Disrupted Resting Frontal-Parietal Attention Network Topology Is Associated With a Clinical Measure in Children With Attention-Deficit/Hyperactivity Disorder

Maternal prenatal distress exposure negatively associates with the stability of neonatal frontoparietal network

Maternal prenatal distress (PD), frequently defined as in utero prenatal stress exposure (PSE) to the developing fetus, influences the developing brain and numerous associations between PSE and brain structure have been described both in neonates and in older children. Previous studies addressing PSE-linked alterations in neonates' brain activity have focused on connectivity analyses from predefined seed regions, but the effects of PSE at the level of distributed functional networks remains unclear. In this study, we investigated the impact of prenatal distress on the spatial and temporal properties of functional networks detected in functional MRI data from 20 naturally sleeping, term-born (age 25.85 ± 7.72 days, 11 males), healthy neonates. First, we performed group level independent component analysis (GICA) to evaluate an association between PD and the identified functional networks. Second, we searched for an association with PD at the level of the stability of functional networks over time using leading eigenvector dynamics analysis (LEiDA). No statistically significant associations were detected at the spatial level for the GICA-derived networks. However, at the dynamic level, LEiDA revealed that maternal PD negatively associated with the stability of a frontoparietal network. These results imply that maternal PD may influence the stability of frontoparietal connections in neonatal brain network dynamics and adds to the cumulating evidence that frontal areas are especially sensitive to PSE. We advocate for early preventive intervention strategies regarding pregnant mothers. Nevertheless, future research venues are required to assess optimal intervention timing and methods for maximum benefit.

Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

Background and objectives

The study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI).

Materials and methods

We investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty six drug-naive ADHD children (average age 11.86 ± 2.21 years; 49 male) and 56 age matched TD controls (average age 11.51 ± 1.77 years, 44 male) were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.

Results

An accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.

Conclusion

Pattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls.

Functional Connectivity Differences Between Two Culturally Distinct Prairie Vole Populations: Insights Into the Prosocial Network

BACKGROUND

The goal of this study was to elucidate the fundamental connectivity-resting-state connectivity-within and between nodes in the olfactory and prosocial (PS) cores, which permits the expression of social monogamy in males; and how differential connectivity accounts for differential expression of prosociality and aggression.

METHODS

Using resting-state functional magnetic resonance imaging, we integrated graph theory analysis to compare functional connectivity between two culturally/behaviorally distinct male prairie voles (Microtusochrogaster).

RESULTS

Illinois males display significantly higher levels of prosocial behavior and lower levels of aggression than KI (Kansas dam and Illinois sire) males, which are associated with differences in underlying neural mechanisms and brain microarchitecture. Shared connectivity 1) between the anterior hypothalamic area and the paraventricular nucleus and 2) between the medial preoptic area and bed nucleus of the stria terminalis and the nucleus accumbens core suggests essential relationships required for male prosocial behavior. In contrast, Illinois males displayed higher levels of global connectivity and PS intracore connectivity, a greater role for the bed nucleus of the stria terminalis and anterior hypothalamic area, which were degree connectivity hubs, and greater PS and olfactory intercore connectivity.

CONCLUSIONS

These findings suggest that behavioral differences are associated with PS core degree of connectivity and postsignal induction. This transgenerational system may serve as powerful mental health and drug abuse translational model in future studies.

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology and disorder specificity

Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.

Handedness in ADHD: Meta-Analyses

Meta-analyses have shown that several neurodevelopmental and psychiatric disorders, such as autism spectrum disorder and schizophrenia, are associated with a higher prevalence of atypical (left-, non-right-, or mixed-) handedness. One neurodevelopmental disorder for which this association is unclear is attention deficit hyperactivity disorder (ADHD). Here, some empirical studies have found evidence for a higher prevalence of atypical handedness in individuals with ADHD compared to neurotypical individuals. However, other studies failed to establish such an association. Therefore, meta-analytic integration is critical to estimate whether or not there is an association between handedness and ADHD. We report the results of three meta-analyses (left-, mixed-, and non-right-handedness) comparing handedness in individuals with ADHD to controls (typically developing individuals). The results show evidence of a trend towards elevated levels of atypical handedness when it comes to differences in left- and mixed-handedness (p = 0.09 and p = 0.07, respectively), but do show clear evidence of elevated levels of non-right-handedness between individuals with ADHD and controls (p = 0.02). These findings are discussed in the context of the hypothesis that ADHD is a disorder in which mostly right-hemispheric brain networks are affected. Since right-handedness represents a dominance of the left motor cortex for fine motor behavior, such as writing, as well as a left-hemispheric dominance for language functions, and about 90% of individuals are right-handers, this hypothesis might explain why there is not stronger evidence for an association of left-handedness with ADHD. We suggest that the mechanisms involved in the pathogenesis of ADHD might show an overlap with the mechanisms involved in handedness strength, but not handedness direction.

Diffusion tensor imaging brain structural clustering patterns in major depressive disorder

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

Growing evidence indicates that disruptions in the brain's functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Disentangling age- and disease-related alterations in schizophrenia brain network using structural equation modeling: A graph theoretical study based on minimum spanning tree

Functional brain networks have been shown to undergo fundamental changes associated with aging or schizophrenia. However, the mechanism of how these factors exert influences jointly or interactively on brain networks remains elusive. A unified recognition of connectomic alteration patterns was also hampered by heterogeneities in network construction and thresholding methods. Recently, an unbiased network representation method regardless of network thresholding, so called minimal spanning tree algorithm, has been applied to study the critical skeleton of the brain network. In this study, we aimed to use minimum spanning tree (MST) as an unbiased network reconstruction and employed structural equation modeling (SEM) to unravel intertwined relationships among multiple phenotypic and connectomic variables in schizophrenia. First, we examined global and local brain network properties in 40 healthy subjects and 40 schizophrenic patients aged 21–55 using resting‐state functional magnetic resonance imaging (rs‐fMRI). Global network alterations are measured by graph theoretical metrics of MSTs and a connectivity‐transitivity two‐dimensional approach was proposed to characterize nodal roles. We found that networks of schizophrenic patients exhibited a more star‐like global structure compared to controls, indicating excessive integration, and a loss of regional transitivity in the dorsal frontal cortex (corrected p <.05). Regional analysis of MST network topology revealed that schizophrenia patients had more network hubs in frontal regions, which may be linked to the “overloading” hypothesis. Furthermore, using SEM, we found that the level of MST integration mediated the influence of age on negative symptom severity (indirect effect 95% CI [0.026, 0.449]). These findings highlighted an altered network skeleton in schizophrenia and suggested that aging‐related enhancement of network integration may undermine functional specialization of distinct neural systems and result in aggravated schizophrenic symptoms.

Attention-deficit/hyperactivity disorder is characterized by a delay in subcortical maturation

Although previous studies have found that ADHD is characterized by a delay in cortical maturation, it is not clear whether this phenomenon was secondary to developmental trajectories in subcortical regions (caudate, putamen, pallidum, thalamus, hippocampus and amygdala). Using the ADHD-200 dataset, we estimated subcortical volumes in 339 individuals with ADHD and 568 typically developing controls. We defined the growth trajectory of each subcortical structure, delineating a phase of childhood increase followed by an adolescent decrease in subcortical volumes using a quadratic growth model. From these trajectories, the age of attaining peak subcortical volumes was derived and used as an index of subcortical maturation. We found that subcortical structures (caudate, putamen, pallidum, thalamus, hippocampus and amygdala) followed curvilinear trajectories similar to those reported in previous studies. The volumes of these subcortical structures in ADHD were also delayed in the developmental trajectory, which suggested that ADHD may be characterized by a delay in subcortical maturation. This delay may lead to a shift in which individuals with ADHD go through the process of pruning the nerve connections that is part of the normal maturation process during adolescence. Further, we also found that the asymmetric development of subcortical structures was abnormal in ADHD, which resulted from the imbalance of the maturation delay of bilateral subcortical structures. The subcortical maturation delay may play an important role in the pathophysiology of ADHD. Our findings provide new potential targets to investigate the pathophysiology of ADHD.

Reduced Thalamus Volume and Enhanced Thalamus and Fronto-Parietal Network Integration in the Chess Experts

The ability of chess experts depends to a large extent on spatial visual processing, attention, and working memory, all of which are thought to be mediated by the thalamus. This study explored whether continued practice and rehearsal over a long period of time results in structural changes in the thalamic region. We found smaller gray matter volume regions in the thalami of expert Chinese chess players in comparison with novice players. We then used these regions as seeds for resting-state functional connectivity analysis and observed significantly strengthened integration between the thalamus and fronto-parietal network in expert Chinese chess players. This strengthened integration that includes a group of brain regions showing an increase in activation to external stimulation, particularly during tasks relying on working memory and attention. Our findings demonstrate structural changes in the thalamus caused by a wide range of engagement in chess problem solving, and that this strengthened functional integration with widely distributed circuitry better supports high-level cognitive control of behavior.

Altered resting functional network topology assessed using graph theory in youth with attention-deficit/hyperactivity disorder

Notwithstanding an extensive literature about attention-deficit/hyperactivity disorder (ADHD) and brain structure and function, the controversy of ADHD resulting from dysfunction or developmental delay remains unclear. Graph analysis studies have reached consensus about the pattern of increased integration and decreased randomness during childhood and early adulthood. Here, we hypothesized that ADHD is a neurodevelopmental disorder resulting from developmental delay and would show a pattern of decreased integration and increased randomness during childhood and early adulthood compared with typically developing children. To test this hypothesis, publicly available resting-state fMRI data from 102 children with ADHD and 143 typically developing controls (TDC) were compared using graph theoretical analysis. Functional connectivity was estimated using Pearson correlation analysis, and network topology was characterized using small-world (SW) and minimum spanning tree (MST) properties. The mean strength of global connectivity was significantly weaker in those with ADHD and was related to ADHD diagnosis scores. Significant group differences were observed for SW(clustering coefficient, path length, global and local efficiency) and MST (leaf number, kappa and hierarchy) topology. In addition, except for global efficiency, all of these parameters showed significant correlations with ADHD-related disability. The topology of SW and MST showed less integration and more randomness, which confirmed that ADHD is a disorder associated with developmental delay. Moreover, the topology of resting-state functional networks in children with ADHD that show abnormalities was associated with the degree of disability, which can be considered neurological hallmarks of neurodevelopmental disorders and may facilitate the evaluation and monitoring of clinical status in individuals with ADHD.