Imaging of Brain Tumor Connectivity

Abstract

Background

Brain tumors, especially glioblastomas, remain among the tumor diseases with the worst prognosis. Recent findings in brain tumor research show that neuronal and glial integration of tumors, as well as the formation of glioma cell networks, promote tumor progression and therapy resistance. This highlights the need for innovative imaging techniques that conceptualize brain tumors as systemic central nervous system (CNS) diseases that are deeply integrated in the brain’s network architecture.

Materials and Methods

This review presents current imaging methods for analyzing tumor-associated functional and structural connectivity with a focus on resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).

Results

Functional connectivity changes in glioma patients can be detected and quantified using fMRI. These changes are associated with tumor biology, as well as prognosis and cognitive performance. Rs-fMRI parameters may support prognostic assessment and the development of new therapeutic strategies. Quantitative structural connectivity analysis at the individual patient level can provide further insight into tumor integration in the brain’s connectional architecture. DTI-based tractography is especially relevant in neurosurgical planning, as it maps the spatial relationship between the tumor and white matter tracts.

Conclusion

Imaging analysis of tumor-associated network alterations provides deeper insight into brain tumor biology and may support the development of network-targeted therapeutic approaches. Connectivity-based imaging methods, particularly rs-fMRI and DTI, hold great potential to further enhance preoperative planning, prognostic assessment, and personalized treatment strategies for patients with brain tumors.

Key Points

• Glioma cells form networks beyond macroscopic tumor boundaries and promote therapy resistance.

• Glioma cells form synapses with neurons and exploit neural signals for growth.

• Network alterations can be visualized and quantified using rs-fMRI and DTI.

• Tumor-associated network alterations in imaging correlate with tumor biology and prognosis.

• Imaging markers optimize patient management and support development of new therapeutic strategies.

Citation Format

• Suvak S, Wunderlich S, Stoecklein V et al. Imaging of Brain Tumor Connectivity. Rofo 2026; DOI 10.1055/a-2779-7718

Keywords

brain tumor - fMRI - DTI - tractography - functional connectivity - structural connectivity

Publication History

Received: 08 September 2025

Accepted after revision: 19 December 2025

Article published online:
06 February 2026

• Literatur

• 1 Weller M, van den Bent M, Tonn JC. et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol 2017; 18: e315-e329
• 2 Delgado-López PD, Corrales-García EM. Survival in glioblastoma: a review on the impact of treatment modalities. Clin Transl Oncol 2016; 18: 1062-1071
• 3 Salvalaggio A, Pini L, Bertoldo A. et al. Glioblastoma and brain connectivity: the need for a paradigm shift. Lancet Neurol 2024; 23: 740-748
• 4 Sahm F, Capper D, Jeibmann A. et al. Addressing diffuse glioma as a systemic brain disease with single-cell analysis. Arch Neurol 2012; 69: 523-526
• 5 Winkler F, Venkatesh HS, Amit M. et al. Cancer neuroscience: State of the field, emerging directions. Cell 2023; 186: 1689-1707
• 6 Osswald M, Jung E, Sahm F. et al. Brain tumour cells interconnect to a functional and resistant network. Nature 2015; 528: 93-98
• 7 Venkataramani V, Yang Y, Schubert MC. et al. Glioblastoma hijacks neuronal mechanisms for brain invasion. Cell 2022; 185: 2899-2917.e31
• 8 Venkataramani V, Tanev DI, Strahle C. et al. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature 2019; 573: 532-538
• 9 Venkatesh HS, Morishita W, Geraghty AC. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 2019; 573: 539-545
• 10 Jung E, Osswald M, Ratliff M. et al. Tumor cell plasticity, heterogeneity, and resistance in crucial microenvironmental niches in glioma. Nat Commun 2021; 12: 1014
• 11 Stoecklein VM, Stoecklein S, Galiè F. et al. Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients. Neuro Oncol 2020; 22: 1388-1398
• 12 Hua B, Ding X, Xiong M. et al. Alterations of functional and structural connectivity in patients with brain metastases. PLoS One 2020; 15: e0233833
• 13 Sprugnoli G, Rigolo L, Faria M. et al. Tumor BOLD connectivity profile correlates with glioma patients’ survival. Neurooncol Adv 2022; 4: vdac153
• 14 Daniel AGS, Park KY, Roland JL. et al. Functional connectivity within glioblastoma impacts overall survival. Neuro Oncol 2021; 23: 412-421
• 15 Daniel AGS, Hacker CD, Lee JJ. et al. Homotopic functional connectivity disruptions in glioma patients are associated with tumor malignancy and overall survival. Neurooncol Adv 2021; 3: vdab176
• 16 Luckett PH, Olufawo M, Lamichhane B. et al. Predicting survival in glioblastoma with multimodal neuroimaging and machine learning. J Neurooncol 2023; 164: 309-320
• 17 Angstwurm P, Hense K, Rosengarth K. et al. Attenuation of the BOLD fMRI signal and changes in functional connectivity affecting the whole brain in presence of brain metastasis. Cancers (Basel) 2024; 16
• 18 Salvalaggio A, Pini L, Gaiola M. et al. White matter tract density index prediction model of overall survival in glioblastoma. JAMA Neurol 2023; 80: 1222-1231
• 19 Wei Y, Li C, Cui Z. et al. Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients. Brain 2023;
• 20 de Godoy LL, Chen YJ, Chawla S. et al. Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI. Br J Radiol 2022; 95: 20220516
• 21 Urut DU, Karabulut D, Hereklioglu S. et al. Diffusion tensor imaging: survival analysis prediction in breast cancer patients. Radiologie (Heidelb) 2024; 64: 54-59
• 22 Smitha KA, Akhil Raja K, Arun KM. et al. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 2017; 30: 305-317
• 23 Ogawa S, Menon RS, Kim SG. et al. On the characteristics of functional magnetic resonance imaging of the brain. Annu Rev Biophys Biomol Struct 1998; 27: 447-474
• 24 Kim SG, Uğurbil K. Comparison of blood oxygenation and cerebral blood flow effects in fMRI: estimation of relative oxygen consumption change. Magn Reson Med 1997; 38: 59-65
• 25 Logothetis NK, Pauls J, Augath M. et al. Neurophysiological investigation of the basis of the fMRI signal. Nature 2001; 412: 150-157
• 26 Poustchi-Amin M, Mirowitz SA, Brown JJ. et al. Principles and applications of echo-planar imaging: a review for the general radiologist. Radiographics 2001; 21: 767-779
• 27 Stehling MK, Turner R, Mansfield P. Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science 1991; 254: 43-50
• 28 Kundu P, Voon V, Balchandani P. et al. Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals. Neuroimage 2017; 154: 59-80
• 29 Biswal B, Yetkin FZ, Haughton VM. et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995; 34: 537-541
• 30 Beckmann CF, DeLuca M, Devlin JT. et al. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 2005; 360: 1001-1013
• 31 Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009; 10: 186-198
• 32 Lamichhane B, Daniel AGS, Lee JJ. et al. Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients. Front Neurol 2021; 12: 642241
• 33 Krishna S, Choudhury A, Keough MB. et al. Glioblastoma remodelling of human neural circuits decreases survival. Nature 2023; 617: 599-607
• 34 Nenning K, Furtner J, Kiesel B. et al. Distributed changes of the functional connectome in patients with glioblastoma. Sci Rep 2020; 10
• 35 Mandal AS, Romero-García R, Hart M. et al. Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma. Brain 2020; 143: 3294-3307
• 36 Stoecklein VM, Wunderlich S, Papazov B. et al. Perifocal edema in patients with meningioma is associated with impaired whole-brain connectivity as detected by resting-state fMRI. AJNR Am J Neuroradiol 2023; 44: 814-819
• 37 Stoecklein S, Wunderlich S, Papazov B. et al. Functional connectivity MRI provides an imaging correlate for chimeric antigen receptor T-cell-associated neurotoxicity. Neurooncol Adv 2023; 5: vdad135
• 38 Toloknieiev A, Voitsekhivskyi D, Kholodkov H. et al. Quantification of brain functional connectivity deviations in individuals: A scoping review of functional MRI studies. medRxiv 2024;
• 39 Bookheimer SY, Salat DH, Terpstra M. et al. The lifespan Human Connectome Project in aging: An overview. Neuroimage 2019; 185: 335-348
• 40 Van Dijk KRA, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 2012; 59: 431-438
• 41 Wunderlich S, Alici C, Motevalli S. et al. Denoising strategies of functional connectivity MRI data in lesional and non-lesional brain diseases. Imaging Neurosci (Camb) 2025;
• 42 Jellison BJ, Field A, Medow J. et al. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR American journal of neuroradiology 2004; 25: 356-369
• 43 Mori S, van Zijl PCM. Fiber tracking: principles and strategies – a technical review. NMR Biomed 2002; 15: 468-480
• 44 Le Bihan D, Mangin JF, Poupon C. et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001; 13: 534-546
• 45 Gerrish AC, Thomas AG, Dineen RA. Brain white matter tracts: functional anatomy and clinical relevance. Semin Ultrasound CT MR 2014; 35: 432-444
• 46 Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994; 103: 247-254
• 47 Henderson F, Abdullah KG, Verma R. et al. Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential. Neurosurg Focus 2020; 48: E6
• 48 Mukherjee P, Berman JI, Chung SW. et al. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am J Neuroradiol 2008; 29: 632-641
• 49 Ramos-Llordén G, Lee H-H, Davids M. et al. Ultra-high gradient connectomics and microstructure MRI scanner for imaging of human brain circuits across scales. Nat Biomed Eng 2025; 1-16
• 50 Yeh F-C, Irimia A, Bastos DC de A. et al. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 2021; 245: 118651
• 51 Grisot G, Haber SN, Yendiki A. Diffusion MRI and anatomic tracing in the same brain reveal common failure modes of tractography. Neuroimage 2021; 239: 118300
• 52 Panigrahy A, Borzage M, Blüml S. Basic principles and concepts underlying recent advances in magnetic resonance imaging of the developing brain. Semin Perinatol 2010; 34: 3-19
• 53 Behrens TEJ, Berg HJ, Jbabdi S. et al. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?. Neuroimage 2007; 34: 144-155
• 54 Seow P, Hernowo AT, Narayanan V. et al. Neural fiber integrity in high- versus low-grade glioma using probabilistic fiber tracking. Acad Radiol 2021; 28: 1721-1732
• 55 Hu B, Ye B, Yang Y. et al. Quantitative diffusion tensor deterministic and probabilistic fiber tractography in relapsing-remitting multiple sclerosis. Eur J Radiol 2011; 79: 101-107
• 56 Li Z, Peck KK, Brennan NP. et al. Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models. J Biomed Sci Eng 2013; 6: 192-200
• 57 Zolal A, Sobottka SB, Podlesek D. et al. Comparison of probabilistic and deterministic fiber tracking of cranial nerves. J Neurosurg 2017; 127: 613-621
• 58 Kamagata K, Andica C, Uchida W. et al. Advancements in diffusion MRI tractography for neurosurgery. Invest Radiol 2024; 59: 13-25
• 59 Aylmore H, Young F, Aquilina K. et al. The use of intraoperative tractography in brain tumor and epilepsy surgery: a systematic review and meta-analysis. Front Neuroimaging 2025; 4
• 60 Cozzi FM, Mayrand RC, Wan Y. et al. Predicting glioblastoma progression using MR diffusion tensor imaging: A systematic review. J Neuroimaging 2025; 35: e13251
• 61 Chamberland M, Genc S, Tax CMW. et al. Detecting microstructural deviations in individuals with deep diffusion MRI tractometry. Nat Comput Sci 2021; 1: 598-606
• 62 Sprugnoli G, Rossi S, Rotenberg A. et al. Personalised, image-guided, noninvasive brain stimulation in gliomas: Rationale, challenges and opportunities. EBioMedicine 2021; 70: 103514