Atypical development in white matter microstructures in ADHD: A longitudinal diffusion imaging study

Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.

Exploring Neuroimaging Association Scores in adulthood ADHD and middle-age trajectories

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder associated with brain differences in children, but not in adults. A combined evaluation of the regional brain differences could improve statistical power and, consequently, allow the detection of possible effects in adults. Thus, our aim is to verify whether Neuroimaging Association Scores (NAS) are associated with adulthood ADHD and clinical trajectories of the disorder in midlife. Clinical and neuroimaging data were collected for 121 subjects with ADHD (mean age: 47.1 ± 10.5; 43% male) and 82 controls (mean age: 38.2 ± 9.0; 54.9% male). Cases were assessed seven and thirteen years after baseline diagnosis, and their clinical trajectories were classified as stable if they fulfilled ADHD diagnosis in all assessments or unstable if they presented remission and recurrence of symptoms. Neuroimaging data were acquired in the last clinical assessment (thirteen years after baseline) and NAS were calculated as a weighted sum of the associations previously reported by meta-analyses for three types of structural brain modalities: cortical thickness, cortical surface area, and subcortical volume. The NAS for cortical surface area was higher in cases compared to controls. No association was found for NAS and number of symptoms of ADHD or clinical trajectories. The fact that differences were restricted to ADHD diagnostic status suggests a susceptibility effect that is not extended to subtle aspects of the disorder. Our results also suggest that evaluating overall effects may have advantages especially when applied to adult ADHD samples.

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study

BACKGROUND

We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyperactivity disorder (ADHD) from those without ADHD using a machine-learning approach.

METHODS

Fifty-one ADHD patients and 60 typically developing controls (TDC) underwent diffusion spectrum imaging at two time points. We evaluated three models to classify ADHD and TDC using various machine-learning algorithms. Model 1 employed baseline white matter features of 45 white matter tracts at Time 1; Model 2 incorporated features from both time points; and Model 3 (main analysis) further included the relative rate of change per year of white matter tracts.

RESULTS

The random forest algorithm demonstrated the best performance for classification. Model 1 achieved an area-under-the-curve (AUC) of 0.67. Model 3, incorporating Time 2 variables and relative rate of change per year, improved the performance (AUC = 0.73). In addition to identifying several white matter features at two time points, we found that the relative rate of change per year in the superior longitudinal fasciculus, frontal aslant tract, stria terminalis, inferior fronto-occipital fasciculus, thalamic and striatal tracts, and other tracts involving sensorimotor regions are important features of ADHD. A higher relative change rate in certain tracts was associated with greater improvement in visual attention, spatial short-term memory, and spatial working memory.

CONCLUSIONS

Our findings support the significant diagnostic value of white matter microstructure and the developmental change rates of specific tracts, reflecting deviations from typical development trajectories, in identifying ADHD.

Distinct developmental changes in regional gray matter volume and covariance in individuals with attention-deficit hyperactivity disorder: A longitudinal voxel-based morphometry study

BACKGROUND

Very few studies have investigated longitudinal clinical cohorts of attention-deficit/hyperactivity disorder (ADHD). Moreover, how baseline brain changes could affect the development of ADHD symptoms later in life remains elusive. Therefore, we aimed to fill this gap by exploring brain and clinical changes in youth with ADHD using a longitudinal design.

METHODS

This prospective study consisted of 74 children and adolescents with ADHD and 50 age-, sex-, intelligence-matched typically developing controls (TDC), evaluated at baseline (aged 7-19 years) and re-evaluated 5.3 years later (a mean follow-up latency). We applied voxel-based morphometry to characterize brain structures, followed by both mass-univariate and multivariate structural covariance statistics to identify brain regions with significant diagnosis-by-time interactions from late childhood/adolescence to early adulthood. We used the cross-lagged panel model to investigate the longitudinal association between structural brain metrics and core ADHD symptoms.

RESULTS

The mass-univariate statistic revealed significant diagnosis-by-time interactions in the right striatum and the sixth lobule of the cerebellum. This was expressed by increased striatal and decreased cerebellar volume in ADHD, while TDC showed inverse volume changes over time. The multivariate method showed significant diagnosis-by-time interactions in a structural covariance network consisting of the regions involved in the functional sensory-motor and default-mode networks. Higher baseline right striatal and cerebellar volumes were associated with elevated ADHD symptoms at follow-up.

CONCLUSIONS

Our findings suggest a temporal association between the divergent development of striatal and cerebellar regions and dynamical ADHD phenotypic expression through young adulthood. These results highlight a potential brain marker of future outcomes.

Adolescents with ADHD and co-occurring motor difficulties show a distinct pattern of maturation within the corticospinal tract from those without: A longitudinal fixel-based study

It is well documented that attention-deficit hyperactivity disorder (ADHD) often presents with co-occurring motor difficulties. However, little is known about the biological mechanisms that explain compromised motor skills in approximately half of those with ADHD. To provide insight into the neurobiological basis of poor motor outcomes in ADHD, this study profiled the development of white matter organization within the cortico-spinal tract (CST) in adolescents with ADHD with and without co-occurring motor problems, as well as non-ADHD control children with and without motor problems. Participants were 60 children aged 9-14 years, 27 with a history of ADHD and 33 controls. All underwent high-angular resolution diffusion MRI data at up to three time points (115 in scans total). We screened for motor impairment in all participants at the third time point (≈14 years) using the Developmental Coordination Disorder Questionnaire (DCD-Q). Following pre-processing of diffusion MRI scans, fixel-based analysis was performed, and the bilateral CST was delineated using TractSeg. Mean fiber density (FD) and fiber cross-section (FC) were extracted for each tract at each time-point. To investigate longitudinal trajectories of fiber development, linear mixed models were performed separately for the left and right CST, controlling for nuisance variables. To examine possible variations in fiber development between groups, we tested whether the inclusion of group and the interaction between age and group improved model fit. At ≈10 years, those with ADHD presented with lower FD within the bilateral CST relative to controls, irrespective of their prospective motor status. While these microstructural abnormalities persisted into adolescence for individuals with ADHD and co-occurring motor problems, they resolved for those with ADHD alone. Divergent maturational pathways of motor networks (i.e., the CST) may, at least partly, explain motor problems individuals with ADHD.

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.

Adult Attention-Deficit/Hyperactivity Disorder: a Narrative Review of Biological Mechanisms, Treatments, and Outcomes

PURPOSE OF REVIEW

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous and complex neurodevelopmental disorder related to disruptions in various neuronal structures and pathways, dopamine (DA) transporter, and receptor genes, resulting in cognitive and regulation deficits. This article reviews recent research on the biological mechanisms and markers, clinical manifestations, treatments, and outcomes of adult ADHD as well as current controversies within the field.

RECENT FINDINGS

New research identifies white matter disruptions in multiple cortical pathways in adults with ADHD. New treatments for ADHD in adults such as viloxazine ER have shown preliminary effectiveness in addition to research showing transcranial direct current stimulation can be an effective treatment for adults with ADHD. Although questions exist about the effectiveness of current assessments of and treatments for adult ADHD, recent findings represent a step towards improving the quality of life and outcomes for individuals experiencing this life-long, chronic health condition.