Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning.

Publisher: Lippincott Williams & Wilkins Country of Publication: England NLM ID: 9100935 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1473-558X (Electronic) Linking ISSN: 09594965 NLM ISO Abbreviation: Neuroreport Subsets: MEDLINE

Publication: London, England : Lippincott Williams & Wilkins
Original Publication: Oxford, UK : Rapid Communications of Oxford Ltd., [1990-

Retinal Detachment*/physiopathology ; Retinal Detachment*/diagnostic imaging ; Magnetic Resonance Imaging*/methods ; Primary Visual Cortex*/physiopathology ; Primary Visual Cortex*/diagnostic imaging; Humans ; Male ; Female ; Adult ; Middle Aged ; Machine Learning ; Connectome/methods ; Nerve Net/diagnostic imaging ; Nerve Net/physiopathology ; Support Vector Machine ; Young Adult

Objective: Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated.
Methods: In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs).
Results: RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786.
Conclusion: This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.
(Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Mozaffarieh M, Sacu S, Benesch T, Wedrich A. Mental health measures of anxiety and depression in patients with retinal detachment. Clin Pract Epidemiol Ment Health 2007; 3:10.
Lee CY, Chen HC, Huang JY, Lai CC, Yang SF, Wu WC. Elevated risk of mood disorders after the occurrence of recurrent retinal detachment: a population-based cohort study. Ophthalmologica 2022; 245:249–257.
van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 2010; 20:519–534.
Zang ZX, Yan CG, Dong ZY, Huang J, Zang YF. Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing. J Neurosci Methods 2012; 203:418–426.
Xue J, Guo H, Gao Y, Wang X, Cui H, Chen Z, et al. Altered directed functional connectivity of the hippocampus in mild cognitive impairment and Alzheimer’s disease: a resting-state fMRI study. Front Aging Neurosci 2019; 11:326.
Song Z, Chen J, Wen Z, Zhang L. Abnormal functional connectivity and effective connectivity between the default mode network and attention networks in patients with alcohol-use disorder. Acta Radiol 2021; 62:251–259.
Zhou GP, Chen YC, Li WW, Wei H-L, Yu Y-S, Zhou Q-Q, et al. Aberrant functional and effective connectivity of the frontostriatal network in unilateral acute tinnitus patients with hearing loss. Brain Imaging Behav 2022; 16:151–160.
Liu X, Duyn JH. Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci U S A 2013; 110:4392–4397.
Ozer ME, Sarica PO, Arga KY. New machine learning applications to accelerate personalized medicine in breast cancer: rise of the support vector machines. OMICS 2020; 24:241–246.
Santana CP, de Carvalho EA, Rodrigues ID, Bastos GS, de Souza AD, de Brito LL. rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis. Sci Rep 2022; 12:6030.
Zhao Z, Chuah JH, Lai KW, Chow CO, Gochoo M, Dhanalakshmi S, et al. Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: a review. Front Comput Neurosci 2023; 17:1038636.
Guo CY, Chou YC. A novel machine learning strategy for model selections - stepwise support vector machine (StepSVM). PLoS One 2020; 15:e0238384.
An Z, Tang K, Xie Y, Tong C, Liu J, Tao Q, Feng Y; DIRECT Consortium. Aberrant resting-state co-activation network dynamics in major depressive disorder. Transl Psychiatry 2024; 14:1.
Wu J, Ma L, Luo D, Jin Z, Wang L, Wang L, et al. Functional and structural gradients reveal atypical hierarchical organization of Parkinson’s disease. Hum Brain Mapp 2024; 45:e26647.
Zhong YL, Hu RY, Huang X. Aberrant neurovascular coupling in diabetic retinopathy using arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) methods. Diabetes Metab Syndr Obes 2024; 17:2809–2822.
Jia XZ, Wang J, Sun HY, Zhang H, Liao W, Wang Z, et al. RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. Sci Bull (Beijing) 2019; 64:953–954.
Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement-related effects in fMRI time-series. Magn Reson Med 1996; 35:346–355.
Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 2013; 76:183–201.
Ji Y, Huang SQ, Cheng Q, Fu W-W, Zhong P-P, Chen X-L, et al. Exploration of static functional connectivity and dynamic functional connectivity alterations in the primary visual cortex among patients with high myopia via seed-based functional connectivity analysis. Front Neurosci 2023; 17:1126262.
Liao W, Wu GR, Xu Q, Ji G-J, Zhang Z, Zang Y-F, Lu G. DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connect 2014; 4:780–790.
Glover GH. Overview of functional magnetic resonance imaging. Neurosurg Clin N Am 2011; 22:133–139, vii.
Wu GR, Colenbier N, Van Den Bossche S, Clauw K, Johri A, Tandon M, Marinazzo D. rsHRF: A toolbox for resting-state HRF estimation and deconvolution. Neuroimage 2021; 244:118591.
Wu GR, Liao W, Stramaglia S, Ding JR, Chen H, Marinazzo D. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med Image Anal 2013; 17:365–374.
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 2014; 24:663–676.
Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 2009; 45(1 Suppl):S199–S209.
Wang K, Jiang T, Yu C, Tian L, Li J, Liu Y, et al. Spontaneous activity associated with primary visual cortex: a resting-state FMRI study. Cereb Cortex 2008; 18:697–704.
Menon V. 20 years of the default mode network: a review and synthesis. Neuron 2023; 111:2469–2487.
Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. Alcohol Clin Exp Res (Hoboken) 2023; 47:687–703.
Li F, Lu L, Shang S, Chen H, Wang P, Muthaiah VP, et al. Altered static and dynamic functional network connectivity in post-traumatic headache. J Headache Pain 2021; 22:137.
Huang X, Tong Y, Qi CX, Dan HD, Deng QQ, Shen Y. Large-scale neuronal network dysfunction in diabetic retinopathy. Neural Plast 2020; 2020:6872508.
Jin H, Chen RB, Zhong YL, Lai PH, Huang X. Effect of impaired stereoscopic vision on large-scale resting-state functional network connectivity in comitant exotropia patients. Front Neurosci 2022; 16:833937.
Vu TA, Fenwick EK, Gan A, Man R, Tan B, Gupta P, et al. The bidirectional relationship between vision and cognition: a systematic review and meta-analysis. Ophthalmology 2021; 128:981–992.
Kang HH, Shu YQ, Yang L, Zhu PW, Li D, Li QH, et al. Measuring abnormal intrinsic brain activities in patients with retinal detachment using amplitude of low-frequency fluctuation: a resting-state fMRI study. Int J Neurosci 2019; 129:681–686.
Shao Y, Yang L, Zhu PW, Su T, Zhou XZ, Li B, et al. Functional connectivity density alterations in middle-age retinal detachment patients. Brain Behav 2021; 11:e01783.
Huang X, Li D, Li HJ, Zhong YL, Freeberg S, Bao J, et al. Abnormal regional spontaneous neural activity in visual pathway in retinal detachment patients: a resting-state functional MRI study. Neuropsychiatr Dis Treat 2017; 13:2849–2854.
Ji Y, Wang YY, Cheng Q, Fu WW, Huang SQ, Zhong PP, et al. Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment. Front Neurosci 2023; 17:1227081.
Pandya DN. Anatomy of the auditory cortex. Rev Neurol (Paris) 1995; 151:486–494.
Bhaya-Grossman I, Chang EF. Speech Computations of the Human Superior Temporal Gyrus. Annu Rev Psychol 2022; 73:79–102.
Caspers J, Rubbert C, Eickhoff SB, Hoffstaedter F, Südmeyer M, Hartmann CJ, et al. Within- and across-network alterations of the sensorimotor network in Parkinson’s disease. Neuroradiology 2021; 63:2073–2085.
Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: is there potential for an imaging biomarker. Neuroimage Clin 2018; 18:849–870.
Yi SJ, Chen RB, Zhong YL, Huang X. The effect of long-term menstrual pain on large-scale brain network in primary dysmenorrhea patients. J Pain Res 2022; 15:2123–2131.
Zhong YL, Liu H, Huang X. Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma. Neuroscience 2024; 558:11–21.
Wang Y, Wang C, Wei Y, Miao P, Liu J, Wu L, et al. Abnormal functional connectivities patterns of multidomain cognitive impairments in pontine stroke patients. Hum Brain Mapp 2022; 43:4676–4688.
Xu L, Wei H, Sun Z, Chu T, Li M, Liu R, et al. Dynamic alterations of spontaneous neural activity in post-stroke aphasia: a resting-state functional magnetic resonance imaging study. Front Neurosci 2023; 17:1177930.
Li J, Zhang D, Liang A, Liang B, Wang Z, Cai Y, et al. High transition frequencies of dynamic functional connectivity states in the creative brain. Sci Rep 2017; 7:46072.
Hellyer PJ, Scott G, Shanahan M, Sharp DJ, Leech R. cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome. J Neurosci 2015; 35:9050–9063.
Qi CX, Wen Z, Huang X. Altered functional connectivity strength of primary visual cortex in subjects with thyroid-associated ophthalmopathy. Neuroreport 2024; 35:568–576.
Li Z, Huang C, Zhao X, Gao Y, Tian S. Abnormal postcentral gyrus voxel-mirrored homotopic connectivity as a biomarker of mild cognitive impairment: a resting-state fMRI and support vector machine analysis. Exp Gerontol 2024; 195:112547.
Shu Y, Huang Y, Chen J, Chen L, Cai G, Guo Y, et al. Effects of primary angle-closure glaucoma on interhemispheric functional connectivity. Front Neurosci 2023; 17:1053114.
Herrmann CS, Oertel U, Wang Y, Maess B, Friederici AD. Noise affects auditory and linguistic processing differently: an MEG study. Neuroreport 2000; 11:227–229.
Heinrich A, Szostek A, Meyer P, Reinhard I, Gilles M, Paslakis G, et al. Women are more strongly affected by dizziness in static magnetic fields of magnetic resonance imaging scanners. Neuroreport 2014; 25:1081–1084.
Keulers EH, Stiers P, Nicolson NA, Jolles J. The association between cortisol and the BOLD response in male adolescents undergoing fMRI. Brain Res 2015; 1598:1–11.
Mutschler I, Wieckhorst B, Meyer AH, Schweizer T, Klarhöfer M, Wilhelm FH, et al. Who gets afraid in the MRI-scanner? Neurogenetics of state-anxiety changes during an fMRI experiment. Neurosci Lett 2014; 583:81–86.
Wu YK, Su YA, Li L, Zhu LL, Li K, Li JT, et al. Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective. Psychol Med 2024; 54:763–774.
Yan CG, Chen X, Li L, Castellanos FX, Bai TJ, Bo QJ, et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 2019; 116:9078–9083.
Ma ZH, Lu B, Li X, Mei T, Guo YQ, Yang L, et al. Atypicalities in the developmental trajectory of cortico-striatal functional connectivity in autism spectrum disorder. Autism 2022; 26:1108–1122.

82160207 National Nature Science Foundation of China; No.20202ACBL216008 Key projects of Jiangxi Youth Science Fund; 202130156 Science and Technology Plan of Jiangxi Provincial Health and Health Commission; YC2022â€"s198 Postgraduate Innovation Special Fund Project in Jiangxi Province

Date Created: 20241018 Date Completed: 20241104 Latest Revision: 20241104

20241105

10.1097/WNR.0000000000002100

39423327