Share：
Share this content in WeChat
X
[Chinese][PDF]7301
• Clinical Article •
Assessment of attention deficit symptoms in ADHD based on T1W-MRI radiomics brain network
ZHAO Lei  WANG Xunheng  FAN Ming  LI Lihua 

Cite this article as: ZHAO L, WANG X H, FAN M, et al. Assessment of attention deficit symptoms in ADHD based on T1W-MRI radiomics brain network[J]. Chin J Magn Reson Imaging, 2025, 16(5): 54-61. DOI:10.12015/issn.1674-8034.2025.05.009.


[Abstract] Objective To predict attention deficit symptoms in attention deficit hyperactivity disorder (ADHD) based on T1-weighted MRI (T1W-MRI) and to explore brain regions and brain network connections that are significantly associated with the symptoms.Materials and Methods The subjects of this experiment included 21 groups of repeated-measurement healthy individuals from Vanderbilt University and 38 patients with combined type of ADHD from Peking University. After obtaining the brain T1W-MRI of each subject, the images were preprocessed to obtain standardized data. The voxel-level cortical thickness morphological features and corresponding radiomics features were extracted, and the reliability of the features was evaluated using the intra-class correlation coefficient (ICC). The nodal features of an individualized brain morphological network were constructed based on the radiomics sorted in descending order by the mean ICC of the whole brain and the Desikan-Killiany (DK) brain atlas, and the individualized brain morphological connections were characterized by feature distance similarity. The support vector regression (SVR) model was used to predict attention deficit symptoms, and the model performance was evaluated by leave-one-out cross-validation.Results The correlation between attention deficit symptoms and the predicted values was r = 0.44 (P = 0.01). The predictive model showed that the brain region-related connections were centered on the right lateral occipital cortex and the right temporal transverse cortex. The findings of these significant brain regions and network connections support the hypothesis of abnormal prefrontal cortex function and default mode network abnormality in ADHD.Conclusions Individualized brain morphology networks based on cortical thickness radiomics features can effectively characterize the topological structure of the brain and have the potential to become a new imaging marker for assessing attention deficit.
[Keywords] attention deficit hyperactivity disorder；magnetic resonance imaging；cortical thickness；radiomics；individualized morphometric network

ZHAO Lei   WANG Xunheng*   FAN Ming   LI Lihua  

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Corresponding author: WANG X H, E-mail: xhwang@hdu.edu.cn

Conflicts of interest   None.

Received  2024-12-24
Accepted  2025-05-10
DOI: 10.12015/issn.1674-8034.2025.05.009
Cite this article as: ZHAO L, WANG X H, FAN M, et al. Assessment of attention deficit symptoms in ADHD based on T1W-MRI radiomics brain network[J]. Chin J Magn Reson Imaging, 2025, 16(5): 54-61. DOI:10.12015/issn.1674-8034.2025.05.009.

[1]
FELDMAN H M, REIFF M I. Clinical practice. Attention Deficit–Hyperactivity Disorder in Children and Adolescents[J]. N Engl J Med, 2014, 370(9): 838-846. DOI: 10.1056/NEJMcp1307215.
[2]
OLFSON M, WALL M M, WANG S, et al. Prevalence and Correlates of Mental Disorders in Children Aged 9 and 10 Years: Results From the ABCD Study[J/OL]. J Am Acad Child Adolesc Psychiatry, 2023, 62(8): 908-919. DOI: 10.1016/j.jaac.2023.04.005.
[3]
AURO K, HOLOPAINEN I, PEROLA M, et al. Attention-Deficit/Hyperactivity Disorder Diagnoses in Finland During the COVID-19 Pandemic[J/OL]. JAMA Network Open, 2024, 7(6): e2418204 [2024-12-24]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11211961/. DOI: 10.1001/jamanetworkopen.2024.18204.
[4]
YANG W, ZHANG W. Research progress on attention level evaluation based on electroencephalogram signals[J]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023, 40(4): 820-828. DOI: 10.7507/1001-5515.202208085.
[5]
PETERSON B S, TRAMPUSH J, BROWN M, et al. Tools for the diagnosis of ADHD in children and adolescents: a systematic review[J/OL]. Pediatrics, 2024, 153(4): e2024065854 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/38523599/. DOI: 10.1542/peds.2024-065854.
[6]
ROSENBERG M D, SCHEINOST D, GREENE A S, et al. Functional connectivity predicts changes in attention observed across minutes, days, and months[J]. Proc Natl Acad Sci U S A, 2020, 117(7): 3797-3807. DOI: 10.1073/pnas.1912226117.
[7]
YOO K, ROSENBERG M D, KWON Y H, et al. A brain-based general measure of attention[J]. Nat Hum Behav, 2022, 6(6): 782-795. DOI: 10.1038/s41562-022-01301-1.
[8]
TOZER D J, PFLANZ C P, MARKUS H S. Reproducibility of regional structural and functional MRI networks in cerebral small vessel disease compared to age matched and stroke-free controls[J/OL]. Cereb Circ Cogn Behav, 2023, 4: 100167 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/37397269/. DOI: 10.1016/j.cccb.2023.100167.
[9]
MAIER S, PHILIPSEN A, PERLOV E, et al. Left insular cortical thinning differentiates the inattentive and combined subtype of adult attention-deficit/hyperactivity disorder[J]. J Psychiatr Res, 2023, 159: 196-204. DOI: 10.1016/j.jpsychires.2023.01.030.
[10]
SARABIN E, HARKNESS K, MURIAS K. The Relationship Between Cortical Thickness and Executive Function Measures in Children With and Without ADHD[J]. J Atten Disord, 2023, 27(11): 1263-1271. DOI: 10.1177/10870547231174036.
[11]
WANG J, HE Y. Toward individualized connectomes of brain morphology[J]. Trends Neurosci, 2023. 2024, 47(2):106-119. DOI: 10.1016/j.tins.2023.11.011.
[12]
LIN L, CHANG Z, ZHANG Y, et al. Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies[J/OL]. Neuroimage, 2024, 297: 120688 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/38878916/. DOI: 10.1016/j.neuroimage.2024.120688.
[13]
KIM S, KIM Y W, JEON H, et al. Altered cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder[J/OL]. J Clin Med, 2020, 9(6): 1846 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/32545747/. DOI: 10.3390/jcm9061846.
[14]
JI Y, PEARLSON G, BUSTILLO J, et al. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering[J]. Schizophr Res, 2023, 264: 130-139. DOI: 10.1016/j.schres.2023.12.013.
[15]
CAI M, MA J, WANG Z, et al. Individual‐level brain morphological similarity networks: Current methodologies and applications[J]. CNS Neurosci Ther, 2023, 29(12): 3713-3724. DOI: 10.1111/cns.14384.
[16]
VAN TIMMEREN J E, CESTER D, TANADINI-LANG S, et al. Radiomics in medical imaging—"how-to" guide and critical reflection[J/OL]. Insights Imaging, 2020, 11(1): 91 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/32785796/. DOI: 10.1186/s13244-020-00887-2.
[17]
LIU G, LU W, QIU J, et al. Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis[J]. Hum Brain Mapp, 2023, 44(8): 3433-3445. DOI: 10.1002/hbm.26290.
[18]
SHI L, LIU X, WU K, et al. Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention‐deficit/hyperactivity disorder[J]. Eur J Neurosci, 2021, 54(10): 7654-7667. DOI: 10.1111/ejn.15485.
[19]
FARAONE SV, BANASCHEWSKI T, COGHILL D, et al. The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder[J]. Neurosci Biobehav Rev, 2021, 128: 789-818. DOI: 10.1016/j.neubiorev.2021.01.022.
[20]
LI Z, FAN Q, BILGIC B, et al. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat)[J/OL]. Med Image Anal, 2023, 86: 102744 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/36867912/. DOI: 10.1016/j.media.2023.102744.
[21]
ZHAO Z, NIE C, ZHAO L, et al. Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas[J]. Eur Radiol, 2023, 34(4): 2468-2479. DOI: 10.1007/s00330-023-10252-8.
[22]
WEI L, DONAIRE-GONZALEZ D, HELBICH M, et al. Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment[J]. Environ Sci Technol, 2024, 58(24): 10685-10695. DOI: 10.1021/acs.est.3c10867.
[23]
LORCA-PULS D L, GAJARDO-VIDAL A, MANDELLI M L, et al. Neural basis of speech and grammar symptoms in non-fluent variant primary progressive aphasia spectrum[J]. Brain, 2024, 147(2): 607-626. DOI: 10.1093/brain/awad327.
[24]
ROSENBERG M D, FINN E S, SCHEINOST D, et al. A neuromarker of sustained attention from whole-brain functional connectivity[J]. Nat Neurosci, 2016, 19(1): 165-171. DOI: 10.1038/nn.4179.
[25]
SHEN X, FINN E S, SCHEINOST D, et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity[J]. Nat Protoc, 2017, 12(3): 506-518. DOI: 10.1038/nprot.2016.178.
[26]
LI Y, JIANG J, LU J, et al. Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18 F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment[J/OL]. Ther Adv Neurol Disord, 2019, 12: 175628641983868 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/30956687/. DOI: 10.1177/1756286419838682.
[27]
SUN H, CHEN Y, HUANG Q, et al. Psychoradiologic Utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis[J]. Radiology, 2018, 287(2): 620-630. DOI: 10.1148/radiol.2017170226.
[28]
TOMASZEWSKI M R, GILLIES R J. The Biological Meaning of Radiomic Features[J]. Radiology, 2021, 298(3): 505-516. DOI: 10.1148/radiol.2021202553.
[29]
LIU G, LU W, QIU J, et al. Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis[J]. Hum Brain Mapp, 2023, 44(8): 3433-3445. DOI: 10.1002/hbm.26290.
[30]
WANG C, SHEN Y, CHENG M, et al. Cortical gray-white matter contrast abnormalities in male children with attention deficit hyperactivity disorder[J/OL]. Front Hum Neurosci, 2023, 17: 1303230 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/38188507/. DOI: 10.3389/fnhum.2023.1303230.
[31]
POSTEMA M C, HOOGMAN M, AMBROSINO S, et al. Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets[J]. J Child Psychol Psychiatry, 2021, 62(10): 1202-1219. DOI: 10.1111/jcpp.13396.
[32]
AERTS H J W L, VELAZQUEZ E R, LEIJENAAR R T H, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J/OL]. Nat Commun, 2014, 5(1): 4006 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/24892406/. DOI: 10.1038/ncomms5006.
[33]
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[34]
LAMBIN P, LEIJENAAR R T, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[35]
ZHAO K, ZHENG Q, CHE T, et al. Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis[J]. Netw Neurosci, 2021, 5(3): 783-797. DOI: 10.1162/netn_a_00200.
[36]
TENG X, WANG Y, NICOL A J, et al. Enhancing the clinical utility of radiomics: Addressing the challenges of repeatability and reproducibility in CT and MRI[J/OL]. Diagnostics (Basel), 2024, 14(16): 1835 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/39202322/. DOI: 10.3390/diagnostics14161835.
[37]
MU W, SCHABATH M B, GILLIES R J. Images are data: Challenges and opportunities in the clinical translation of radiomics[J]. Cancer Res, 2022, 82(11): 2066-2068. DOI: 10.1158/0008-5472.CAN-22-1183.
[38]
LOHANI D C, RANA B. ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework[J/OL]. Psychiatry Res Neuroimaging, 2023, 334: 111689 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/37536046/. DOI: 10.1016/j.pscychresns.2023.111689.
[39]
FERRANTI A S, LUESSEN D J, NISWENDER C M. Novel pharmacological targets for GABAergic dysfunction in ADHD[J/OL]. Neuropharmacology, 2024, 249: 109897 [2024-12-24]. https://pubmed.ncbi.nlm.nih.gov/38462041/. DOI: 10.1016/j.neuropharm.2024.109897.
[40]
LUKITO S, NORMAN L, CARLISI C, et al. Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder[J]. Psychol Med, 2020, 50(6): 894-919. DOI: 10.1017/S0033291720000574.
[41]
VALERA E M, FARAONE S V, MURRAY K E, et al. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder[J]. Biol Psychiatry, 2007, 61(12): 1361-1369. DOI: 10.1016/j.biopsych.2006.06.011.
[42]
NARR K L, WOODS R P, LIN J, et al. Widespread cortical thinning is a robust anatomical marker for attention-deficit/hyperactivity disorder[J]. J Am Acad Child Adolesc Psychiatry, 2009, 48(10): 1014-1022. DOI: 10.1097/CHI.0b013e3181b395c0.
[43]
NORMAN L J, SUDRE G, PRICE J, et al. Evidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples[J]. Neuropsychopharmacology, 2023, 48(2): 281-289. DOI: 10.1038/s41386-022-01408-z.
[44]
CURTIN P, NEUFELD J, CURTIN A, et al. Altered periodic dynamics in the default mode network in autism and attention-deficit/hyperactivity disorder[J]. Biol Psychiatry, 2022, 91(11): 956-966. DOI: 10.1016/j.biopsych.2022.01.010.
[45]
SEGAL A, PARKES L, AQUINO K, et al. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders[J]. Nat Neurosci, 2023, 26(9): 1613-1629. DOI: 10.1038/s41593-023-01404-6.
[46]
CORTESE S, AOKI Y Y, ITAHASHI T, et al. Systematic review and meta-analysis: resting-state functional magnetic resonance imaging studies of attention-deficit/hyperactivity disorder[J]. J Am Acad Child Adolesc Psychiatry, 2021, 60(1): 61-75. DOI: 10.1016/j.jaac.2020.08.014.

PREV Study on the characteristics of resting-state and dynamic functional connectivity of the bilateral amygdala in generalized anxiety disorder: A functional MRI research
NEXT The diffusion kurtosis imaging analysis of white matter microstructural features in children with attention deficit hyperactivity disorder
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn