White matter microstructural integrity correlates of emotion dysregulation in children with ADHD: A diffusion imaging tractography study

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Human transcriptome array analysis and diffusion tensor imaging in attention-deficit/hyperactivity disorder

The mRNA markers identified using microarray assay and diffusion tensor magnetic resonance imaging (DTI) were applied to elucidate the pathophysiology of attention-deficit hyperactivity disorder (ADHD). First, we obtained total RNA from leukocytes from three children with ADHD and three healthy controls for analysis with microarray assays. Subsequently, we applied real-time quantitative polymerase chain reaction (qRT‒PCR) assays to validate the differential expression of 7 genes (COX7B, CYCS, TFAM, UTP14A, ZNF280C, IFT57 and NDUFB5) between 130 ADHD patients and 70 controls, and we built an ADHD prediction model based on the ΔCt values of aforementioned seven genes (AUROC = 0.98). Finally, in a validation group (28 patients with ADHD and 27 healthy controls), mRNA expression of the above seven genes also significantly differentiated ADHD patients from controls (AUROC value = 0.91). The DTI analysis showed increased fractional anisotropy (FA) of the forceps minor, superior corona radiata, posterior corona radiata and anterior corona radiata in ADHD patients. Moreover, the FA of the right superior corona radiata tract was positively correlated with ΔCt levels of the COX7B gene and the IFT57 gene. The results shed a new light on a genetic profile of ADHD that may help in deciphering the white matter microstructural features in disease pathogenesis.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Sex differences in atypical fronto-subcortical structural connectivity among children with attention-deficit/hyperactivity disorder: Associations with delay discounting

PURPOSE

Atypical fronto-subcortical neural circuitry has been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD), including connections between prefrontal cortical regions involved in top-down cognitive control and subcortical limbic structures (striatum and amygdala) involved in bottom-up reward and emotional processing. The integrity of fronto-subcortical connections may also relate to interindividual variability in delay discounting, or a preference for smaller, immediate over larger, delayed rewards, which is associated with ADHD, with recent evidence of ADHD-related sex differences.

METHODS

We applied diffusion tensor imaging to compare the integrity of the white matter connections within fronto-subcortical tracts among 187 8-12 year-old children either with ADHD ((n = 106; 29 girls) or typically developing (TD) controls ((n = 81; 28 girls). Analyses focused on diagnostic group differences in fractional anisotropy (FA) within fronto-subcortical circuitry implicated in delay discounting, connecting subregions of the striatum (dorsal executive and ventral limbic areas) and amygdala with prefrontal regions of interest (dorsolateral [dlPFC], orbitofrontal [OFC] and anterior cingulate cortex [ACC]), and associations with two behavioral assessments of delay discounting.

RESULTS

Children with ADHD showed reduced FA in tracts connecting OFC with ventral striatum, regardless of sex, whereas reduced FA in the OFC-amygdala and ventral ACC-amygdala tracts were specific to boys with ADHD. Across diagnostic groups and sex, reduced FA in the dorsal ACC-executive striatum tract correlated with greater game time delay discounting.

CONCLUSIONS

These results suggest a potential neurobiological substrate of heightened delay discounting in children with ADHD and support the need for additional studies including larger sample sizes of girls with ADHD to further elucidate ADHD-related sex differences in these relationships.

Brain white matter correlates of learning ankle tracking using a wearable device: importance of the superior longitudinal fasciculus II

Background

Wearable devices have been found effective in training ankle control in patients with neurological diseases. However, the neural mechanisms associated with using wearable devices for ankle training remain largely unexplored. This study aimed to investigate the ankle tracking performance and brain white matter changes associated with ankle tracking learning using a wearable-device system and the behavior–brain structure relationships in middle-aged and older adults.

Methods

Twenty-six middle-aged and older adults (48–75 years) participated in this study. Participants underwent 5-day ankle tracking learning with their non-dominant foot using a custom-built ankle tracking system equipped with a wearable sensor and a sensor-computer interface for real-time visual feedback and data acquisition. Repeated and random sequences of target tracking trajectories were both used for learning and testing. Ankle tracking performance, calculated as the root-mean-squared-error (RMSE) between the target and actual ankle trajectories, and brain diffusion spectrum MR images were acquired at baseline and retention tests. The general fractional anisotropy (GFA) values of eight brain white matter tracts of interest were calculated to indicate their integrity. Two-way (Sex × Time) mixed repeated measures ANOVA procedures were used to investigate Sex and Time effects on RMSE and GFA. Correlations between changes in RMSE and those in GFA were analyzed, controlling for age and sex.

Results

After learning, both male and female participants reduced the RMSE of tracking repeated and random sequences (both p < 0.001). Among the eight fiber tracts, the right superior longitudinal fasciculus II (R SLF II) was the only one which showed both increased GFA (p = 0.039) after learning and predictive power of reductions in RMSE for random sequence tracking with its changes in GFA [β = 0.514, R² change = 0.259, p = 0.008].

Conclusions

Our findings implied that interactive tracking movement learning using wearable sensors may place high demands on the attention, sensory feedback integration, and sensorimotor transformation functions of the brain. Therefore, the SLF II, which is known to perform these brain functions, showed corresponding neural plasticity after such learning, and its plasticity also predicted the behavioral gains. The SLF II appears to be a very important anatomical neural correlate involved in such learning paradigms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01042-2.

Clinical and Socio-Demographic Variables Associated with the Diagnosis of Long COVID Syndrome in Youth: A Population-Based Study

This study examines the demographic, clinical and socioeconomic factors associated with diagnosis of long COVID syndrome (LCS). Data of 20,601 COVID-19-positive children aged 5 to 18 years were collected between 2020 and 2021 in an Israeli database. Logistic regression analysis was used to evaluate the adjusted odds ratio for the characteristics of the COVID-19 infection and pre-COVID-19 morbidities. Children with LCS were significantly more likely to have been severely symptomatic, required hospitalization, and experienced recurrent acute infection within 180 days. In addition, children with LCS were significantly more likely to have had ADHD, chronic urticaria, and allergic rhinitis. Diagnosis of LCS is significantly associated with pre-COVID-19 ADHD diagnosis, suggesting clinicians treating ADHD children who become infected with COVID-19 remain vigilant for the possibility of LCS. Although the risk of severe COVID-19 infection and LCS in children is low, further research on possible morbidity related to LCS in children is needed.