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European Radiology
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22-09-2023 | Glioma | Oncology

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients

Authors: Yukun Liu, Meng Cui, Xin Gao, Hui Yang, Hewen Chen, Bing Guan, Xiaodong Ma

Published in: European Radiology | Issue 4/2024

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Abstract

Objectives

A decline in language function is a common complication after glioma surgery, affecting patients’ quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.

Materials and methods

Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.

Results

Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.

Conclusion

We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.

Clinical relevance statement

Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.

Key Points

• Structural connectome combining diffusion tensor imaging features predicted glioma patients’ language decline after surgery.
• Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder.
• Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.
Appendix
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Literature
1.
Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507PubMed
2.
Jiang T, Nam DH, Ram Z et al (2021) Clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett 499:60–72PubMed
3.
Yordanova YN, Moritz-Gasser S, Duffau H (2011) Awake surgery for WHO grade II gliomas within “noneloquent” areas in the left dominant hemisphere: toward a “supratotal” resection. Clinical article. J Neurosurg 115:232–239PubMed
4.
Stummer W, Reulen HJ, Meinel T et al (2008) Extent of resection and survival in glioblastoma multiforme: identification of and adjustment for bias. Neurosurgery 62:564–576 discussion 564-576PubMed
5.
De Witt Hamer PC, Robles SG, Zwinderman AH, Duffau H, Berger MS (2012) Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis. J Clin Oncol 30:2559–2565PubMed
6.
Bosma I, Reijneveld JC, Douw L et al (2009) Health-related quality of life of long-term high-grade glioma survivors. Neuro Oncol 11:51–58PubMedPubMedCentral
7.
Gabel N, Altshuler DB, Brezzell A et al (2019) Health related quality of life in adult low and high-grade glioma patients using the national institutes of health Patient Reported Outcomes Measurement Information System (PROMIS) and Neuro-QOL assessments. Front Neurol 10:212PubMedPubMedCentral
8.
Abhinav K, Yeh FC, Pathak S et al (2014) Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: a review. Biochim Biophys Acta 1842:2286–2297PubMed
9.
Henderson F, Abdullah KG, Verma R, Brem S (2020) Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus 48:E6PubMedPubMedCentral
10.
Voets NL, Bartsch A, Plaha P (2017) Brain white matter fibre tracts: a review of functional neuro-oncological relevance. J Neurol Neurosurg Psychiatry 88:1017–1025PubMed
11.
Sollmann N, Fratini A, Zhang H, Zimmer C, Meyer B, Krieg SM (2019) Associations between clinical outcome and tractography based on navigated transcranial magnetic stimulation in patients with language-eloquent brain lesions. J Neurosurg 132:1033–1042PubMed
12.
Fang S, Liang Y, Li L et al (2021) Tumor location-based classification of surgery-related language impairments in patients with glioma. J Neurooncol 155:143–152PubMed
13.
Ille S, Engel L, Kelm A, Meyer B, Krieg SM (2018) Language-eloquent white matter pathway tractography and the course of language function in glioma patients. Front Oncol 8:572PubMedPubMedCentral
14.
Caverzasi E, Hervey-Jumper SL, Jordan KM et al (2016) Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas. J Neurosurg 125:33–45PubMed
15.
Acosta-Cabronero J, Williams GB, Pengas G, Nestor PJ (2010) Absolute diffusivities define the landscape of white matter degeneration in Alzheimer’s disease. Brain 133:529–539PubMed
16.
17.
Deverdun J, van Dokkum LEH, Le Bars E et al (2020) Language reorganization after resection of low-grade gliomas: an fMRI task based connectivity study. Brain Imaging Behav 14:1779–1791PubMed
18.
Kinoshita M, Nakada M, Okita H, Hamada JI, Hayashi Y (2014) Predictive value of fractional anisotropy of the arcuate fasciculus for the functional recovery of language after brain tumor resection: a preliminary study. Clin Neurol Neurosurg 117:45–50PubMed
19.
20.
Traut T, Sardesh N, Bulubas L et al (2019) MEG imaging of recurrent gliomas reveals functional plasticity of hemispheric language specialization. Hum Brain Mapp 40:1082–1092PubMed
21.
Desmurget M, Bonnetblanc F, Duffau H (2007) Contrasting acute and slow-growing lesions: a new door to brain plasticity. Brain 130:898–914PubMed
22.
Herbet G, Maheu M, Costi E, Lafargue G, Duffau H (2016) Mapping neuroplastic potential in brain-damaged patients. Brain 139:829–844PubMed
23.
Duffau H (2005) Lessons from brain mapping in surgery for low-grade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 4:476–486PubMed
24.
Jehna M, Becker J, Zaar K et al (2017) Symmetry of the arcuate fasciculus and its impact on language performance of patients with brain tumors in the language-dominant hemisphere. J Neurosurg 127:1407–1416PubMed
25.
Shafto MA, Tyler LK (2014) Language in the aging brain: the network dynamics of cognitive decline and preservation. Science 346:583–587ADSPubMed
26.
Duffau H (2014) The huge plastic potential of adult brain and the role of connectomics: new insights provided by serial mappings in glioma surgery. Cortex 58:325–337PubMed
27.
Wilmskoetter J, He X, Caciagli L et al (2022) Language recovery after brain injury: a structural network control theory study. J Neurosci 42:657–669PubMedPubMedCentral
28.
Ille S, Zhang H, Sogerer L et al (2022) Preoperative function-specific connectome analysis predicts surgery-related aphasia after glioma resection. Hum Brain Mapp 43:5408–5420PubMedPubMedCentral
29.
Shen X, Finn ES, Scheinost D et al (2017) Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc 12:506–518PubMedPubMedCentral
30.
Lamichhane B, Daniel AGS, Lee JJ, Marcus DS, Shimony JS, Leuthardt EC (2021) Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients. Front Neurol 12:642241PubMedPubMedCentral
31.
Yuan B, Zhang N, Yan J, Cheng J, Lu J, Wu J (2019) Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network. Neuroimage Clin 24:102023PubMedPubMedCentral
32.
Ren C, Zhang G, Xu X et al (2019) The effect of rTMS over the different targets on language recovery in stroke patients with global aphasia: a randomized sham-controlled study. Biomed Res Int 2019:4589056PubMedPubMedCentral
33.
34.
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl. Neuroimage 62:782–790PubMed
35.
Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386PubMedPubMedCentral
36.
Le TT, Fu W, Moore JH (2020) Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics 36:250–256PubMed
37.
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830MathSciNet
38.
Huang Z, Li G, Li Z et al (2021) Contralesional structural plasticity in different molecular pathologic subtypes of insular glioma. Front Neurol 12:636573PubMedPubMedCentral
39.
Jutten K, Mainz V, Delev D et al (2020) Asymmetric tumor-related alterations of network-specific intrinsic functional connectivity in glioma patients. Hum Brain Mapp 41:4549–4561PubMedPubMedCentral
40.
Jiao Y, Lin F, Wu J et al (2020) Plasticity in language cortex and white matter tracts after resection of dominant inferior parietal lobule arteriovenous malformations: a combined fMRI and DTI study. J Neurosurg 134:953–960PubMed
41.
Kourtidou E, Kasselimis D, Angelopoulou G et al (2021) The role of the right hemisphere white matter tracts in chronic aphasic patients after damage of the language tracts in the left hemisphere. Front Hum Neurosci 15:635750PubMedPubMedCentral
42.
Daniel AGS, Park KY, Roland JL et al (2021) Functional connectivity within glioblastoma impacts overall survival. Neuro Oncol 23:412–421PubMed
43.
Liu L, Zhang H, Rekik I, Chen X, Wang Q, Shen D (2016) Outcome prediction for patient with high-grade gliomas from brain functional and structural networks. Med Image Comput Comput Assist Interv 9901:26–34PubMedPubMedCentral
44.
Yoo C, Park S, Kim MJ (2022) Structural connectome-based prediction of trait anxiety. Brain Imaging Behav 16:2467–2476PubMed
45.
Horien C, Floris DL, Greene AS et al (2022) Functional connectome-based predictive modeling in autism. Biol Psychiatry 92:626–642PubMedPubMedCentral
46.
Zhu C, Fu Z, Chen L et al (2022) Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder. J Affect Disord 311:595–603PubMed
47.
Yu F, Fang H, Zhang J et al (2022) Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder. Depress Anxiety 39:858–869PubMed
48.
Yu J, Rawtaer I, Fam J, Feng L, Kua EH, Mahendran R (2020) The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features. Neuroimage 223:117310PubMed
49.
Wang X, Yoo K, Chen H et al (2022) Antagonistic network signature of motor function in Parkinson’s disease revealed by connectome-based predictive modeling. NPJ Parkinsons Dis 8:49PubMedPubMedCentral
50.
Wirth M, Jann K, Dierks T, Federspiel A, Wiest R, Horn H (2011) Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis. Neuroimage 54:3057–3066PubMed
51.
Binder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19:2767–2796PubMedPubMedCentral
52.
Partovi S, Jacobi B, Rapps N et al (2012) Clinical standardized fMRI reveals altered language lateralization in patients with brain tumor. AJNR Am J Neuroradiol 33:2151–2157PubMedPubMedCentral
53.
Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41
54.
Fridriksson J, Yourganov G, Bonilha L, Basilakos A, Den Ouden DB, Rorden C (2016) Revealing the dual streams of speech processing. Proc Natl Acad Sci U S A 113:15108–15113ADSPubMedPubMedCentral
55.
56.
(2016) Paths, Diffusion, and Navigation. In: Fornito A, Zalesky A, Bullmore ET, (eds) Fundamentals of brain network analysis. Academic Press, San Diego, pp. 207-255
57.
Thiel A, Herholz K, Koyuncu A et al (2001) Plasticity of language networks in patients with brain tumors: a positron emission tomography activation study. Ann Neurol 50:620–629PubMed
58.
Duffau H, Capelle L, Denvil D et al (2003) Functional recovery after surgical resection of low grade gliomas in eloquent brain: hypothesis of brain compensation. J Neurol Neurosurg Psychiatry 74:901–907PubMedPubMedCentral
59.
Ille S, Engel L, Albers L et al (2019) Functional reorganization of cortical language function in glioma patients-a preliminary study. Front Oncol 9:446PubMedPubMedCentral
60.
Nieberlein L, Rampp S, Gussew A, Prell J, Hartwigsen G (2023) Reorganization and plasticity of the language network in patients with cerebral gliomas. Neuroimage Clin 37:103326PubMedPubMedCentral
61.
Nenning KH, Fosleitner O, Schwartz E et al (2021) The impact of hippocampal impairment on task-positive and task-negative language networks in temporal lobe epilepsy. Clin Neurophysiol 132:404–411PubMed
62.
Schevenels K, Michiels L, Lemmens R, De Smedt B, Zink I, Vandermosten M (2022) The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia. Neuroimage Clin 36:103243PubMedPubMedCentral
63.
Luders H, Lesser RP, Dinner DS, Morris HH, Wyllie E, Godoy J (1988) Localization of cortical function: new information from extraoperative monitoring of patients with epilepsy. Epilepsia 29(Suppl 2):S56–S65PubMed
64.
Canini M, Della Rosa PA, Catricala E et al (2016) Semantic interference and its control: a functional neuroimaging and connectivity study. Hum Brain Mapp 37:4179–4196PubMedPubMedCentral
65.
Li L, Emmorey K, Feng X, Lu C, Ding G (2016) Functional connectivity reveals which language the “control regions” control during bilingual production. Front Hum Neurosci 10:616PubMedPubMedCentral
66.
Papagno C, Gallucci M, Casarotti A et al (2011) Connectivity constraints on cortical reorganization of neural circuits involved in object naming. Neuroimage 55:1306–1313PubMed
67.
Papagno C, Casarotti A, Comi A et al (2016) Long-term proper name anomia after removal of the uncinate fasciculus. Brain Struct Funct 221:687–694PubMed
68.
69.
Caeyenberghs K, Swinnen SP (2015) Neural correlates of motor deficits in young patients with traumatic brain injury. In: Toga AW (ed) Brain mapping. Academic Press, Waltham, pp 461–468
70.
(2016) Node Degree and Strength. In: Fornito A, Zalesky A, Bullmore ET, (eds) Fundamentals of brain network analysis. Academic Press, San Diego, pp. 115-136
71.
Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23:1231–1251PubMedPubMedCentral
Metadata
Title
Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients
Authors
Yukun Liu
Meng Cui
Xin Gao
Hui Yang
Hewen Chen
Bing Guan
Xiaodong Ma
Publication date
22-09-2023
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 4/2024
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-023-10212-2

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