ArticleQuantitative imaging in medicine and surgery2026
Investigation of quantitative synthetic MRI in the evaluation of attention-deficit/hyperactivity disorder.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Currently, synthetic magnetic resonance imaging (SyMRI) has been considered as a promising way to characterize intrinsic microstructural tissue alterations by quantitatively measuring tissue relaxation times. This study aimed to explore the utility of combining machine learning algorithms with SyMRI for identifying attention-deficit/hyperactivity disorder (ADHD) and further revealing its underlying brain pathology. Methods: Multiple quantitative parameters (T1 and T2 relaxometry values and myelin volume fraction) of whole-brain gray and white matter derived from SyMRI data on 50 individuals with ADHD and 50 age-, sex-matched healthy controls (HCs) were used as original features. Feature selection was performed by a two-sample Results: Machine learning model based on combined features demonstrated stable performance across different classifiers [support vector machine, random forest and linear regression yielding accuracies of 0.780, 0.770, and 0.780 (all P<0.001), respectively] and showed superior performance compared to those based on single feature [accuracy of 0.730 (gray matter) and 0.770 (white matter) with support vector machine (both P<0.001)]. The most discriminative features mainly involved the macrostructure of the frontal, temporal lobes and the cerebellum, and the microstructure of the corpus callosum, frontostriatal tracts, corticospinal tract and cingulum. Moreover, the T2 relaxation time of the left cingulum was associated with symptom severity in ADHD (r=0.285, P=0.050). Conclusions: Our study demonstrated that multi-parametric quantitative metrics derived from SyMRI combined with machine learning not only achieved good classification accuracy for distinguishing ADHD from HCs but also revealed widespread microscopic changes of gray and white matter underlying ADHD. These findings highlight the potential of using SyMRI to provide a novel perspective for exploring the disease-related microstructural processes and detecting the promising imaging marker for early diagnosis of ADHD.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.