In the current study, the MR quantitative indices of GM regions were compared to show substantial differences between individuals with drug-naïve ADHD and typical healthy controls. The main differences between ADHD patients and control individuals were: 1) widespread microstructure changes in cortical and subcortical regions were observed in ADHD patients; 2) GM regions, including cerebellum, default mode network (DMN), attention and execution control network (EAN), limbic areas, etc., have been implicated in the disorder; 3) focal GM regions signal changes correlated to clinical attention concentration level were also found in our study, like left cerebellum 8, left fusiform gyrus, and right cerebellum crus2.
T1, T2 values charting cortical microstructure
As a kind of developmental disorder, ADHD has been reported to correlate with brain structure development retardation and abnormal neural network interplay [6, 7, 19]. Grey matter volume reductions in the ventrolateral prefrontal/insular-striatal regions, such as the right insula, putamen, globus pallidus, and caudate nucleus, have been found in ADHD patients [20, 21]. Voxel-based morphometry (VBM) analyses of unaffected siblings of ADHD cases have identified smaller GM volume in the prefrontal cortex, medial and orbitofrontal cortex, fronto-occipital regions, and cingulate regions in ADHD patients compared to healthy controls [22]. In our study, changes in MRI relaxometry times of these regions have also been observed in ADHD patients, which may reflect macrostructure changes and microstructure alteration in these regions.
The development of the neuron fiber myelination process has a critical role in brain development. Although myelin is more abundant in white matter, a considerable quantity of myelinated fibers can be found in cortical GM [9, 14]. In our observation, widespread microstructure changes in cortical and subcortical regions were detected via T1 and T2 map, especially the T2 map. Increased T1value of left cerebellum 7b/8 and right cerebellum 7b/8 in ADHD patients can be explained by low myelin content and iron deficiency. Moreover, we also found a weak, significant negative correlation between significantly altered T1 relaxation times in the left cerebellum 8 and attention concentration level, which provide support for our assumption. For further study, more specific iron-target sequence, for example, R2*, susceptibility weighted imaging (SWI) or post-processing quantitative susceptibility mapping (QSM) may use to explore the between-group iron content differences.
The longer T2 relaxation times of GM found in ADHD could also be explained by low concentration of myelin; water trapped between the myelin layers has a shorter T2 relaxation time than water in the intra- and extra-cellular compartments [12, 15]. Thus, the increased T2 relaxation times of the cerebellum, EAN, DMN, and limbic areas found in our study may suggest a relatively low concentration of myelin content and increased water content. Furthermore, the significant correlation relationships between T2 relaxation times of the left fusiform gyrus and right cerebellum crus2 and the symptom severity indicated that these regions might have critical roles in ADHD pathogenesis.
Apart from GM's lower myelination degree and correspondingly increased water content, the increased vascular interstitial space due to developmental retardation may lead to signal change. In previous study, perivascular spaces had been well realized in the neurodevelopmental degeneration and neurological diseases[23], but regards to the developmental disorder, we have poor knowledge of its role related to the pathology and physiology in ADHD. Thus, further studies are needed to clarify the related pathology occurred in ADHD. Moreover, the interference caused by individual age and potential brain development bias is hard to be diminished. In summary, the quantitative MR imaging could reflect the brain tissue microstructure changes, thus furthering our knowledge of the brain changes in ADHD.
The varied brain regions and the network involved
The altered brain GM regions have been mainly classified as located in some specific functional regions [24, 25], including cerebellum, default mode network (DMN), attention and execution control network (EAN), limbic areas, etc. In our study, higher T2 values of right cerebellum crus II/7b, left fusiform, and higher T1 values of left cerebellum 7b/8, right cerebellum 7b/8 have been found in ADHD compared to healthy controls, which greatly overlapped with recent findings suggesting that significant gray matter network reductions occurred in bilateral crus I, left lingual gyrus, left crus II, and left fusiform in ADHD [7]. Longer T2 belongs to DMN and EAN regions, including prefrontal, parietal, cingulate, precuneus, and rectus lobes, which were also found in our study. A previous study suggested that the inverse correlation of DMN and the cognitive control networks were diminished or absent in ADHD-related children and adults [26, 27]. In addition, a more diffuse pattern of resting-state network connectivity and delayed functional network development in children with ADHD have been reported [19].Significant increase in T2 values of limbic areas, including cingulate, insula, putamen, and temporal lobe, was also observed in ADHD, which was consistent with previous studies (meta- and mega-analysis of subcortical structural imaging studies) that found additional volume reductions in basal ganglia, insula [19, 28], amygdala and hippocampus [5], as well as entire cingulate cortex structure/function abnormalities in patients with ADHD [29]. More than structure/function abnormalities, our findings indicated microstructure alterations in these regions. In general, the microstructure alteration located in the specific GM regions may be the pathophysiology basis for the structure/function abnormalities.
In the present study, we propose that apart from GM structure/function abnormalities in ADHD, GM microstructure abnormalities may also have an important role in the underlying mechanism of ADHD. Besides, some other networks were found to be involved in having signal values alternation in our study, including the visual network (VN), sensorimotor network (SMN), and subcortical areas. However, these other regions were rather limited and thus are not discussed in detail.
This study has a few limitations. First, the sample size was relatively small. Besides, though ADHD can be divided into inattention, hyperactive and combined ADHD subtype, the difference between subtypes was not analyzed in the present study. Therefore, extensive studies with larger populations and long-term follow-up analysis are needed to further confirm the effectiveness of T1, T2 mapping in understanding ADHD GM microstructure changes.