An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Altered brain functional connectivity in patients with resistance to thyroid hormone ß

To investigate changes in brain network organization and possible neurobehavioral similarities to attention-deficit hyperactivity disorder (ADHD), we measured changes in brain resting-state functional connectivity (rs-fMRI) and cognitive domains in patients with resistance to thyroid hormone β (RTHβ) and compared them with those in healthy control subjects. In this prospective case-control study, twenty-one participants with genetically confirmed RTHβ were matched with 21 healthy controls. The Adult ADHD Self-Report Scale (ASRS-v1.1) and ADHD Rating Scale-IV were used to assess self-reported symptoms of ADHD. A voxel-wise and atlas-based approach was used to identify changes in the brain networks. The RTHβ group reported behavioral symptoms similar to those of ADHD. We found evidence of weaker network integration of the lingual and fusiform gyri in the RTHβ group, which was mainly driven by weaker connectivity to the bilateral insula and supplementary motor cortex. Functional connectivity between regions of the default mode network (angular gyrus/middle temporal gyrus) and regions of the cognitive control network (bilateral middle frontal gyrus) was increased in RTHβ patients compared to healthy controls. Increased connectivity between regions of the default mode network and the dorsolateral prefrontal cortex is frequently reported in ADHD and is interpreted to be associated with deficits in attention. Our finding of weaker connectivity of the lingual gyrus to the bilateral insula (salience network) in RTHβ patients has also been reported previously in ADHD and may reflect decreased habituation to visual stimuli and increased distractibility. Overall, our observations support the notion of neuropsychological similarities between RTHβ and ADHD.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia

PURPOSE

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia.

METHODS

The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods.

RESULTS

The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups.

CONCLUSIONS

Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Neural correlates of inattention in adults with ADHD

In the last two decades, numerous magnetic resonance imaging (MRI) studies have examined differences in cortical structure between individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) and healthy controls. These studies primarily emphasized alterations in gray matter volume (GMV) and cortical thickness (CT). Still, the scientific literature is notably scarce in regard to investigating associations of cortical structure with ADHD psychopathology, specifically inattention within adults with ADHD. The present study aimed to elucidate neurobiological underpinnings of inattention beyond GMV and CT by including cortical gyrification, sulcal depth, and fractal dimension. Building upon the Comparison of Methylphenidate and Psychotherapy in Adult ADHD Study (COMPAS), cortical structure parameters were investigated using 141 T1-weighted anatomical scans of adult patients with ADHD. All brain structural analyses were performed using the threshold-free cluster enhancement (TFCE) approach and the Computational Anatomy Toolbox (CAT12) integrated into the Statistical Parametric Mapping Software (Matlab Version R2021a). Results revealed significant correlations of inattention in multiple brain regions. Cortical gyrification was negatively correlated, whereas cortical thickness and fractal dimension were positively associated with inattention. The clusters showed widespread distribution across the cerebral cortex, with both hemispheres affected. The cortical regions most prominently affected included the precuneus, para-, pre-, and postcentral gyri, superior parietal lobe, and posterior cingulate cortex. This study highlights the importance of cortical alterations in attentional processes in adults with ADHD. Further research in this area is warranted to elucidate intricacies of inattention in adults with ADHD to potentially enhance diagnostic accuracy and inform personalized treatment strategies.

Cerebellar network alterations in adult attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition that often persists into adulthood. Underlying alterations in brain connectivity have been identified but some relevant connections, such as the middle, superior, and inferior cerebellar peduncles (MCP, SCP, and ICP, respectively), have remained largely unexplored; thus, we sought to investigate whether the cerebellar peduncles contribute to ADHD pathophysiology among adults.

METHODS

We applied diffusion-weighted spherical deconvolution tractography to dissect the cerebellar peduncles of male adults with ADHD (including those who did or did not respond to methylphenidate, based on at least 30% symptom improvement at 2 months) and controls. We investigated differences in tract metrics between controls and the whole ADHD sample and between controls and treatment-response groups using sensitivity analyses. Finally, we analyzed the association between the tract metrics and cliniconeuropsychological profiles.

RESULTS

We included 60 participants with ADHD (including 42 treatment responders and 18 nonresponders) and 20 control participants. In the whole ADHD sample, MCP fractional anisotropy (FA; t 78 = 3.24, p = 0.002) and hindrance modulated orientational anisotropy (HMOA; t 78 = 3.01, p = 0.004) were reduced, and radial diffusivity (RD) in the right ICP was increased (t 78 = -2.84, p = 0.006), compared with controls. Although case-control differences in MCP FA and HMOA, which reflect white-matter microstructural organization, were driven by both treatment response groups, only responders significantly differed from controls in right ICP RD, which relates to myelination (t 60 = 3.14, p = 0.003). Hindrance modulated orientational anisotropy of the MCP was significantly positively associated with hyperactivity measures.

LIMITATIONS

This study included only male adults with ADHD. Further research needs to investigate potential sex- and development-related differences.

CONCLUSION

These results support the role of the cerebellar networks, especially of the MCP, in adult ADHD pathophysiology and should encourage further investigation.

CLINICAL TRIAL REGISTRATION

NCT03709940.

Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians

The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.

Altered Intrinsic Brain Spontaneous Activities in Children With Autism Spectrum Disorder Comorbid ADHD

OBJECTIVE

The study involved 17 children with Autism Spectrum Disorder (ASD), 21 with ADHD, 30 with both (ASD + ADHD), and 28 typically developing children (TD).

METHODS

The amplitude of low-frequency fluctuations (ALFF) was measured as a regional brain function index. Intrinsic functional connectivity (iFC) was also analyzed using the region of interest (ROI) identified in ALFF analysis. Statistical analysis was done via one-way ANCOVA, Gaussian random field (GRF) theory, and post-hoc pair-wise comparisons.

RESULTS

The ASD + ADHD group showed increased ALFF in the left middle frontal gyrus (MFG.L) compared to the TD group. In terms of global brain function, the ASD group displayed underconnectivity in specific regions compared to the ASD + ADHD and TD groups.

CONCLUSION

The findings contribute to understanding the neural mechanisms underlying ASD + ADHD.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

Impact of prenatal marijuana exposure on adolescent brain structural and functional connectivity and behavioural outcomes

There has been an increase in the number of women using marijuana whilst pregnant. Previous studies have shown that children with prenatal marijuana exposure have developmental deficits in memory and decreased attentiveness. In this study, we assess whether prenatal marijuana exposure is associated with alterations in brain regional morphometry and functional and structural connectivity in adolescents. We downloaded behavioural scores and subject image files from the Adolescent Brain Cognitive DevelopmentSM Study. A total of 178 anatomical and diffusion magnetic resonance imaging files (88 prenatal marijuana exposure and 90 age- and gender-matched controls) and 152 resting-state functional magnetic resonance imaging files (76 prenatal marijuana exposure and 76 controls) were obtained. Behavioural metrics based on the parent-reported child behavioural checklist were also obtained for each subject. The associations of prenatal marijuana exposure with 17 subscales of the child behavioural checklist were calculated. We assessed differences in brain morphometry based on voxel-based and surface-based morphometry in adolescents with prenatal marijuana exposure versus controls. We also evaluated group differences in structural and functional connectivity in adolescents for region-to-region connectivity and graph theoretical metrics. Interactions of prenatal marijuana exposure and graph networks were assessed for impact on behavioural scores. Multiple comparison correction was performed as appropriate. Adolescents with prenatal marijuana exposure had greater abnormal or borderline child behavioural checklist scores in 9 out of 17 subscales. There were no significant differences in voxel- or surface-based morphometry, structural connectivity or functional connectivity between prenatal marijuana exposure and controls. However, there were significant differences in prenatal marijuana exposure-graph network interactions with respect to behavioural scores. There were three structural prenatal marijuana exposure-graph network interactions and seven functional prenatal marijuana exposure-graph network interactions that were significantly associated with behavioural scores. Whilst this study was not able to confirm anatomical or functional differences between prenatal marijuana exposure and unexposed pre-adolescent children, there were prenatal marijuana exposure-brain structural and functional graph network interactions that were significantly associated with behavioural scores. This suggests that altered brain networks may underlie behavioural outcomes in adolescents with prenatal marijuana exposure. More work needs to be conducted to better understand the prognostic value of brain structural and functional network measures in prenatal marijuana exposure.

Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents

Neurodevelopmental disorders (NDDs) are a group of complex neurologic and psychiatric disorders. Functional and molecular imaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), can be used to measure network activity noninvasively and longitudinally during maturation in both humans and rodent models. Here, we review the current knowledge on rs-fMRI and PET biomarkers in the study of normal and abnormal neurodevelopment, including intellectual disability (ID; with/without epilepsy), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), in humans and rodent models from birth until adulthood, and evaluate the cross-species translational value of the imaging biomarkers. To date, only a few isolated studies have used rs-fMRI or PET to study (abnormal) neurodevelopment in rodents during infancy, the critical period of neurodevelopment. Further work to explore the feasibility of performing functional imaging studies in infant rodent models is essential, as rs-fMRI and PET imaging in transgenic rodent models of NDDs are powerful techniques for studying disease pathogenesis, developing noninvasive preclinical imaging biomarkers of neurodevelopmental dysfunction, and evaluating treatment-response in disease-specific models.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.

Construction of Meta-Thinking Educational Program Based on Mental-Brain Simulation (MTMBS) and Evaluating its Effectiveness on Executive Functions, Emotion Regulation, and Impulsivity in Children With ADHD: A Resting-State Functional MRI Study

OBJECTIVE

The aim of present research was to make a Meta-Thinking educational program based on mental-brain simulation and to evaluate its effectiveness on executive functions, emotion regulation and impulsivity in children with ADHD.

METHODS

The research method was Embedded Design: Embedded Experimental Model. The research sample included 32 children with ADHD who were randomly assigned to two experimental and control groups. The intervention was implemented for eight sessions of 1.5 hr for the experimental group, and fMRI images were taken from them, while the control group didn't receive any treatment. Finally, using semi-structured interviews, coherent information was collected from the parents of the experimental group about the changes made. Data were analyzed with SPSS-24, MAXQDA, fMRIprep, and FSL software.

RESULTS

The Meta-Thinking Educational Program had effect on performance of ADHD children and suppressed brain regions related to DMN.

CONCLUSION

The Implementation of this educational program plays a vital role in improving psychological problems of children with ADHD.

Methylphenidate as a treatment option for substance use disorder: a transdiagnostic perspective

A transition in viewing mental disorders from conditions defined as a set of unique characteristics to one of the quantitative variations on a collection of dimensions allows overlap between disorders. The overlap can be utilized to extend to treatment approaches. Here, we consider the overlap between attention-deficit/hyperactivity disorder and substance use disorder to probe the suitability to use methylphenidate as a treatment for substance use disorder. Both disorders are characterized by maladaptive goal-directed behavior, impaired cognitive control, hyperactive phasic dopaminergic neurotransmission in the striatum, prefrontal hypoactivation, and reduced frontal cortex gray matter volume/density. In addition, methylphenidate has been shown to improve cognitive control and normalize associated brain activation in substance use disorder patients and clinical trials have found methylphenidate to improve clinical outcomes. Despite the theoretical basis and promising, but preliminary, outcomes, many questions remain unanswered. Most prominent is whether all patients who are addicted to different substances may equally profit from methylphenidate treatment.

Methylphenidate is more effective to improve inhibitory control and working memory compared to tDCS in children and adolescents with attention deficit/hyperactivity disorder: a proof-of-concept study

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is characterized by an inappropriate, pervasive and persistent pattern of inattention, hyperactivity, and/or impulsivity and associated with substantial functional impairment. Despite considerable advances in the understanding and management of ADHD, some patients do not respond well to methylphenidate (MPH), the first-choice pharmacological treatment. Over the past decades, among non-invasive brain stimulation techniques, transcranial direct current stimulation (tDCS) has proven to be an effective and safe technique to improve behavior and cognition in children with neurodevelopmental disorders, including ADHD, by modifying cortical excitability. However, the effect of tDCS has never been directly compared with that of the MPH. The present randomized sham-controlled trial evaluated the effect of a single session of anodal tDCS compared with the administration of a single dose of MPH in children and adolescents with ADHD.

Methods

After completing baseline assessment (T0), 26 children and adolescents with ADHD were exposed to 3 conditions with a 24-h interval-sessions: (A) a single session of anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC); (B) a single session of sham tDCS over the left DLPFC; (C) a single dose of MPH.

Results

Our results showed that after administering a single dose of MPH, children and adolescents with ADHD improved inhibitory control and visual-spatial WM compared with baseline, anodal, and sham tDCS. However, a single session of active tDCS over the left DLPFC was not effective compared with either baseline or sham tDCS.

Discussion

In conclusion, our protocol in ADHD involving a single tDCS session did not demonstrate consistent improvements in neurocognitive features compared with baseline, sham tDCS, or single MPH administration. Different protocols need to be developed to further test the effectiveness of tDCS in improving ADHD symptoms.

Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children and is characterised by inattention, impulsiveness and hyperactivity. While several studies have analysed the static functional connectivity in the resting-state functional MRI (rs-fMRI) of ADHD patients, detailed investigations are required to characterize the connectivity dynamics in the brain. In an attempt to establish a link between attention instability and the dynamic properties of Functional Connectivity (FC), we investigated the differences in temporal variability of FC between 40 children with ADHD and 40 Typically Developing (TD) children. Using a sliding-window method to segment the rs-fMRI scans in time, we employed seed-to-voxel correlation analysis for each window to obtain time-evolving seed connectivity maps for seeds placed in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC). For each subject, the standard deviation of the voxel connectivity time series was used as a measure of the temporal variability of FC. Results showed that ADHD patients exhibited significantly higher variability in dFC than TD children in the cingulo-temporal, cingulo-parietal, fronto-temporal, and fronto-parietal networks ( pFW E < 0.05). Atypical temporal variability was observed in the left and right temporal gyri, the anterior cingulate cortex, and lateral regions of the right parietal cortex. The observations are consistent with visual attention issues, executive control deficit, and rightward parietal dysfunction reported in ADHD, respectively. These results help in understanding the disorder with a fresh perspective linking behavioural inattention with instability in FC in the brain.

A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.

Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.

Assessing the relationship between maternal risk for attention deficit hyperactivity disorder and functional connectivity in their biological toddlers

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with increased risk for poor educational attainment and compromised social integration. Currently, clinical diagnosis rarely occurs before school-age, despite behavioral signs of ADHD in very early childhood. There is no known brain biomarker for ADHD risk in children ages 2-3 years-old.

METHODS

The current study aimed to investigate the functional connectivity (FC) associated with ADHD risk in 70 children aged 2.5 and 3.5 years via functional near-infrared spectroscopy (fNIRS) in bilateral frontal and parietal cortices; regions involved in attentional and goal-directed cognition. Children were instructed to passively watch videos for approximately 5 min. Risk for ADHD in each child was assessed via maternal symptoms of ADHD, and brain data was evaluated for FC.

RESULTS

Higher risk for maternal ADHD was associated with lower FC in a left-sided parieto-frontal network. Further, the interaction between sex and risk for ADHD was significant, where FC reduction in a widespread bilateral parieto-frontal network was associated with higher risk in male, but not female, participants.

CONCLUSIONS

These findings suggest functional organization differences in the parietal-frontal network in toddlers at risk for ADHD; potentially advancing the understanding of the neural mechanisms underlying the development of ADHD.

Effects of cathodal transcranial direct current stimulation on inhibitory and attention control in children and adolescents with attention-deficit hyperactivity disorder: A pilot randomized sham-controlled crossover study

The pathophysiological of attention-deficit hyperactivity disorder (ADHD) includes hypoactivation of the dorso-lateral prefrontal cortex (DLPFC). Most studies have used anodal (excitatory) transcranial direct current stimulation (tDCS) to improve ADHD symptoms, however, a meta-analysis showed limited effect on improving inhibition, and no evidence of attention improvement. We thus present a pilot protocol for investigating the effect of other montage i.e. cathodal (inhibitory) tDCS on neurophysiological and behavioral measures in ADHD. Eleven participants underwent active (1.5 mA, 20 min) and sham cathodal tDCS over the left DLPFC for 5 consecutive days at a 1-month interval. Quantitative electroencephalography was recorded in a resting state with the eyes opened and closed during visual go/no-go and auditory continuous performance tasks at baseline, after five sessions, and at 1-week and 1-month follow-ups. Correct responses and omission errors were recorded. After five active sessions, alpha power increased in the right frontal area when the eyes were opened, and delta power in the left frontal area and omission errors decreased during go/no-go tasks, with no differences at follow-ups. The results revealed improvements in inhibitory control, but not for attention. No aftereffects were observed in either outcomes. However, the changes found in both hemispheres would probably support the hypothesis that cathodal stimulation over the left DLPFC may increase the activity of the right DLPFC via transcallosal inhibition. Results of this pilot trial would help to design and implement a full-scale randomized control trials for further ADHD research. This study was registered on ClinicalTrials.gov (NCT03955692).

Feature and decision-level fusion for schizophrenia detection based on resting-state fMRI data

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network

The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

Frontal corticostriatal functional connectivity reveals task positive and negative network dysregulation in relation to ADHD, sex, and inhibitory control

Frontal corticostriatal circuits (FCSC) are involved in self-regulation of cognition, emotion, and motor function. While these circuits are implicated in attention-deficit/hyperactivity disorder (ADHD), the literature establishing FCSC associations with ADHD is inconsistent. This may be due to study variability in considerations of how fMRI motion regression was handled between groups, or study specific differences in age, sex, or the striatal subregions under investigation. Given the importance of these domains in ADHD it is crucial to consider the complex interactions of age, sex, striatal subregions and FCSC in ADHD presentation and diagnosis. In this large-scale study of 362 8-12 year-old children with ADHD (n = 165) and typically developing (TD; n = 197) children, we investigate associations between FCSC with ADHD diagnosis and symptoms, sex, and go/no-go (GNG) task performance. Results include: (1) increased striatal connectivity with age across striatal subregions with most of the frontal cortex, (2) increased frontal-limbic striatum connectivity among boys with ADHD only, mostly in default mode network (DMN) regions not associated with age, and (3) increased frontal-motor striatum connectivity to regions of the DMN were associated with greater parent-rated inattention problems, particularly among the ADHD group. Although diagnostic group differences were no longer significant when strictly controlling for head motion, with motion possibly reflecting the phenotypic variance of ADHD itself, the spatial distribution of all symptom, age, sex, and other ADHD group effects were nearly identical to the initial results. These results demonstrate differential associations of FCSC between striatal subregions with the DMN and FPN in relation to age, ADHD, sex, and inhibitory control.

Intranasal Oxytocin Modulates the Salience Network in Aging

Growing evidence supports a role of the neuropeptide oxytocin in promoting social cognition and prosocial behavior, possibly via modulation of the salience of social information. The effect of intranasal oxytocin administration on the salience network, however, is not well understood, including in the aging brain. To address this research gap, 42 young (22.52 ± 3.02 years; 24 in the oxytocin group) and 43 older (71.12 ± 5.25 years; 21 in the oxytocin group) participants were randomized to either self-administer intranasal oxytocin or placebo prior to resting-state functional imaging. The salience network was identified using independent component analysis (ICA). Independent t-tests showed that individuals in the oxytocin compared to the placebo group had lower within-network resting-state functional connectivity, both for left amygdala (MNI coordinates: x = −18, y = 0, z = −15; corrected p < 0.05) within a more ventral salience network and for right insula (MNI coordinates: x = 39, y = 6, z = −6; corrected p < 0.05) within a more dorsal salience network. Age moderation analysis furthermore demonstrated that the oxytocin-reduced functional connectivity between the ventral salience network and the left amygdala was only present in older participants. These findings suggest a modulatory role of exogenous oxytocin on resting-state functional connectivity within the salience network and support age-differential effects of acute intranasal oxytocin administration on this network.

ADHD classification using auto-encoding neural network and binary hypothesis testing

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD treatment, wherein its neurobiological diagnosis (or classification) is helpful and provides the objective evidence to clinicians. The existing ADHD classification methods suffer two problems, i.e., insufficient data and feature noise disturbance from other associated disorders. As an attempt to overcome these difficulties, a novel deep-learning classification architecture based on a binary hypothesis testing framework and a modified auto-encoding (AE) network is proposed in this paper. The binary hypothesis testing framework is introduced to cope with insufficient data of ADHD database. Brain functional connectivities (FCs) of test data (without seeing their labels) are incorporated during feature selection along with those of training data and affect the sequential deep learning procedure under binary hypotheses. On the other hand, the modified AE network is developed to capture more effective features from training data, such that the difference of inter- and intra-class variability scores between binary hypotheses can be enlarged and effectively alleviate the disturbance of feature noise. On the test of ADHD-200 database, our method significantly outperforms the existing classification methods. The average accuracy reaches 99.6% with the leave-one-out cross validation. Our method is also more robust and practically convenient for ADHD classification due to its uniform parameter setting across various datasets.

Alterations in Structural and Functional Connectivity in ADHD: Implications for Theories of ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is increasingly viewed as a disorder of brain connectivity. We review connectivity-based theories of ADHD including the default mode network (DMN) interference and multiple network hypotheses. We outline the main approaches used to study brain connectivity in ADHD: diffusion tensor imaging and resting-state functional connectivity. We discuss the basic principles underlying these methods and the main analytical approaches used and consider what the findings have told us about connectivity alterations in ADHD. The most replicable finding in the diffusion tensor imaging literature on ADHD is lower fractional anisotropy in the corpus callosum, a key commissural tract which connects the brain's hemispheres. Meta-analyses of resting-state functional connectivity studies have failed to identify spatial convergence across studies, with the exception of meta-analyses focused on specific networks which have reported within-network connectivity alterations in the DMN and between the DMN and the fronto-parietal control and salience networks. Overall, methodological heterogeneity between studies and differences in sample characteristics are major barriers to progress in this area. In addition, females, adults and medication-naïve/unmedicated individuals are under-represented in connectivity studies, comorbidity needs to be assessed more systematically, and longitudinal research is needed to investigate whether ADHD is characterized by maturational delays in connectivity.

Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control

Background

Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study, we apply a novel whole-matrix regression approach referred to as covariate assisted principal regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control.

Methods

Participants included 8- to 12-year-old children with ADHD (n = 115; 29 girls) and typically developing control children (n = 102; 35 girls) who completed a resting-state functional magnetic resonance imaging scan and a Go/NoGo task. We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau).

Results

The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models, suggesting some specificity to the covariates of interest.

Conclusions

These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.

Right prefrontal activation predicts ADHD and its severity: A TMS-EEG study in young adults

OBJECTIVE

Here we bring a neurophysiological diagnostic tool, based on pathophysiologically-relevant brain region, that is critical for reducing the variability between clinicians, and necessary for quantitative measures of ADHD severity.

METHODS

54 healthy and 57 ADHD adults participated in the study. Electroencephalography (EEG) was recorded when combined with transcranial magnetic stimulation (TMS) over the right prefrontal cortex and also recorded during the Stop Signal task.

RESULTS

TMS evoked potentials (TEPs) and the event related potential (ERP) components in the Stop Signal task were found to be significantly reduced in ADHD relative to the matched healthy controls. Stop signal reaction time (SSRT) and stopping accuracy was found to correlate with the ERP signal, and ADHD severity correlated with the TEP signal. Cortical activity (early TEP and Stop Signal ERP) diagnostic model yielded accuracy of 72%.

CONCLUSION

TEPs and ERPs reveal that right PFC excitability was associated with ADHD severity, and with behavioral impulsivity - as a hallmark of ADHD pathology. This electrophysiological biomarker supports the potential of objective diagnosis for ADHD.

SIGNIFICANCE

Such tools would allow better assessment of treatment efficacy and prognosis, may advance understanding of the pathophysiology of the disease and better the public's attitudes and stigma towards ADHD.

TRIAL REGISTRATION

Trial to Evaluate the Efficacy of the HLPFC Coil Deep Transcranial Magnetic Stimulation System in Treating Attention Deficit and Hyperactivity Disorder (ADHD) in Adults, https://clinicaltrials.gov/ct2/show/NCT01737476, ClinicalTrials.govnumberNCT01737476.

Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network

Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.

A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.

Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention

Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.

A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.

The longitudinal association between externalizing behavior and frontoamygdalar resting-state functional connectivity in late adolescence and young adulthood

BACKGROUND

Externalizing behavior has been attributed, in part, to decreased frontolimbic control over amygdala activation. However, little is known about developmental trajectories of frontoamygdalar functional connectivity and its relation to externalizing behavior. The present study addresses this gap by examining longitudinal associations between adolescent and adult externalizing behavior and amygdala-anterior cingulate cortex (ACC) and amygdala-orbitofrontal cortex (OFC) resting-state functional connectivity in a sample of 111 typically developing participants aged 11-23 at baseline.

METHODS

Participants completed two-to-four data waves spaced approximately two years apart, resulting in a total of 309 data points. At each data wave, externalizing behavior was measured using the Externalizing Behavior Broadband Scale from the Achenbach Youth/Adult Self-Report questionnaire. Resting-state fMRI preprocessing was performed using FSL. Amygdala functional connectivity was examined using AFNI. The longitudinal association between externalizing behavior and amygdala-ACC/OFC functional connectivity was examined using linear mixed effect models in R.

RESULTS

Externalizing behavior was associated with increased amygdala-ACC and amygdala-OFC resting-state functional connectivity across adolescence and young adulthood. For amygdala-ACC connectivity, externalizing behavior at baseline primarily drove this association, whereas for amygdala-OFC functional connectivity, change in externalizing behavior relative to baseline drove the main effect of externalizing behavior on amygdala-OFC functional connectivity. No evidence was found for differential developmental trajectories of frontoamygdalar connectivity for different levels of externalizing behavior (i.e., age-by-externalizing behavior interaction effect).

CONCLUSIONS

Higher externalizing behavior is associated with increased resting-state attunement between the amygdala and ACC/OFC, perhaps indicating a generally more vigilant state for neural networks important for emotional processing and control.

Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control

Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region’s inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD.

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The DMN is more integrated with task-relevant networks in children with ADHD.

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Higher and less variable DMN integration relates to poorer response control in ADHD.

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DMN dysfunction may play a key role in response control deficits in ADHD.

The Neurological Basis of Developmental Dyslexia and Related Disorders: A Reappraisal of the Temporal Hypothesis, Twenty Years on

In a now-classic article published a couple of decades ago (Brain, 2000; 123: 2373-2399), I proposed an "extended temporal processing deficit hypothesis of dyslexia", suggesting that a deficit in temporal processing could explain not only language-related peculiarities usually noticed in dyslexic children, but also a wider range of symptoms related to impaired processing of time in general. In the present review paper, I will revisit this "historical" hypothesis both in the light of a new clinical perspective, including the central yet poorly explained notion of comorbidity, and also taking a new look at the most recent experimental work, mainly focusing on brain imaging data. First, consistent with daily clinical practice, I propose to distinguish three groups of children who fail to learn to read, of fairly equal occurrence, who share the same initial presentation (difficulty in mastering the rules of grapheme-phoneme correspondence) but with differing associated signs and/or comorbid conditions (language disorders in the first group, attentional deficits in the second one, and motor coordination problems in the last one), thus suggesting, at least in part, potentially different triggering mechanisms. It is then suggested, in the light of brain imaging information available to date, that the three main clinical presentations/associations of cognitive impairments that compromise reading skills acquisition correspond to three distinct patterns of miswiring or "disconnectivity" in specific brain networks which have in common their involvement in the process of learning and their heavy reliance on temporal features of information processing. With reference to the classic temporal processing deficit of dyslexia and to recent evidence of an inability of the dyslexic brain to achieve adequate coupling of oscillatory brain activity to the temporal features of external events, a general model is proposed according to which a common mechanism of temporal uncoupling between various disconnected-and/or mis-wired-processors may account for distinct forms of specific learning disorders, with reading impairment being a more or less constant feature. Finally, the potential therapeutic implications of such a view are considered, with special emphasis on methods seeking to enhance cross-modal connectivity between separate brain systems, including those using rhythmic and musical training in dyslexic patients.

Altered resting-state functional connectivity of the default mode and central executive networks following cognitive processing therapy for PTSD

Psychotherapy research is increasingly targeting both psychological and neurobiological mechanisms of therapeutic change. This trend is evident in and applicable to post-traumatic stress disorder (PTSD) treatment research given the high nonresponse rate of individuals with PTSD who undergo cognitive-behavioral therapy (CBT). Functional connectivity analyses investigating disrupted brain networks across mental disorders have been employed to understand both mental disorder symptoms and therapeutic mechanisms. However, few studies have examined pre-post CBT brain changes in PTSD using functional connectivity analyses. The current study investigated a) whether brain networks commonly implicated in psychopathology (e.g., default mode network [DMN], central executive network [CEN], and salience network [SN]) changed following Cognitive Processing Therapy (CPT) for PTSD and b) whether change in these networks was associated with PTSD and/or transdiagnostic symptom change. Independent components analysis was implemented to investigate resting-state functional connectivity in DMN, CEN, and SN in 42 women with PTSD and 18 trauma-exposed controls (TEC). Results indicated decreased CEN-cerebellum connectivity in PTSD participants versus TEC prior to CPT and decreased DMN connectivity in PTSD participants after CPT. Additionally, DMN and SN connectivity was related to change in positive and negative affectivity, while exploratory analyses at a cluster threshold of pFDR < .10 indicated DMN and SN connectivity was also related to change in PTSD symptoms and rumination. These findings provide evidence for normalization of CEN connectivity with treatment and implicate the DMN and SN in clinical symptom change following CPT.

Spontaneous and deliberate creative cognition during and after psilocybin exposure

Creativity is an essential cognitive ability linked to all areas of our everyday functioning. Thus, finding a way to enhance it is of broad interest. A large number of anecdotal reports suggest that the consumption of psychedelic drugs can enhance creative thinking; however, scientific evidence is lacking. Following a double-blind, placebo-controlled, parallel-group design, we demonstrated that psilocybin (0.17 mg/kg) induced a time- and construct-related differentiation of effects on creative thinking. Acutely, psilocybin increased ratings of (spontaneous) creative insights, while decreasing (deliberate) task-based creativity. Seven days after psilocybin, number of novel ideas increased. Furthermore, we utilized an ultrahigh field multimodal brain imaging approach, and found that acute and persisting effects were predicted by within- and between-network connectivity of the default mode network. Findings add some support to historical claims that psychedelics can influence aspects of the creative process, potentially indicating them as a tool to investigate creativity and subsequent underlying neural mechanisms. Trial NL6007; psilocybin as a tool for enhanced cognitive flexibility; https://www.trialregister.nl/trial/6007 .

Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing

Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.

Hyperfocus: the forgotten frontier of attention

'Hyperfocus' is a phenomenon that reflects one's complete absorption in a task, to a point where a person appears to completely ignore or 'tune out' everything else. Hyperfocus is most often mentioned in the context of autism, schizophrenia, and attention deficit hyperactivity disorder, but research into its effect on cognitive and neural functioning is limited. We propose that hyperfocus is a critically important aspect of cognition, particularly with regard to clinical populations, and that it warrants significant investigation. Hyperfocus, though ostensibly self-explanatory, is poorly defined within the literature. In many cases, hyperfocus goes undefined, relying on the assumption that the reader inherently knows what it entails. Thus, there is no single consensus to what constitutes hyperfocus. Moreover, some studies do not refer to hyperfocus by name, but describe processes that may be related. In this paper, we review how hyperfocus (as well as possibly related phenomena) has been defined and measured, the challenges associated with hyperfocus research, and assess how hyperfocus affects both neurotypical and clinical populations. Using this foundation, we provide constructive criticism about previously used methods and analyses. We also propose an operational definition of hyperfocus for researchers to use moving forward.

Systematic Review and Meta-analysis: Resting-State Functional Magnetic Resonance Imaging Studies of Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

To conduct a meta-analysis of resting-state functional magnetic resonance imaging (R-fMRI) studies in children and adolescents with attention-deficit/hyperactivity disorder (ADHD) and in adults with ADHD to assess spatial convergence of findings from available studies.

METHOD

Based on a preregistered protocol in PROSPERO (CRD42019119553), a large set of databases were searched up to April 9, 2019, with no language or article type restrictions. Study authors were systematically contacted for additional unpublished information/data. Resting-state functional magnetic resonance imaging studies using seed-based connectivity (SBC) or any other method (non-SBC) reporting whole-brain results of group comparisons between participants with ADHD and typically developing controls were eligible. Voxelwise meta-analysis via activation likelihood estimation with cluster-level familywise error (voxel-level: p < .001; cluster-level: p < .05) was used.

RESULTS

Thirty studies (18 SBC and 12 non-SBC), comprising 1,978 participants (1,094 with ADHD; 884 controls) were retained. The meta-analysis focused on SBC studies found no significant spatial convergence of ADHD-related hyperconnectivity or hypoconnectivity across studies. This nonsignificant finding remained after integrating 12 non-SBC studies into the main analysis and in sensitivity analyses limited to studies including only children or only non-medication-naïve patients.

CONCLUSION

The lack of significant spatial convergence may be accounted for by heterogeneity in study participants, experimental procedures, and analytic flexibility as well as in ADHD pathophysiology. Alongside other neuroimaging meta-analyses in other psychiatric conditions, the present results should inform the conduct and publication of future neuroimaging studies of psychiatric disorders.

Mindfulness training preserves sustained attention and resting state anticorrelation between default-mode network and dorsolateral prefrontal cortex: A randomized controlled trial

Mindfulness training can enhance cognitive control, but the neural mechanisms underlying such enhancement in children are unknown. Here, we conducted a randomized controlled trial (RCT) with sixth graders (mean age 11.76 years) to examine the impact of 8 weeks of school-based mindfulness training, relative to coding training as an active control, on sustained attention and associated resting-state functional brain connectivity. At baseline, better performance on a sustained-attention task correlated with greater anticorrelation between the default mode network (DMN) and right dorsolateral prefrontal cortex (DLPFC), a key node of the central executive network. Following the interventions, children in the mindfulness group preserved their sustained-attention performance (i.e., fewer lapses of attention) and preserved DMN-DLPFC anticorrelation compared to children in the active control group, who exhibited declines in both sustained attention and DMN-DLPFC anticorrelation. Further, change in sustained-attention performance correlated with change in DMN-DLPFC anticorrelation only within the mindfulness group. These findings provide the first causal link between mindfulness training and both sustained attention and associated neural plasticity. Administered as a part of sixth graders' school schedule, this RCT supports the beneficial effects of school-based mindfulness training on cognitive control.

Interactions between the neural correlates of dispositional internally directed thought and visual imagery

Cognition is not always directed to the events in the here and now and we often self-generate thoughts and images in imagination. Important aspects of these self-generated experiences are associated with various dispositional traits. In this study, we explored whether these psychological associations relate to a common underlying neurocognitive mechanism. We acquired resting state functional magnetic resonance imaging data from a large cohort of participants and asked them to retrospectively report their experience during the scan. Participants also completed questionnaires reflecting a range of dispositional traits. We found thoughts emphasizing visual imagery at rest were associated with dispositional tendency towards internally directed attention (self-consciousness and attentional problems) and linked to a stronger correlation between a posterior parietal network and a lateral fronto-temporal network. Furthermore, decoupling between the brainstem and a lateral visual network was associated with dispositional internally directed attention. Critically, these brain-cognition associations were related: the correlation between parietal-frontal regions and reports of visual imagery was stronger for individuals with increased connectivity between brainstem and visual cortex. Our results highlight neural mechanisms linked to the dispositional basis for patterns of self-generated thought, and suggest that accounting for dispositional traits is important when exploring the neural substrates of self-generated experience (and vice versa). This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Assessing Fine-Granularity Structural and Functional Connectivity in Children With Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) was considered to be a disorder with high heterogeneity, as various abnormalities were found across widespread brain regions in recent neuroimaging studies. However, remarkable individual variability of cortical structure and function may have partially contributed to these discrepant findings. In this work, we applied the Dense Individualized and Common Connectivity-Based Cortical Landmarks (DICCCOL) method to identify fine-granularity corresponding functional cortical regions across different subjects based on the shape of a white matter fiber bundle and measured functional connectivities between these cortical regions. Fiber bundle pattern and functional connectivity were compared between ADHD patients and normal controls in two independent samples. Interestingly, four neighboring DICCCOLs located close to the left parietooccipital area consistently exhibited discrepant fiber bundles in both datasets. The left precentral gyrus (DICCCOL 175, BA 6) and the right anterior cingulate gyrus (DICCCOL 321, BA 32) had the highest connection number among 78 pairs of abnormal functional connectivities with good cross-sample consistency. Furthermore, abnormal functional connectivities were significantly correlated with ADHD symptoms. Our studies revealed novel fine-granularity structural and functional alterations in ADHD.

Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis

Background

ADHD is one of the most common psychiatric disorders in children and adolescents. Altered functional connectivity has been associated with ADHD symptoms. This study aimed to investigate abnormal changes in the functional connectivity of resting-state brain networks (RSNs) among adolescent patients with different subtypes of ADHD.

Methods

The data were obtained from the ADHD-200 Global Competition, including fMRI data from 88 ADHD patients (56 patients of ADHD-Combined, ADHD-C and 32 patients of ADHD-Inattentive, ADHD-I) and 67 typically developing controls (TD-C). Group ICA was utilized to research aberrant brain functional connectivity within the different subtypes of ADHD.

Results

In comparison with the TD-C group, the ADHD-C group showed clusters of decreased functional connectivity in the left inferior occipital gyrus (p = 0.0041) and right superior occipital gyrus (p = 0.0011) of the dorsal attention network (DAN), supplementary motor area (p = 0.0036) of the executive control network (ECN), left supramarginal gyrus (p = 0.0081) of the salience network (SN), middle temporal gyrus (p = 0.0041), and superior medial frontal gyrus (p = 0.0055) of the default mode network (DMN), while the ADHD-I group showed decreased functional connectivity in the right superior parietal gyrus (p = 0.0017) of the DAN and left middle temporal gyrus (p = 0.0105) of the DMN. In comparison with the ADHD-I group, the ADHD-C group showed decreased functional connectivity in the superior temporal gyrus (p = 0.0062) of the AN, inferior temporal gyrus (p = 0.0016) of the DAN, and the dorsolateral superior frontal gyrus (p = 0.0082) of the DMN. All the clusters surviving at p < 0.05 (AlphaSim correction).

Conclusion

The results suggested that decreased functional connectivity within the DMN and DAN was responsible, at least in part, for the symptom of inattention in ADHD-I patients. Similarly, we believed that the impaired functional connectivity within networks may contribute to the manifestations of ADHD-C patients, including inattention, hyperactivity/impulsivity, and unconscious movements.

A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Alterations in sympathetic and parasympathetic brain networks in obstructive sleep apnea

OBJECTIVE/BACKGROUND

Obstructive sleep apnea (OSA) patients experience hypoxia and, potentially, autonomic impairments stemming from neural damage. In this study, the executive control networks (ECNs), salience networks (SNs), and default mode networks (DMNs) of adult OSA patients, as well as their relationships with autonomic impairment, were investigated through independent component analysis (ICA).

PATIENTS/METHODS

A total of 41 OSA patients and 19 healthy controls volunteers were recruited and subjected to polysomnography to ascertain their degree, if any, of sleep apnea. Each participant also underwent a cardiovascular autonomic survey, with the participant's baroreflex sensitivity (BRS) being determined based on heart rate and blood pressure alterations. The resting fMRI data of the participants was separated using probabilistic ICA, and six autonomic resting-state networks were established for group comparisons. The differences in autonomic parameters, autonomic functional connectivity (FC), and clinical severity were then correlated.

RESULTS

The OSA group had significantly worse BRS values than the controls, as well as lower FC in the posterior and anterior SNs, bilateral ECNs, and the ventral DMN, and higher FC in the left ECN. These intrinsic connectivity networks showed dissociable correlations with greater baroreflex impairment and clinical disease severity. The higher FC in the left ECN was associated with the lower FC in the ventral DMN.

CONCLUSIONS

Our findings suggest that autonomic dysfunction in OSA might be accompanied by central autonomic network alterations. The stronger sympathetic-associated regions in ECNs and the weaker parasympathetic-associated regions in DMNs may represent intrinsic neural architecture fluctuations underlining their consequent processes in OSA.

Does the child brain rest?: An examination and interpretation of resting cognition in developmental cognitive neuroscience

In cognitive neuroscience, measurements of "resting baseline" are often considered stable across age and used as a reference point against which to judge cognitive state. The task-based approach-comparing resting baseline to task conditions-implies that resting baseline is an equalizer across participants and-in the case of studies of developmental changes in cognition-across age groups. In contrast, network neuroscience explicitly examines the development of "resting state" networks across age, at odds with the idea of a consistent resting baseline. Little attention has been paid to how cognition during rest may shift across development, particularly in children under the age of eight. Childhood is marked by striking maturation of neural systems, including a protracted developmental period for cognitive control systems. To grow and shape these cognitive systems, children have a developmental imperative to engage their neural circuitry at every possible opportunity. Thus, periods of "rest" without specific instructions may require additional control for children as they fight against developmental expectation to move, speak, or otherwise engage. We therefore theorize that the child brain does not rest in a manner consistent with the adult brain as longer rest periods may represent increased cognitive control. To shape this theory, we first review the extant literature on neurodevelopment across early childhood within the context of cognitive development. Next, we present nascent evidence for a destabilized baseline for comparisons across age. Finally, we present recommendations for designing, analyzing, and interpreting tasks conducted with young children as well as for resting state. Future work must aim to tease apart the cognitive context under which we examine functional brain development in young children and take considerations into account unique to each age.

Topological Data Analysis Reveals Robust Alterations in the Whole-Brain and Frontal Lobe Functional Connectomes in Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder characterized by difficulty to control the own behavior. Neuroimaging studies have related ADHD with the interplay of fronto-parietal attention systems with the default mode network (DMN; Castellanos and Aoki, 2016). However, some results have been inconsistent, potentially due to methodological differences in the analytical strategies when defining the brain functional network, i.e., the functional connectivity threshold and/or the brain parcellation scheme. Here, we make use of topological data analysis (TDA) to explore the brain connectome as a function of the filtration value (i.e., the connectivity threshold), instead of using a static connectivity threshold. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value. We explored the utility of such a method to identify differences between 81 children with ADHD (45 male, age: 7.26–17.61 years old) and 96 typically developing children (TDC; 59 male, age: 7.17–17.96 years old), using a public dataset of resting state (rs)fMRI in human subjects. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole-brain network and the functional subnetwork levels, particularly involving the frontal lobe and the DMN. Therefore, this is a solid approach that complements connectomics-related methods and may contribute to identify the neurophysio-pathology of ADHD.

A NETWORK-BASED APPROACH TO STUDY OF ADHD USING TENSOR DECOMPOSITION OF RESTING STATE FMRI DATA

Identifying changes in functional connectivity in Attention Deficit Hyperactivity Disorder (ADHD) using functional magnetic resonance imaging (fMRI) can help us understand the neural substrates of this brain disorder. Many studies of ADHD using resting state fMRI (rs-fMRI) data have been conducted in the past decade with either manually crafted features that do not yield satisfactory performance, or automatically learned features that often lack interpretability. In this work, we present a tensor-based approach to identify brain networks and extract features from rs-fMRI data. Results show the identified networks are interpretable and consistent with our current understanding of ADHD conditions. The extracted features are not only predictive of ADHD score but also discriminative for classification of ADHD subjects from typically developed children.

BOLD differences normally attributed to inhibitory control predict symptoms, not task-directed inhibitory control in ADHD

Background

Altered brain activity that has been observed in attention deficit hyperactivity disorder (ADHD) while performing cognitive control tasks like the stop signal task (SST) has generally been interpreted as reflecting either weak (under-active) or compensatory (over-active) versions of the same functions as in healthy controls. If so, then regional activities that correlate with the efficiency of inhibitory control (i.e. stop signal reaction time, SSRT) in healthy subjects should also correlate with SSRT in ADHD. Here we test the alternate hypothesis that BOLD (blood-oxygen-level-dependent) differences might instead reflect the redirection of neural processing resources normally used for task-directed inhibitory control, towards actively managing symptomatic behaviour. If so, then activities that correlate with SSRT in TD should instead correlate with inattentive and hyperactive symptoms in ADHD.

Methods

We used fMRI (functional magnetic resonance imaging) in 14 typically developing (TD) and 14 ADHD adolescents performing the SST, and in a replication sample of 14 healthy adults. First, we identified significant group BOLD differences during all phases of activity in the SST (i.e. warning, response, reactive inhibition, error detection and post-error slowing). Next, we correlated these phases of activity with SSRT in TD and with SSRT, inattentive and hyperactive symptom scores in ADHD. We then identified whole brain significant correlations in regions of significant group difference in activity.

Results

Only three regions of significant group difference were correlated with SSRT in TD and replication groups (left and right inferior frontal gyri (IFG) during error detection and hypothalamus during post-error slowing). Consistent with regions of altered activity managing symptomatic behaviour instead of task-directed behaviour, left IFG correlated with greater inattentive score, right IFG correlated with lower hyperactive score and hypothalamus correlated with greater inattentive score and oppositely correlated with SSRT compared to TD.

Conclusions

Stimuli that elicit task-directed integration of neural processing in healthy subjects instead appear to be directing integrated function towards managing symptomatic behaviour in ADHD. The ability of the current approach to determine whether altered neural activities reflect comparable functions in ADHD and control groups has broad implications for the development and monitoring of therapeutic interventions.

ADHD with Comorbid Bipolar Disorders: A Systematic Review of Neurobiological, Clinical and Pharmacological Aspects Across the Lifespan

BACKGROUND

Attention deficit hyperactivity (ADHD) disorder is a neurodevelopmental disorder characterized by inattention, hyperactivity, disruptive behaviour, and impulsivity. Despite considered typical of children for a long time, the persistence of ADHD symptoms in adulthood gained increasing interest during the last decades. Indeed, its diagnosis, albeit controversial, is rarely carried out even because ADHD is often comorbid with several other psychiatric diosrders, in particular with bipolar disorders (BDs), a condition that complicates the clinical picture, assessment and treatment.

AIMS

The aim of this paper was to systematically review the scientific literature on the neurobiological, clinical features and current pharmacological management of ADHD comorbid with BDs across the entire lifespan, with a major focus on the adulthood.

DISCUSSION

The pharmacology of ADHD-BD in adults is still empirical and influenced by the individual experience of the clinicians. Stimulants are endowed of a prompt efficacy and safety, whilst non-stimulants are useful when a substance abuse history is detected, although they require some weeks in order to be fully effective. In any case, an in-depth diagnostic and clinical evaluation of the single individual is mandatory.

CONCLUSION

The comorbidity of ADHD with BD is still a controversial matter, as it is the notion of adult ADHD as a distinct nosological category. Indeed, some findings highlighted the presence of common neurobiological mechanisms and overlapping clinical features, although disagreement does exist. In any case, while expecting to disentangle this crucial question, a correct management of this comorbidity is essential, which requires the co-administration of mood stabilizers. Further controlled clinical studies in large samples of adult ADHD-BD patients appear extremely urgent in order to better define possible therapeutic guidelines, as well as alternative approaches for this potentially invalidating condition.