Brain Connectivity

Attention-deficit hyperactivity disorder (ADHD) in adulthood shows high co-occurrence rates with cocaine use disorder (CoUD). The self-medication hypothesis (SMH) provides a theoretical explanation for this comorbidity. This study investigates the neurobiological mechanisms that could support SMH in adult patients with attention-deficit hyperactivity disorder with cocaine use disorder (ADHD–CoUD).

We included 19 ADHD–CoUD patients (84.2% male; age: 32.11 years [7.18]) and 16 CoUD patients (68.7% male; age: 36.63 years [8.12]). All subjects underwent a fluorine-18-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) brain scan. We tested brain metabolism differences between ADHD–CoUD and CoUD patients using voxel-based and regions of interest (ROIs)-based analyses. The correlation between dependence/abstinence duration and regional brain metabolism was also assessed in the two groups. Lastly, we investigated the integrity of brain metabolic connectivity of mesocorticolimbic and nigrostriatal dopaminergic systems, and large-scale brain networks involved in ADHD and addictions.

The voxel-wise and ROIs-based approaches showed that ADHD–CoUD patients had a lower metabolism in the thalamus and increased metabolism in the amygdala and parahippocampus, bilaterally, than CoUD subjects and healthy controls (HCs). Metabolism in the thalamus negatively correlated with years of dependence in ADHD–CoUD patients. Moreover, connectivity analyses revealed that ADHD–CoUD patients had a more preserved metabolic connectivity than CoUD patients in the dopaminergic networks and large-scale networks involved in self-regulation mechanisms of attention and behaviors (i.e., anterior default mode network [ADMN], executive network [ECN], and anterior salience network [aSAN]).

We demonstrated distinct neuropathological substrates underlying substance-use behaviors in ADHD–CoUD and CoUD patients. Furthermore, we provided neurobiological evidence in support of SMH, demonstrating that ADHD–CoUD patients might experience short-term advantages of cocaine assumption (i.e., compensation of dopaminergic deficiency and related cognitive-behavioral deficits).

This study provides neurobiological evidence for self-medication hypothesis (SMH) in adults with attention-deficit hyperactivity disorder with cocaine use disorder (ADHD–CoUD). The current results suggest differential treatment approaches, namely pharmacological approaches for cocaine use disorder (CoUD) individuals with and without attention-deficit hyperactivity disorder (ADHD), to improve functional adjustment and reduce the risk of relapse in addictive behaviors. Specifically, stimulant pharmacological treatments (e.g., methylphenidate- and amphetamine-based stimulants) with a long-acting formulation should be considered the first line of intervention for adults with ADHD–CoUD. Furthermore, these pharmacological treatments could be combined with evidence-based behavioral interventions for emotional dysregulation in patients with substance use disorders and ADHD.

Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative “objective function” thresholding method.

The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided.

Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks.

Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.

Network thresholding refers to removing edges between node pairs in a functional network that have weak edge-weights potentially arising from unwanted variability or noise. Since edge-weight cutoffs used to generate a binary network can be sensitive to the thresholding method, we introduce a novel thresholding algorithm. We find that when applied to networks derived via perturbations, namely through simulated functional connectivity of a known network, our approach yields a binary network that is more similar to the known network compared with using existing thresholding approaches. Our algorithm is a competitive candidate for thresholding brain connectomes.

Extremely preterm (EPT) birth, defined as birth at a gestational age (GA) <28 weeks, can have a lasting impact on cognition throughout the life span. Previous investigations reveal differences in brain structure and connectivity between infants born preterm and full-term (FT), but how does preterm birth impact the adolescent connectome?

In this study, we investigate how EPT birth can alter broadscale network organization later in life by comparing resting-state functional magnetic resonance imaging connectome-based parcellations of the entire cortex in adolescents born EPT (N = 22) to age-matched adolescents born FT (GA ≥37 weeks, N = 28). We compare these parcellations to adult parcellations from previous studies and explore the relationship between an individual's network organization and behavior.

Primary (occipital and sensorimotor) and frontoparietal networks were observed in both groups. However, there existed notable differences in the limbic and insular networks. Surprisingly, the connectivity profile of the limbic network of EPT adolescents was more adultlike than the same network in FT adolescents. Finally, we found a relationship between adolescents' overall cognition score and their limbic network maturity.

Overall, preterm birth may contribute to the atypical development of broadscale network organization in adolescence and may partially explain the observed cognitive deficits.

Extremely preterm (EPT) birth is associated with persistent cognitive and behavioral impairments throughout the life span. Previous research in infants has revealed altered resting-state networks due to EPT, but are these differences also observed in adolescence? In this study, we compare brain-wide parcellations based on patterns in functional connectivity in EPT and full-term adolescents. We found differences in the insula and limbic network, where EPT adolescents show a more adultlike limbic network, and the maturity of personalized limbic networks may predict cognition. These results highlight the effect of preterm birth on brain network organization well beyond infancy.

Callous-unemotional (CU) traits are a youth antisocial phenotype hypothesized to be a result of differences in the integration of multiple brain systems. However, mechanistic insights into these brain systems are a continued challenge. Where prior work describes activation and connectivity, new mechanistic insights into the brain's functional connectome can be derived by removing nodes and quantifying changes in network properties (hereafter referred to as computational lesioning) to characterize connectome resilience and vulnerability.

Here, we study the resilience of connectome integration in CU traits by estimating changes in efficiency after computationally lesioning individual-level connectomes. From resting-state data of 86 participants (48% female, age 14.52 ± 1.31) drawn from the Nathan Kline institute's Rockland study, individual-level connectomes were estimated using graphical lasso. Computational lesioning was conducted both sequentially and by targeting global and local hubs. Elastic net regression was applied to determine how these changes explained variance in CU traits. Follow-up analyses characterized modeled node hubs, examined moderation, determined impact of targeting, and decoded the brain mask by comparing regions to meta-analytic maps.

Elastic net regression revealed that computational lesioning of 23 nodes, network modularity, and Tanner stage explained variance in CU traits. Hub assignment of selected hubs differed at higher CU traits. No evidence for moderation between simulated lesioning and CU traits was found. Targeting global hubs increased efficiency and targeting local hubs had no effect at higher CU traits. Identified brain mask meta-analytically associated with more emotion and cognitive terms. Although reliable patterns were found across participants, adolescent brains were heterogeneous even for those with a similar CU traits score.

Adolescent brain response to simulated lesioning revealed a pattern of connectome resiliency and vulnerability that explains variance in CU traits, which can aid prediction of youth at greater risk for higher CU traits.

Mechanistic insights into the differences in multiple brain systems underlying callous-unemotional (CU) traits represent a continued challenge. By examining changes in the brain functional connectome after computationally lesioning that node and examining changes in network properties, we can derive unique mechanistic insights. By applying this method to individual-level connectomes, we revealed a pattern of vulnerability and resiliency in the individual-level connectomes that aid the prediction of CU traits. Regions revealed with this method contextualize behavioral impairments in these youth, and this mask of identified regions could improve the prediction of youth higher in CU traits.

Neonatal hypoxic–ischemic encephalopathy (HIE) is the main cause of neonatal death and disability worldwide. At present, there are few researches on the application of resting-state functional magnetic resonance imaging (rs-fMRI) to explore the brain development of HIE children. This study aimed to explore the changes of brain function in neonates with different degrees of HIE using rs-fMRI.

From February 2018 to May 2020, 44 patients with HIE were recruited, including 21 mild patients and 23 moderate and severe patients. The recruited patients were scanned by conventional and functional magnetic resonance image, and the method of amplitude of low-frequency fluctuation and connecting edge analysis of brain network was used.

Compared with the mild group, the connections between the right supplementary motor area and the right precentral gyrus, the right lingual gyrus and the right hippocampus, the left calcarine cortex and the right amygdala, and the right pallidus and the right posterior cingulate cortex in the moderate and severe groups were reduced (t values were 4.04, 4.04, 4.04, 4.07, all p < 0.001, uncorrected).

By analyzing the functional connection changes of brain network in infants with different degrees of HIE, the findings of the current study suggested that neonates with moderate to severe HIE lag behind those with mild HIE in emotional processing, sensory movement, cognitive function, and learning and memory. Chinese Clinical Trial Registry registration number: ChiCTR1800016409.

The results from this study suggest that moderate to severe hypoxic–ischemic encephalopathy (HIE) children showed reductions in connection strength between the right supplementary motor area and the right precentral gyrus, the right lingual gyrus and the right hippocampus, the left calcarine cortex and the right amygdala, and the right pallidus and the right posterior cingulate cortex compared with children with mild HIE, suggesting that alterations in these brain areas are associated with the developmental changes of children with moderate and severe HIE.