ADHD symptoms are associated with the modular structure of intrinsic brain networks in a representative sample of healthy adults

Abnormal wiring of the structural connectome in adults with ADHD

Current knowledge of white matter changes in large-scale brain networks in adult attention-deficit/hyperactivity disorder (ADHD) is scarce. We collected diffusion-weighted magnetic resonance imaging data in 40 adults with ADHD and 36 neurotypical controls and used constrained spherical deconvolution-based tractography to reconstruct whole-brain structural connectivity networks. We used network-based statistic (NBS) and graph theoretical analysis to investigate differences in these networks between the ADHD and control groups, as well as associations between structural connectivity and ADHD symptoms assessed with the Adult ADHD Self-Report Scale or performance in the Conners Continuous Performance Test 2 (CPT-2). NBS revealed decreased connectivity in the ADHD group compared to the neurotypical controls in widespread unilateral networks, which included subcortical and corticocortical structures and encompassed dorsal and ventral attention networks and visual and somatomotor systems. Furthermore, hypoconnectivity in a predominantly left-frontal network was associated with higher amount of commission errors in CPT-2. Graph theoretical analysis did not reveal topological differences between the groups or associations between topological properties and ADHD symptoms or task performance. Our results suggest that abnormal structural wiring of the brain in adult ADHD is manifested as widespread intrahemispheric hypoconnectivity in networks previously associated with ADHD in functional neuroimaging studies.

Characteristic dynamic functional connectivity during sevoflurane-induced general anesthesia

General anesthesia (GA) during surgery is commonly maintained by inhalational sevoflurane. Previous resting state functional MRI (rs-fMRI) studies have demonstrated suppressed functional connectivity (FC) of the entire brain networks, especially the default mode networks, transitioning from the awake to GA condition. However, accuracy and reliability were limited by previous administration methods (e.g. face mask) and short rs-fMRI scans. Therefore, in this study, a clinical scenario of epilepsy patients undergoing laser interstitial thermal therapy was leveraged to acquire 15 min of rs-fMRI while under general endotracheal anesthesia to maximize the accuracy of sevoflurane level. Nine recruited patients had fMRI acquired during awake and under GA, of which seven were included in both static and dynamic FC analyses. Group independent component analysis and a sliding-window method followed by k-means clustering were applied to identify four dynamic brain states, which characterized subtypes of FC patterns. Our results showed that a low-FC brain state was characteristic of the GA condition as a single featuring state during the entire rs-fMRI session; In contrast, the awake condition exhibited frequent fluctuations between three distinct brain states, one of which was a highly synchronized brain state not seen in GA. In conclusion, our study revealed remarkable dynamic connectivity changes from awake to GA condition and demonstrated the advantages of dynamic FC analysis for future studies in the assessments of the effects of GA on brain functional activities.

Exploring neural heterogeneity in inattention and hyperactivity

Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.

Longitudinal Changes of Resting-State Networks in Children With Attention-Deficit/Hyperactivity Disorder and Typically Developing Children

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent childhood neurodevelopmental disorder. Given the profound brain changes that occur across childhood and adolescence, it is important to identify functional networks that exhibit differential developmental patterns in children with ADHD. This study sought to examine whether children with ADHD exhibit differential developmental trajectories in functional connectivity compared with typically developing children using a network-based approach.

METHODS

This longitudinal neuroimaging study included 175 participants (91 children with ADHD and 84 control children without ADHD) between ages 9 and 14 and up to 3 waves (173 total resting-state scans in children with ADHD and 197 scans in control children). We adopted network-based statistics to identify connected components with trajectories of development that differed between groups.

RESULTS

Children with ADHD exhibited differential developmental trajectories compared with typically developing control children in networks connecting cortical and limbic regions as well as between visual and higher-order cognitive regions. A pattern of reduction in functional connectivity between corticolimbic networks was seen across development in the control group that was not present in the ADHD group. Conversely, the ADHD group showed a significant decrease in connectivity between predominantly visual and higher-order cognitive networks that was not displayed in the control group.

CONCLUSIONS

Our findings show that the developmental trajectories in children with ADHD are characterized by a subnetwork involving different trajectories predominantly between corticolimbic regions and between visual and higher-order cognitive network connections. These findings highlight the importance of examining the longitudinal maturational course to understand the development of functional connectivity networks in children with ADHD.

Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

Pilot study to explore poor visual searching capabilities in children with ADHD: a tablet-based computerized test battery study

Aim: The frequent visual attention deficiency reported in children with attention-deficit/hyperactivity disorder (ADHD) could represent a relevant biomarker but robust estimates of such cognitive impairment in clinical populations remained challenging. To assess visual attention impairment in children ADHD using a new design tablet-based computerized test battery which allowed objective recording of visual search performances.Methods: Forty-nine children with ADHD and their IQ- and age-matched typically developmental (TD) children were enrolled in the study. Visual attention abilities were estimated by using the computerized modified barrage test developed by Metrisquare. We analyzed the time spent to achieve the whole battery and, the errors and omissions done by each child during each of the three sub-tasks.Results: We observed a significant association between the load of sustained attention requested to perform a sub-task and the numbers of errors and omissions made by the children whatever the group considered. During the most stringent sub-task in term of visual attention engagement, children with ADHD displayed more significant errors and omissions when compared to IQ- and age-matched controls. This effect was not mediated by the time spent to perform the task since we did not report any significant difference between groups.Conclusion: The different performance of the most stringent sub-task observed in children with ADHD could be due to their deficient neural activity in frontal areas responsible of visual endogenous attention needed for difficult visual searching tasks. This cognitive battery could be a useful instrument to estimate visual attention impairment in children with ADHD.HIGHLIGHTSWe assessed if a new design tablet-based computerized test battery would allow objective recording of visual search performances.We observed that children with ADHD made significantly more errors and omissions with respect to age-, sex- matched controls during the most stringent sub-task in terms of visual attention engagementThe tablet-based computerized test battery could be a promising tool to objectively estimate abnormal attention search impairment in ADHD.

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion

Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.

Reduced Modular Segregation of White Matter Brain Networks in Attention Deficit Hyperactivity Disorder

OBJECTIVE

Despite studies reporting alterations in the brain networks of patients with ADHD, alterations in the modularity of white matter (WM) networks are still unclear.

METHOD

Based on the results of module division by generalized Louvain algorithm, the modularity of ADHD was evaluated. The correlation between the modular changes of ADHD and its clinical characteristics was analyzed.

RESULTS

The participation coefficient and the connectivity between modules of ADHD increased, and the modularity coefficient decreased. Provincial hubs of ADHD did not change, and the number of connector hubs increased. All results showed that the modular segregation of WM networks of ADHD decreased. Modules with reduced modular segregation are mainly responsible for language and motor functions. Moreover, modularity showed evident correlation with the symptoms of ADHD.

CONCLUSION

The modularity changes in WM network provided a novel insight into the understanding of brain cognitive alterations in ADHD.

Adolescents with a concussion have altered brain network functional connectivity one month following injury when compared to adolescents with orthopedic injuries

Concussion is a mild traumatic brain injury (mTBI) with increasing prevalence among children and adolescents. Functional connectivity (FC) within and between the default mode network (DMN), central executive network (CEN) and salience network (SN) has been shown to be altered post-concussion. Few studies have investigated connectivity within and between these 3 networks following a pediatric concussion. The present study explored whether within and between-network FC differs between a pediatric concussion and orthopedic injury (OI) group aged 10-18. Participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan at 4 weeks post-injury. One-way ANCOVA analyses were conducted between groups with the seed-based FC of the 3 networks. A total of 55 concussion and 27 OI participants were included in the analyses. Increased within-network FC of the CEN and decreased between-network FC of the DMN-CEN was found in the concussion group when compared to the OI group. Secondary analyses using spherical SN regions of interest revealed increased within-network FC of the SN and increased between-network FC of the DMN-SN and CEN-SN in the concussion group when compared to the OI group. This study identified differential connectivity patterns following a pediatric concussion as compared to an OI 4 weeks post-injury. These differences indicate potential adaptive brain mechanisms that may provide insight into recovery trajectories and appropriate timing of treatment within the first month following a concussion.

Inattentive and hyperactive traits differentially associate with inter-individual functional synchrony during video viewing in young children without ADHD

Inattention and hyperactivity present on a spectrum and may influence the way children perceive and interact with the world. We investigated whether normative variation in inattentive and hyperactive traits was associated with differences in brain function, while children watched clips from an age-appropriate television program. Functional magnetic resonance imaging (fMRI) data and parent reports of inattention and hyperactivity traits were collected from 81 children 4–7 years of age with no parent-reported diagnoses. Data were analyzed using intersubject correlations (ISCs) in mixed effects models to determine if inattentive and hyperactive traits were associated with idiosyncrasy of fMRI response to the video. We hypothesized that pairs of children with higher average inattention and hyperactivity scores would show less interindividual brain synchrony to one another than pairs with lower average scores on these traits. Video watching engaged widespread visual, auditory, default mode and dorsal prefrontal regions. Inattention and hyperactivity were separably associated with ISC in many of these regions. Our findings suggest that the spectrum of inattention and hyperactivity traits in children without ADHD are differentially associated with neural processing of naturalistic video stimuli, which may have implications for understanding how children with different levels of these traits process audiovisual information in unconstrained conditions.

Differential structure-function network coupling in the inattentive and combined types of attention deficit hyperactivity disorder

The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction.

Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics

The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.

Analysis of Dynamic Network Reconfiguration in Adults with Attention-Deficit/Hyperactivity Disorder Based Multilayer Network

Attention-deficit/hyperactivity disorder (ADHD) has been reported exist abnormal topology structure in the brain network. However, these studies often treated the brain as a static monolithic structure, and dynamic characteristics were ignored. Here, we investigated how the dynamic network reconfiguration in ADHD patients differs from that in healthy people. Specifically, we acquired resting-state functional magnetic resonance imaging data from a public dataset including 40 ADHD patients and 50 healthy people. A novel model of a "time-varying multilayer network" and metrics of recruitment and integration were applied to describe group differences. The results showed that the integration scores of ADHD patients were significantly lower than those of controls at every level. The recruitment scores were lower than healthy people except for the whole-brain level. It is worth noting that the subcortical network and the thalamus in ADHD patients exhibited reduced alliance preference both within and between functional networks. In addition, we also found that recruitment and integration coefficients showed a significant correlation with symptom severity in some regions. Our results demonstrate that the capability to communicate within or between some functional networks is impaired in ADHD patients. These evidences provide a new opportunity for studying the characteristics of ADHD brain networks.

Attentional networks, vigilance, and distraction as a function of attention-deficit/hyperactivity disorder symptoms in an adult community sample

Attentional difficulties are a core axis in attention-deficit/hyperactivity disorder (ADHD). However, establishing a consistent and detailed pattern of these neurocognitive alterations has not been an easy endeavour. Based on a dimensional approach to ADHD, the present study aims at comprehensively characterizing three key attentional domains: the three attentional networks (alerting, orienting, and executive attention), two components of vigilance (executive and arousal vigilance), and distraction. To do so, we modified a single, fine-grained task (the ANTI-Vea) by adding irrelevant distractors. One hundred and twenty undergraduates completed three self-reports of ADHD symptoms in childhood and adulthood and performed the ANTI-Vea. Despite the low reliability of some ANTI-Vea indexes, the task worked successfully. While ADHD symptoms in childhood were related to alerting network and arousal vigilance, symptoms in adulthood were linked to executive vigilance. No association between ADHD symptom severity and executive attention and distraction was found. In general, our hypotheses about the relationships between ADHD symptoms and attentional processes were partially supported. We discuss our findings according to ADHD theories and attention measurement.

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

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

Neurophysiological markers of ADHD symptoms in typically-developing children

Children with attention-deficit/hyperactivity disorder (ADHD) are characterized by symptoms of inattention, impulsivity, and hyperactivity. Neurophysiological correlates of ADHD include changes in the P3 component of event-related brain potentials (ERPs). Motivated by recent advances towards a more dimensional understanding of ADHD, we investigate whether ADHD-related ERP markers relate to continuous variations in attention and executive functioning also in typically-developing children. ERPs were measured while 31 school children (9-11 years) completed an adapted version of the Continuous Performance Task that additionally to inhibitory processes also isolates effects of physical stimulus salience. Children with higher levels of parent-reported ADHD symptoms did not differ in task performance, but exhibited smaller P3 amplitudes related to stimulus salience. Furthermore, ADHD symptoms were associated with the variability of neural responses over time: Children with higher levels of ADHD symptoms demonstrated lower variability in inhibition- and salience-related P3 amplitudes. No effects were observed for ERP latencies and the salience-related N2. By demonstrating that ADHD-associated neurophysiological mechanisms of inhibition and salience processing covary with attention and executive functioning in a children community sample, our study provides neurophysiological support for dimensional models of ADHD. Also, temporal variability in event-related potentials is highlighted as additional indicator of ADHD requiring further investigation.

BrainNET: Inference of Brain Network Topology Using Machine Learning

Background: To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. Methods: BrainNET is based on extremely randomized trees to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open-source simulated fMRI data of 50 subjects in 28 different simulations under various confounding conditions with known ground truth were used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available attention-deficit/hyperactivity disorder (ADHD) data set, including 134 typically developing children (mean age: 12.03, males: 83), 75 ADHD inattentive (mean age: 11.46, males: 56), and 93 ADHD combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. Results: BrainNET demonstrated excellent performance across all simulations and varying confounders in identifying the true presence of connections. In the ADHD data set, BrainNET was able to identify significant changes (p < 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups were identified using correlation and PC. Conclusion: We describe BrainNET, a new network inference method to estimate fMRI connectivity that was adapted from gene regulatory methods. BrainNET out-performed Pearson correlation and PC in fMRI simulation data and real-world ADHD data. BrainNET can be used independently or combined with other existing methods as a useful tool to understand network changes and to determine the true network topology of the brain under various conditions and disease states. Impact statement Developed a new functional magnetic resonance image (fMRI) network inference method named as BrainNET using machine learning. BrainNET out-performed Pearson correlation and partial correlation in fMRI simulation data and real-world attention-deficit/hyperactivity disorder data. BrainNET does not need to be pretrained and can be applied to infer fMRI network topology independently on individual subjects and for varying number of nodes.

Neural primacy of the dorsolateral prefrontal cortex in patients with obsessive-compulsive disorder

The dorsolateral prefrontal cortex (DLPFC), a key structure in the executive system, has consistently emerged as a crucial element in the pathophysiology of obsessive-compulsive disorder (OCD). However, the neural primacy of the DLPFC remains elusive in this disorder. We investigated the causal interaction (measured by effective connectivity) between the DLPFC and the remaining brain areas using bivariate Granger causality analysis of resting-state fMRI collected from 88 medication-free OCD patients and 88 matched healthy controls. Additionally, we conducted seed-based functional connectivity (FC) analyses to identify network-level neural functional alterations using the bilateral DLPFC as seeds. OCD patients demonstrated reduced FC between the right DLPFC and right orbitofrontal cortex (OFC), and activity in the right OFC had an inhibitory effect on the right DLPFC. Additionally, we observed alterations in both feedforward and reciprocal influences between the inferior temporal gyrus (ITG) and the DLPFC in patients. Furthermore, activity in the cerebellum had an excitatory influence on the right DLPFC in OCD patients. These findings may help to elucidate the psychopathology of OCD by detailing the directional connectivity between the DLPFC and the rest of the brain, ultimately helping to identify regions that could serve as treatment targets in OCD.