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

Introduction

Brain tumors, especially gliomas, are among the tumor diseases with the most unfavorable prognosis, despite intensive multimodal treatment approaches [1]. For glioblastoma, the most common high-grade glioma, the 5-year survival rate remains unchanged at less than 5% [2]. In recent years, important insights gained in the field of cancer neuroscience have significantly broadened our understanding of tumor biology and opened new avenues for innovative therapeutic strategies. Translation of this knowledge also presents new challenges for diagnostic imaging [3].

As early as 2012, gliomas were described as a systemic disease of the central nervous system (CNS), because tumor cells could be detected histopathologically in macroscopically unremarkable brain tissue. This suggests that the actual extent of the tumor significantly exceeds the lesion visible in the MRI morphology [4].

Since 2015, several groundbreaking studies in the field of cancer neuroscience have contributed significantly to better understanding the network structure of gliomas. Preclinical models show that glioma cells integrate both functionally and structurally in cerebral architecture and utilize physiological mechanisms for their growth [5] [6] [7] [8]. What stands out particularly is evidence of synaptic connections between neurons and glioma cells, whereby the neuronal input specifically promotes tumor growth [6] [8]. Another key mechanism is the formation of tumor cell networks via what are known as tumor microtubes (TM), in which glioma cells connect via gap junctions [6]. It was possible to demonstrate communication via calcium waves in TMs, which points to active and coordinated network behavior by tumor cells [9]. In addition, studies show that interconnected tumor cells within this glioma-associated network exhibit increased resistance to therapeutic measures and can regenerate themselves. This network formation thus contributes significantly to therapy resistance and an unfavorable prognosis [10]. The insight that gliomas can be understood as a systemic network disease of the brain requires new imaging approaches capable of reproducing and analyzing the network qualities of these tumors ([Fig. 1]). Functional MRI (fMRI) and diffusion tensor imaging (DTI) are promising imaging methods. Initial fMRI and DTI studies have shown that gliomas and cerebral metastases lead to changes in functional and structural connectivity [3] [11] [12], which are partly associated with survival [3] [13].

Fig. 1 Schematic illustration of imaging-based analysis of tumor-associated connectivity in brain tumors. The MRI-based methods rely on the capacity of gliomas to integrate functionally and structurally in cerebral networks, supporting the conceptualization of gliomas as systemic network disorders of the brain. On the left, functional connectivity assessed via resting-state fMRI is shown, capturing tumor-associated alterations in cerebral network activity. On the right, structural connectivity analyzed through diffusion-weighted MRI and tractography illustrates the spatial relationship and integrity of subcortical fiber tracts in relation to the tumor. Created with BioRender.com.

Extensive connectivity changes, measured by functional connectivity MRI (fcMRI) in a resting state, were linked to patients with tumor-grade gliomas [11] [13] [14] [15] and the prognosis [13] [14] [15] [16]. fcMRI studies of brain metastases are rare and limited by small sample sizes. However, at group level they suggest widespread changes in functional connectivity [12] [17].

Changes in structural fiber tracts, studied using diffusion tensor imaging (DTI), are also associated with survival in patients with glioblastoma [18] [19]. Structural changes can also be used to assess prognosis in metastases and are associated with overall survival [20] [21].

Functional connectivity

Functional MRI (fMRI)

fMRI is based on the blood-oxygen-level-dependent (BOLD) signal. Deoxygenated hemoglobin has paramagnetic properties and leads locally to signal attenuation in the BOLD contrast, while oxygenated hemoglobin is diamagnetic and barely affects the MRI signal. The higher the proportion of deoxygenated hemoglobin, the lower the BOLD signal in a voxel [22] [23] [24]. Seconds after the activation of a brain area, for example, through a task, its blood flow increases in a super-compensatory manner [25]. Neural activation thus leads to an increase in the level of oxyhemoglobin, which prolongs the T2* time of the blood and leads to an increased intensity of the T2*-weighted signal [26]. BOLD signals can be captured using echo-planar imaging (EPI) sequences that specifically emphasize this T2* effect [26] and enable fast time-resolved data acquisition [27]. The newer multi-echo fMRI can improve the accuracy and informative value of functional MRI compared to single-echo fMRI by acquiring multiple echo images per slice through precise modeling of the T2* signal waveform [28]. During fMRI, the BOLD signals for each voxel of the brain are recorded over a period of minutes [23].

Functional connectivity via resting-state fMRI

In resting state in the absence of targeted activation of brain areas by specific tasks, the brain exhibits intrinsic fluctuations of the BOLD signal, which characterizes the brain’s resting activity and accounts for 60–80% of cerebral energy consumption [22]. Brain areas that are functionally connected show a correlated signal time course; the stronger the correlation, the higher the functional connectivity of the brain areas.

For resting-state fMRI (rs-fMRI), patients are examined in an MRI scanner for about 6–15 minutes without performing cognitive tasks and without falling asleep. The analysis of the rs-fMRI data is predominantly based on fluctuations of the BOLD signal in the low frequency range (<0.1 Hz) [22] [29].

There are various methods for analyzing functional connectivity in the brain based on rs-fMRI, such as region-of-interest (ROI)-based analyses, where the connectivity of a specific ROI to other brain areas or to the remaining voxels of gray matter is studied [22]. An alternative method is independent component analysis (ICA), a data-driven approach that captures simultaneous voxel-to-voxel interactions and allows researchers to identify multiple spatially-independent but functionally-coherent neural networks in the brain. In this process, signal sources that are statistically independent of each other are separated, making ICA particularly suitable for analyzing complex functional network patterns in resting conditions (rs-fMRI) [30]. Another approach calculates the correlation of the BOLD signal time courses between all the voxels of gray matter and forms an individual correlation matrix from this data. This correlation matrix can be interpreted as a functional connectome and serves as a basis for further analyses, such as graph-theoretical methods (e.g. modularity, efficiency, node strength) [31] or normative models in which individual connectivity profiles are compared with reference data sets (see below).

Connectivity changes in brain tumors

Since 2020, several studies have shown that gliomas are associated with measurable changes in functional connectivity and that these tumor-associated connectivity changes are significantly associated with prognosis in terms of overall survival in both low-grade and high-grade gliomas [3] [13] [15] [16] [32]. Furthermore, a correlation was found between functional connectivity changes and the WHO grade, molecular genetic markers such as IDH mutation status, and cognitive performance [11]. A higher WHO grade was associated with greater deviations in connectivity from a reference distribution obtained from healthy subjects. In addition, patients with IDH wild-type gliomas show greater connectivity abnormalities than patients with IDH-mutated tumors [11]. The extent of connectivity deviation was associated with neurocognitive performance, as measured by the Montreal Cognitive Assessment (MoCA) test. It has also been shown that in brain tumor patients, changes in functional connectivity are not limited to the lesional hemisphere, but also affect the contralesional hemisphere, particularly in higher grade gliomas ([Fig. 2]) [11]. Preliminary results show that different parts of the same tumor also exhibit different connectivity to the remaining brain ([Fig. 3]), which corresponds to the heterogeneity of the glioma and reflects results of a seminal MEG-based glioma study [33].

Fig. 2 fcMRI dysconnectivity map. A T1-weighted MRI with contrast enhancement and B T2-weighted MRI of a patient with glioblastoma. C, D show representative axial and coronal views of the rs-fMRI dysconnectivity map (= dysconnectivity index (DCI) map). Despite morphological tumor localization confined to the left hemisphere, extensive tumor-associated connectivity alterations extending into the contralateral hemisphere are observed. (A, B). Adapted from [11].

Fig. 3 Intratumoral differences in connectivity. Panel (A) shows a tumor region with high connectivity to brain areas outside the tumor, such as in the frontal and occipital lobes (B). In contrast, the tumor region shown in (C) exhibits low connectivity to brain parenchyma outside the tumor (D). The image was created using Elements software (Brainlab AG, Germany).

Methodologically, different analytical approaches are used to study functional connectivity in glioma patients, including measuring the strength of connectivity within defined networks [15], the assessment of functional connectivity between tumor and residual brain tissue [13], correlation-based methods based on voxel-wise matrices of gray matter [11] [32] [34], and graph-theoretical methods for investigating the higher-level network topology [35]. Different aspects of tumor-associated network changes were examined in each case.

Analysis of functional connectivity based on rs-fMRI thus offers promising insights into the network-wide effects of brain tumors such as gliomas. It reveals changes that go far beyond the locally-limited findings that can be detected with conventional MRI or PET. This systemic view of the brain is clinically relevant, particularly, in the case of diffusely infiltrating tumors such as glioblastoma. However, for clinical applications, there is currently no established procedure for quantitatively assessing the individual deviations. While many studies are conducted at the group level, reliable analysis of individual patients is essential for personalized diagnostics and therapy. A first step in this direction is the introduction of the dysconnectivity index (DCI), a voxel-based marker that compares a patient’s connectivity profile with the normative values of a healthy cohort ([Fig. 4]) [11]. The DCI enables researchers to detect and visualize network-based changes at the level of individual patients ([Fig. 2]). Other papers, such as the study by Nenning et al. [34], confirm the relevance of quantitative rs-fMRI metrics in gliomas and demonstrate the potential for applications in extra-axial brain tumors [36], as well as to help to identify the neurotoxic side effects of oncological therapies [37].

Fig. 4 Concept and visualization of the dysconnectivity index (DCI). The DCI provides a quantitative measure of tumor-associated functional connectivity alterations at the individual patient level. This approach compares an individual patient’s functional connectivity profile against normative data derived from a healthy reference cohort (e.g. n=1000). Brain regions significantly deviating from the normative connectivity pattern can subsequently be visualized using voxel-based disconnectivity maps. These maps allow color-coded identification of regions exhibiting increased or decreased functional connectivity, facilitating detailed assessment of tumor-related network disruptions.

A recent overview by Toloknieiev et al. [38] shows, however, that so far only a few methods systematically use normative references, and demographic and technical factors are often disregarded. The combination of large fMRI databases (e.g. HCP) [39]) with AI-based methods opens up new perspectives in this regard: for example, standards-based fcMRI models could form the basis for clinically applicable markers for monitoring disease progression, assessing prognosis, and planning therapies in the future. Particularly when examining patients who are sometimes seriously ill, it should be noted that motion artifacts can limit the interpretability [40], and special noise reduction strategies should therefore be applied, as necessary [41].

Structural connectivity

Not only does functional connectivity play an important role in cerebral tumors, it has also been shown that structural connectivity is altered in patients with glioblastoma and that these alterations correlate with overall survival [18].

Fiber tracts in white matter provide the anatomical basis for connectivity

The brain’s structural connectivity is based on nerve fiber tracts in the white matter, which can be divided into projection fibers, association fibers, and commissural fibers [42]. Projection fibers connect cortical areas with subcortical structures (e.g. corticospinal tract); association fibers connect cortical areas within one hemisphere, and commissural fibers (e.g. corpus callosum) connect both hemispheres. Tractography based on diffusion tensor imaging (DTI) can be used to visualize these fiber connections.

Tractography

Fiber tracking or tractography has been around for more than two decades and it uses DTI sequences [43]. The imaging is based on the diffusion of water molecules. A diffusion tensor is calculated that describes the three-dimensional probability distribution of the diffusion direction of the water molecule [44] [45] [46]. This means, in practical terms, that voxels within fiber tracts (e.g. the corticospinal tract) tend to have a directed diffusion vector along the fiber tract direction [45]. In order to determine the diffusion tensor, the DTI sequence must have at least six non-orthogonal directions. For optimal data quality, between 32 and 64 directions are used [45].

A commonly used measure for the directionality of diffusion is fractional anisotropy (FA). This value lies between 0 and 1. Values close to 0 indicate little directional diffusion, while values close to 1 indicate a stronger direction of diffusion [47]. FA values can also be represented using image morphology in what are known as FA maps ([Fig. 5]). The three orthogonal axes are color-coded: anterior-posterior in green, medial-lateral in red, and ventral-dorsal in blue [48]. Tractography (fiber tracking) can be performed to create a three-dimensional representation of the brain’s fiber pathways. In tractography, there is a distinction made between deterministic and probabilistic methods [47].

Fig. 5 Visualization of structural connectivity using fractional anisotropy (FA) and DTI-based tractography in a patient with glioma. The left panel displays an axial FA map (a) alongside the corresponding T2-weighted MRI (b) from a patient with a right-sided fronto-temporal glioma with infiltration of insula. The FA map demonstrates asymmetry of white matter integrity with reduced FA values in the region of the right hemispheric lesion. The T2-weighted MRI highlights the tumor mass with significant surrounding edema and partial compression of the right lateral ventricle. The right panel c–f shows selected clinically relevant white matter tracts visualized bilaterally using DTI tractography, with the tumor depicted in red. Illustrated are the corticospinal tract (orange, e), arcuate fasciculus (violet, d), and inferior fronto-occipital fasciculus (blue, f). Tractography enables detailed spatial analysis of the relationship between white matter tracts and the glioma, including identification of tumor-associated displacement and compromised structural integrity, exemplified in (f). The visualization was generated using Elements software (Brainlab AG, Germany).

Technical aspects of diffusion-weighted imaging

In diffusion-weighted imaging, technical aspects such as gradient strength and slew rate play an important role. Standard clinical scanners typically use lower gradient strengths (40–60 mT/m), while state-of-the-art, high-performance systems such as the Connectome 2.0 offer a more powerful solution with a gradient strength of 500 mT/m and a slew rate of 600 T/ms [49]. These systems’ higher gradient strengths result in improved SNR, greater sensitivity in terms of tractography, and even more precise visualization of fiber pathways [49]. These performance improvements can be particularly relevant for mapping tumor-associated connectivity, as they may enable more robust modeling, more precise fiber orientations, and thus clinically useful tractography in cases of edema/infiltration.

Deterministic tractography

In deterministic tractography, the fiber tract is calculated using the vector of the diffusion tensor. The fiber tract is reconstructed until certain termination criteria along the vectors are met. Traditionally, the FA can be used for this. When the fiber tract reaches the cortex from the white matter, the FA drops to 0.1–0.2, because the distribution of diffusion directions in the voxel becomes more heterogeneous at this point [43]. As a result, tractography can be terminated with a threshold of 0.2, and the reconstruction process stops as soon as the threshold is reached [43]. Another option for terminating the process relies on the turning angle in the transition from voxel to voxel during reconstruction. If the diffusion vector in the following voxel deviates by more than a certain angle, the reconstruction of the fiber tract is terminated at that point [43].

Deterministic tractography has known limitations, especially in complex anatomical situations. In regions with high fiber density, crossings, fanning, or abrupt changes in direction, fiber reconstruction may be terminated prematurely [43]. This is because the method reconstructs only along the principal diffusion vector (principal eigenvector of the diffusion tensor) for each voxel. If the fiber direction changes significantly from one voxel to the next, this often leads to false-negative results or a complete termination of tract tracking [50] [51]. Additionally, the accuracy of the reconstruction can be further impaired by motion artifacts and susceptibility artifacts (e.g. distortions) [52].

Probabilistic tractography

Another method of tractography is probabilistic tractography. In contrast to deterministic tractography, it is based on probabilities of the diffusion tensor and is therefore less dependent on the FA and the turning angle [48] [53] [54]. Probabilistic fiber tracking calculates several different possibilities for the direction of the diffusion tensor. Based on observed fiber pathways, the probability of a connection to neighboring voxels passing through a user-defined starting point is determined for each voxel. Possible diffusion-based directions are repeatedly calculated at the voxel level, thus reconstructing the course of the fiber pathways [55] [56] [57]. Especially at intersections of fiber tracts and in small tracts with a curved course, the probabilistic method is superior to the deterministic method in reconstructing tracts [56] [57] ([Fig. 6]).

Fig. 6 Comparison of deterministic and probabilistic tractography in a patient with glioma. Exemplary visualization of the corticospinal tract (orange), arcuate fasciculus (violet), and inferior fronto-occipital fasciculus (blue) using probabilistic (a) versus deterministic (b) tractography. The tumor is depicted in red. Direct comparison reveals that probabilistic tractography achieves greater reconstruction accuracy and more comprehensive visualization, especially in fiber tracts with complex trajectories or pronounced curvature (e.g. arcuate fasciculus). Additionally, probabilistic tractography demonstrates superior capability in capturing fiber dispersion in regions exhibiting significant fanning (e.g. occipital segment of the inferior fronto-occipital fasciculus). The visualization was generated using Elements software (Brainlab AG, Germany).

Tractography in brain tumors

Tractography supports the reconstruction of fiber tracts that are spatially related to the tumor ([Fig. 5], [Fig. 6]). Both the tumor itself and the surrounding tumor edema can affect the fiber tracts. These fiber tracts can be displaced by tumor growth [42]. Furthermore, tumor edema can alter the fiber structure, which makes tractography more difficult, as the edema reduces, among other things, the FA [42]. In addition, the fibrous tract can be infiltrated by the tumor and ultimately destroyed [42]. Tractography can provide important information about the location and integrity of eloquent fiber tracts. For this reason, it is therefore valuable for preoperative planning and evaluating the resectability of brain tumors [58]. Tractography can also help to better understand tumor-associated changes in connectivity and possible pathways of spread and infiltration. Although it has already been shown that integrating tractography preoperatively can improve outcomes [59], it is not yet widely used in clinical routine [59]. The amount of time and technical effort involved are cited, specifically, as limitations [59], and this has led to tractographies being carried out primarily in the context of studies.

Structural connectivity in brain tumors

Structural connectivity plays a central role in understanding tumor spread and prognosis in gliomas, particularly glioblastomas. Studies show that glioblastomas spread along white matter fiber tracts, which significantly influences the course of the disease [3]. Wei et al. (2023), using a large-scale study with diffusion-weighted imaging, were able to demonstrate that structural impairments of the connectome extend far beyond the visible tumor and also affect the contralateral hemisphere [19]. These disruptions in structural connectivity correlated significantly with patients’ overall survival and cognitive performance [19].

Salvalaggio et al. (2023) developed the tract density index (TDI), which measures the density of white matter fibers in the tumor region [18]. Tumors in regions with high fiber bundle density show a poorer prognosis, highlighting the importance of the local structural environment for tumor spread. The number of fibers infiltrated by a glioblastoma is also closely associated with survival, suggesting a direct link between tumor extent and anatomical connectivity [18]. In addition, microstructural changes in the white matter can indicate early tumor progression or recurrence, a finding that could potentially improve treatment planning and monitoring [60].

In summary, the analysis of structural connectivity using modern imaging and indices, such as the tract density index, provides important insights into tumor behavior and represents a promising approach for improving the clinical treatment of glioblastomas. As in the area of functional connectivity, the ability to classify individual results as normal or pathological will prove essential for the potential clinical application of structural connectivity. This can be achieved using the large reference data sets that are becoming increasingly available (e.g. the Human Connectome Project (HCP)); https://www.humanconnectome.org/). Tumor-associated changes in the microstructural connectome could thus be precisely detected and analyzed in comparison with reference groups [61].

Tractography and analysis of structural connectivity using DTI also makes it possible to investigate the subcortical correlate for tumor-associated changes in functional connectivity ([Fig. 2]). Regions with strong functional connectivity to the tumor can serve as target points (ROI) for fiber tracking originating from the tumor, so that a possible structural network of fiber tracts can be identified and visualized as a correlate for the functional changes ([Fig. 7]).

Fig. 7 Integration of structural and functional tumor connectivity by combined fMRI and tractography. The figure illustrates an example of combined functional and structural tumor-associated connectivity in a patient with a left temporo-parietal glioma. A depicts the localization of the tumor (red). B shows brain areas functionally associated with the tumor (light blue), identified through correlated BOLD signal fluctuations in resting-state fMRI. C visualizes the structural fiber connections (turquoise) reconstructed by DTI-based probabilistic tractography, linking the tumor with the functionally associated regions. This integrated visualization clearly demonstrates the close relationship between functional and structural connectivity in gliomas and highlights the systemic nature of tumor-associated connectivity. Visualization was created with Elements software (Brainlab AG, Germany).

Summary and outlook

Brain tumors, especially gliomas, can be understood as systemic CNS diseases that are closely embedded in the brain’s functional and structural network architecture ( [Fig. 1]). The formation of a tumor cell network and the interaction of glioma cells with neuronal networks promote tumor growth and significantly complicate the treatment of these diseases [5] [6] [7] [8]. Against this background, improved functional and structural imaging techniques, as well as modern methods of analysis, are urgently needed in order to be able to grasp the network aspect of this disease both in terms of imaging and quantitatively. Since functional and structural connectivity strongly correlate with overall survival, fMRI and DTI are becoming increasingly important in this context [3]. In the future, MRI-based quantitative parameters of functional and structural connectivity could enable precise prognostic assessment, differentiated risk stratification, and early detection of tumor progression and recurrence. Lastly, these parameters could support the development of innovative therapeutic concepts that involve targeted modulation of tumor-brain interaction, e.g. using neuromodulatory stimulation techniques [62] and novel drug therapies [5] aimed at effectively inhibiting progression and relapse.

Conflict of Interest

The authors declare that they have no conflict of interest.

• 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

Publication History

Received: 08 September 2025

Accepted after revision: 19 December 2025

Article published online:
06 February 2026