Neural network connectivity in ADHD children: an independent component and functional connectivity analysis of resting state fMRI data

Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury

In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.

Different brain functional network abnormalities between attention-deficit/hyperactivity disorder youth with and without familial risk for bipolar disorder

Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone.

Functional connectivity and complexity analyses of resting-state fMRI in pre-adolescents demonstrating the behavioral symptoms of ADHD

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), reward and motivation-related circuits (RMN), and salience network (SAL). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with the behavioral symptoms of ADHD, for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children demonstrating the behavioral features of ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in the relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents demonstrating the behavior symptoms of ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in the FPN and RMN in the healthy controls however the association was absent in the participants demonstrating the behavior symptoms of ADHD. The current findings of complexity and FC in ADHD pathology support hypotheses of altered function of inhibitory control networks in ADHD.

Functional-structural decoupling in visual network is associated with cognitive decline in patients with type 2 diabetes mellitus: evidence from a multimodal MRI analysis

Type 2 diabetes mellitus (T2DM) and cognitive dysfunction are highly prevalent disorders worldwide. Although visual network (VN) alteration and functional-structural coupling are potential warning factors for mild cognitive impairment (MCI) in T2DM patients, the relationship between the three in T2DM without MCI is unclear. Thirty T2DM patients without MCI and twenty-nine healthy controls (HC) were prospectively enrolled. Visual components (VC) were estimated by independent component analysis (ICA). Degree centrality (DC), amplitude of low frequency fluctuation (ALFF) and fractional anisotropy (FA) were established to reflect functional and structural characteristics in these VCs respectively. Functional-structural coupling coefficients were further evaluated using combined FA and DC or ALFF. Partial correlations were performed among neuroimaging indicators and neuropsychological scores and clinical variables. Three VCs were selected using group ICA. Deteriorated DC, ALFF and DC-FA coefficients in the VC1 were observed in the T2DM group compared with the HC group, while FA and ALFF-FA coefficients in these three VCs showed no significant differences. In the T2DM group, DC in the VC1 positively correlated with 2 dimensions in the California Verbal Learning Test, including Trial 4 and Total trial 1-5. The impaired DC-FA coefficients in the VC1 markedly affected the Total perseverative responses % of the Wisconsin Card Sorting Test. These findings indicate that DC and DC-FA coefficients in VN may be potential imaging biomarkers revealing early cognitive deficits in T2DM.

Default mode network connectivity and social dysfunction in children with Attention Deficit/Hyperactivity Disorder

Objective

Attention Deficit/Hyperactivity Disorder (ADHD) negatively affects social functioning; however, its neurological underpinnings remain unclear. Altered Default Mode Network (DMN) connectivity may contribute to social dysfunction in ADHD. We investigated whether DMN's dynamic functional connectivity (dFC) alterations were associated with social dysfunction in individuals with ADHD.

Methods

Resting-state fMRI was used to examine DMN subsystems (dorsal medial prefrontal cortex (dMPFC), medial temporal lobe (MTL)) and the midline core in 40 male ADHD patients (7-10 years) and 45 healthy controls (HCs). Connectivity correlations with symptoms and demographic data were assessed. Group-based analyses compared rsFC between groups with two-sample t-tests and post-hoc analyses.

Results

Social dysfunction in ADHD patients was related to reduced DMN connectivity, specifically in the MTL subsystem and the midline core. ADHD patients showed decreased dFC between parahippocampal cortex (PHC) and left superior frontal gyrus, and between ventral medial prefrontal cortex (vMPFC) and right middle frontal gyrus compared to HCs (MTL subsystem). Additionally, decreased dFC between posterior cingulate cortex (PCC), anterior medial prefrontal cortex (aMPFC), and right angular gyrus (midline core) was observed in ADHD patients relative to HCs. No abnormal connectivity was found within the dMPFC.

Conclusion

Preliminary findings suggest that DMN connectional abnormalities may contribute to social dysfunction in ADHD, providing insights into the disorder's neurobiology and pathophysiology.

Functional Connectivity and Complexity Analyses of Resting-State fMRI in Pre-Adolescents with ADHD

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), and reward and motivation-related circuits (RMN). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with ADHD for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children with ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents with ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in fewer regions in the participants with ADHD than in the controls. The observation of reduced entropy is consistent with existing literature using rsfMRI and other neuroimaging modalities. The current findings of complexity and FC in ADHD support hypotheses of altered function of inhibitory control networks in ADHD.

Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder

Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.

Abnormal static and dynamic functional network connectivity in stable chronic obstructive pulmonary disease

Objective

Many studies have explored the neural mechanisms of cognitive impairment in chronic obstructive pulmonary disease (COPD) patients using the functional MRI. However, the dynamic properties of brain functional networks are still unclear. The purpose of this study was to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients.

Materials and methods

The resting-state functional MRI and cognitive assessments were performed on 19 stable COPD patients and 19 age-, sex-, and education-matched healthy controls (HC). We conducted the independent component analysis (ICA) method on the resting-state fMRI data, and obtained seven resting-state networks (RSNs). After that, the static and dynamic functional network connectivity (sFNC and dFNC) were respectively constructed, and the differences of functional connectivity (FC) were compared between the COPD patients and the HC groups. In addition, the correlation between the dynamic functional network attributes and cognitive assessments was analyzed in COPD patients.

Results

Compared to HC, there were significant differences in sFNC among COPD patients between and within networks. COPD patients showed significantly longer mean dwell time and higher fractional windows in weaker connected State I than that in HC. Besides, in comparison to HC, COPD patients had more extensive abnormal FC in weaker connected State I and State IV, and less abnormal FC in stronger connected State II and State III, which were mainly located in the default mode network, executive control network, and visual network. In addition, the dFNC properties including mean dwell time and fractional windows, were significantly correlated with some essential clinical indicators such as FEV₁, FEV₁/FVC, and c-reactive protein (CRP) in COPD patients.

Conclusion

These findings emphasized the differences in sFNC and dFNC of COPD patients, which provided a new perspective for understanding the cognitive neural mechanisms, and these indexes may serve as neuroimaging biomarkers of cognitive performance in COPD patients.

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.

Altered functional connectivity in children with ADHD while performing cognitive control task

Response inhibition is one of the crucial cognitive domains that exhibit deficit in children with ADHD. To further elucidate it, this study examines the task-based functional-connectivity in children with attention deficit hyperactive disorder (ADHD). We acquired the fMRI data of 16 unmedicated children with ADHD and 16 typically developing (TD) children who performed the flanker task. MVPA and seed-based connectivity analysis was performed to identify the abnormal connectivity pattern across the whole brain. MVPA revealed that six important regions, namely the right IFG, right SMA, bilateral precentral gyrus, left DLPFC, and left cerebellum, had abnormal connectivity in children with ADHD while they performed the cognitive control task. Out of these six regions, four were further used for whole-brain seed-based functional connectivity analyses, which revealed patterns of significantly altered connectivity across multiple regions. Signal intensities changes were also extracted to perform BOLD- reaction time (RT) correlation analysis, that suggest positive correlation between left DLPFC and right IFG. Overall, the results suggest that children with ADHD are unable to endure high cognitive control demand. Our findings highlight the utility of analyzing brain connectivity data in identifying the abnormal connectivity in children with ADHD.

Contribution of brain network connectivity in predicting effects of polygenic risk and childhood trauma on state-trait anxiety

BACKGROUND

Anxiety is usually attributed to adverse environmental factors, but it is known as a polygenic inheritance disease. Gene-environment interactions on the occurrence and severity of anxiety are still unclear. The role of brain network connectivity in the gene-environment effects on anxiety has not been explored and may be key to understanding neuropathogenesis and guiding treatment.

METHODS

This study recruited 177 young adults from the community that completed functional magnetic resonance imaging, Childhood Trauma Questionnaire (CTQ), state-trait anxiety scores, and whole exome sequencing. We calculated polygenic risk score (PRS) for anxiety and the sum score of CTQ, which are genetic and environmental factors that may affect anxiety, respectively. Abnormal brain network connectivity determined by the gene-environment effects and its associations with anxiety scores were then explored.

RESULTS

Except for the main effect of PRS or CTQ on intra-network connectivity, significant interactions were found in intra-network connectivity of visual network, default mode network, self-reference network, and sensorimotor network. Moreover, altered network connectivity was related to anxious tendency. In particular, the effect of CTQ on trait anxiety was mediated by the disrupted sensorimotor network, accompanied by a significant direct effect. However, the PRS influence on anxiety was mainly mediated through sensorimotor network paths, which exceeded the direct influence and was moderated by childhood trauma levels.

CONCLUSIONS

These network-specific functional changes related to individual gene-environment risks advance our understanding of psychiatric pathogenesis of anxiety and provide new insights for clinical intervention.

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.

Alteration of brain resting-state networks and functional connectivity in prelingual deafness

BACKGROUND AND PURPOSE

Early hearing loss causes several changes in the brain structure and function at multiple levels; these changes can be observed through neuroimaging. These changes are directly associated with sensory loss (hearing) and the acquisition of alternative communication strategies. Such plasticity changes in the brain might establish a different connectivity pattern with resting-state networks (RSNs) and other brain regions. We performed resting-state functional magnetic resonance imaging (rsfMRI) to evaluate these intrinsic modifications.

METHODS

We used two methods to characterize the functional connectivity (FC) of RSN components in 20 prelingual deaf adults and 20 demographic-matched hearing adults. rsfMRI data were analyzed using independent component analysis (ICA) and region-of-interest seed-to-voxel correlation analysis.

RESULTS

In ICA, we identified altered FC of RSNs in the deaf group. RSNs with altered FC were observed in higher visual, auditory, default mode, salience, and sensorimotor networks. The findings of seed-to-voxel correlation analysis suggested increased temporal coherence with other neural networks in the deaf group compared with the hearing control group.

CONCLUSION

These findings suggest a highly diverse resting-state connectivity pattern in prelingual deaf adults resulting from compensatory cross-modal plasticity that includes both auditory and nonauditory regions.

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.