; p = 0.001] and cyclothymic traits . This is also the case for anxiety in adults with ADHD . Regarding neuropsychological measures, adults with ADHD made more errors on the Hayling Test compared to NT subjects .Compared to NT controls, adults with ADHD and with ADHD + ASD reported higher depressive symptoms . p = 0.030]. Adults with ADHD + ASD produced fewer actions (M = 14.69) compared to NT subjects . However, this difference was no longer significant after the FDR correction (p = 0.072). The other measures, including rule breaks, repetitions, frequency, valence, arousal and dominance of generated words did not differ significantly between groups.Regarding the AVFT, differences between groups are significant for the number of words produced [F  = 3.655; p = 0.003; M = 3.73, p = 0.032, respectively; p = 0.018).Secondly, we also looked at the VFT performance of a group of adults with ASD only . Autistip = 0.012). The other main symptom domains, i.e., inattention and impulsivity, were not related to EWFT characteristics , as well as the frequency of these words in EWFT . The greater the emotional over-reactivity, the more numerous and infrequent the generated emotions were in adults with ADHD. In contrast, there was no relationship between EWFT performances and social adjustment .In the ADHD group only, we were interested in the relationship between core symptoms, emotion dysregulation, cold and hot executive functions . Regarding ADHD symptoms, hyperactivity was related to the frequency of reported emotions . Adults p = 0.024; p = 0.003) were also associated with more arousing actions on the AVFT. In addition, affective lability symptoms tended to be related to the number of actions produced . This suggests that the more adults with ADHD experience mood swings, the more actions they generate spontaneously. Social maladjustment was negatively associated with the frequency of actions reported by adults with ADHD .Hyperactivity . In addition, increased inhibition errors in the Hayling test is associated with fewer emotional words generated .Concerning cold executive functions, correlation analyses indicate significant associations between performance on free, letter, semantic, and action verb VFT (p = 0.045). The number of action verbs produced was positively related to working memory performance on the digit span task. Moreover, the number of correct responses in the ToL task, reflecting planning abilities, was also positively associated  to the number of words generated in the AVFT.For the AVFT, the faster the automatic response inhibition on the Hayling task, the more action nouns adults with ADHD can generate (r = 0.346; This study is the first to use a wide range of VFT, including two conditions assessing hot executive functions, in adults with ADHD, ASD, and co-occurring ADHD + ASD, compared to NT controls. The EWFT and the AVFT conditions revealed specific patterns regarding the particularities of affective language and its potential links with emotion dysregulation symptoms in ADHD, i.e., especially emotional over-reactivity and impulsivity. Consistent with our hypotheses, adults with ADHD + ASD produced fewer action verbs compared to NT subjects. These results are congruous with those obtained by Inokuchi and Kamio in autistic adults  and are Indeed, process-oriented measures of the emotional output on the EWFT differed between groups. Adults with ADHD produced more arousing emotions than NT subjects. However, this result was no longer significant after FDR correction, which may be due to a lack of statistical power. Nevertheless, this result may be explained by the heightened emotional experience of adults with ADHD. Indeed, related to symptoms of emotional over-reactivity, individuals with ADHD report experiencing emotions more intensely, which could translate in the EWFT by more arousing emotional words –18. ConsRegarding the emotional words related to the six primary emotions , 127, thConcerning AVFT, adults with ADHD + ASD generated fewer actions compared to NT controls. These results are consistent with those obtained by Inokuchi and Kamio , who fouIn ADHD, contrary to our hypothesis, participants did not produce more arousing actions compared to the other groups. Nevertheless, symptoms of hyperactivity and impulsivity were positively correlated with the generation of arousing actions. Moreover, we found a negative association between the frequency of actions generated and self-reported difficulties of social adjustment on the WRAADDS. This suggests that unusual actions are related to difficulties in social adaptation in adults with ADHD, which is consistent with the relationship between motor hyperactivity and impaired social functioning in adults with ADHD , 145. InWe also checked the EWFT and AVFT performance of autistic adults without the ADHD co-occurrence. We found that autistic adults produced significantly more emotion words compared to adults with ADHD . ConsideThis study has several limitations. First, this study is limited by the small sample of adults with co-occurring ADHD + ASD and autistic adults, which results in a lack of statistical power. Analysing VFT performance in a larger sample could allow to tackle the specificities of the ASD group, including in the generation of anger-related words and action verbs. Second, we did not exclude participants with psychopathological comorbidities in the ADHD + ASD and ASD groups. These comorbidities, including anxiety and depressive disorders, are very common in these conditions  and are Taken together, our results suggest that even though emotional processing difficulties is a shared symptom domain between autistic adults, adults with ADHD and adults with ASD + ADHD, several differences can be found using hot executive functions conditions of VFT. First of all, adults with co-occurring ADHD + ASD could present unique affective features that are different from those found in separate presentations of ADHD or ASD, and may have more severe cognitive difficulties, especially in verbal initiation. Most importantly, these results point to the fact that their emotional symptoms do not seem to be a simple addition of both conditions. Secondly, increased emotional word output can also be dysfunctional and linked to emotion dysregulation. While future studies are needed with increased sample sizes and measures of emotion dysregulation symptoms in autistic adults, these findings suggest that different mechanisms are involved in emotional processing in each subgroup and different treatment options could be needed to target them.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by CPP South Mediterranean II. The patients/participants provided their written informed consent to participate in this study.AW: study conception, data collection and coding, statistical analysis, and write the first draft of the manuscript. EM: study conception, manuscript writing and revision, and data collection. SW: study conception, manuscript writing and revision, and data collection. LW: study conception, manuscript writing and revision, and data collection. All authors contributed to the article and approved the submitted version.