- Email: [email protected]
Inter-network functional connectivity changes in patients with brain tumors: a resting-state functional magnetic resonance imaging study
Journal Pre-proof Inter-network functional connectivity changes in patients with brain tumors: a restingstate functional magnetic resonance imaging study Dr. Hussam Metwali, MD MSc., Dr. Mathijs Raemaekers, PhD, Tamer Ibrahim, Associate Professor, Dr. Amir Samii, MD PhD, Professor PII:
S1878-8750(20)30195-9
DOI:
https://doi.org/10.1016/j.wneu.2020.01.177
Reference:
WNEU 14207
To appear in:
World Neurosurgery
Received Date: 22 December 2019 Revised Date:
22 January 2020
Accepted Date: 23 January 2020
Please cite this article as: Metwali H, Raemaekers M, Ibrahim T, Samii A, Inter-network functional connectivity changes in patients with brain tumors: a resting-state functional magnetic resonance imaging study, World Neurosurgery (2020), doi: https://doi.org/10.1016/j.wneu.2020.01.177. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
1
Inter-network functional connectivity changes in patients with brain tumors: a resting-
2
state functional magnetic resonance imaging study
3 4
Authors
5
Dr. Hussam Metwali, MD MSc.1,2,
6
Dr. Mathijs Raemaekers, PhD 3
7
Associate Professor Tamer Ibrahim 4
8
Professor Dr. Amir Samii, MD PhD 1,2
9
1)International Neuroscience Institute, Department of Neurosurgery
10
Rudolf-Pichlmayr-Straße 4
11
D - 30625 Hannover
12
Germany
13
2) Leibniz Institute for neurobiology, Magdeburg, Germany
14
3) Brain Center Rudolf Magnus, University Medical Center Utrecht
15
4) Department of neurosurgery, University of Alexandria, Egypt
16
Rudolf-Pichlmayr-Straße 4
17
D - 30625 Hannover
18
Germany
19 20
Corresponding author
21
Dr. Hussam Metwali, MD MSc.
22
Rudolf-Pichlmayr-Straße 4
1
1
D - 30625 Hannover
2
Germany
3
Telephone: +49 (0)511 27092-837
4
Fax: +49 (0)511 27092-453
5
Email: [email protected]
6
According to the ethical committee of Hannover medical school, voting on the study is
7
not required due to its retrospective design.
8
Conflict of Interest: None. Disclosure of Funding: None.
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2
1
Abstract
2
Background:
3
Objective: Measuring functional connectivity (FC) and resting state networks (RSNs) using
4
resting state functional MRI is a method of preoperative planning in patients with brain
5
tumors. However, the baseline FC and RSNs are altered in patients with brain tumors. In this
6
study, we examined changes in inter-network FC in patients with brain tumors.
7
Methods: We performed region of interest (ROI)-ROI analysis of FC in 34 patients with
8
supratentorial gliomas and 14 healthy subjects. We performed bivariate correlation analyses
9
at the level of each subject. Resulting correlations were Fischer Z-transformed. The used
10
nodes included 132 ROIs from the Automated Anatomical Labeling (AAL) atlas in addition
11
to 32 ROIs representing the different functional brain networks. We investigated second-level
12
effects by contrasting dummy encoded co-variates representing the effects of group
13
membership on functional connectivity. The significant two-sided P-value with corrected
14
false discovery rate was set to 0.05. We set the t contrast between the group of patients with
15
brain tumors and the group of healthy subjects to detect the effects of tumors on inter-
16
network connectivity.
17
Results: Overall, the inter-network FC was significantly higher in patients with brain tumors
18
compared to healthy subjects. The anterior and posterior cerebellar networks, as well as the
19
supratentorial network, showed significantly higher connectivity in patients with brain tumors
20
than in healthy subjects.
21
Conclusion: Although brain tumors affect the FC and RSNs, the current study showed higher
22
baseline inter-network connectivity in patients with brain tumors, which could indicate an
23
intrinsic neural compensatory mechanism.
24 25 26
Running title: Inter-network functional connectivity changes in patients with brain tumors
27
Key words: Brain tumor, Functional connectivity, resting state networks.
3
1
Introduction
2
Detection of blood oxygen level-dependent (BOLD) signal changes with functional magnetic
3
resonance imaging (fMRI) has become a powerful tool for in vivo analysis of a wide
4
spectrum of brain functions and resting-state networks (RSNs). These RSNs show relatively
5
consistent and identifiable patterns of activation at rest. Moreover, RSNs show a high level of
6
reproducibility. Accordingly, RSNs could be a robust method for examination of the intrinsic
7
functional architecture, or “connectome,” of the human brain[1-5].
8
Resting state (rs)-fMRI can be used for preoperative planning in neuro-oncology and epilepsy
9
surgeries[6-9]. Diverse eloquent RSNs, such as motor, visual, or language networks, can be
10
identified and spatially represented on co-registered high-resolution structural images (T1 or
11
T2). In addition, rs-fMRI can potentially identify the high-order network[10-15].
12
Understanding RSNs in patients with brain tumors is of paramount importance for presurgical
13
brain mapping. A previous study from our research group identified different networks in
14
patients with brain tumors that displayed variable degrees of disconnections[6]. In the present
15
study, we analyzed changes in inter-network connections in patients with brain tumors in
16
comparison to a group of healthy subjects to gain a better understanding of differential
17
dynamics in functional connectivity in patients with brain tumors. These findings support
18
further investigation into the mechanisms of compensatory cerebral plasticity. Additionally,
19
our data suggest the development of new strategies for preoperative brain mapping and
20
identification of the eloquent regions, particularly in patients with brain tumors.
21
Methods
22
We performed a retrospective analysis of the resting-state fMRI data of 34 patients that had
23
preoperative rs-fMRI before undergoing surgery for supratentorial glioma between March
24
2017 and March 2019. Patient characteristics are summarized in table 1. The control group
25
comprised 14 healthy volunteers. According to the local ethical committee, voting was not
26
required due to the retrospective nature of the study. The data was anonymously analyzed.
27
All patients performed a preoperative rs-fMRI and high resolution T1 images beside the
28
routine diagnostic and preoperative imaging.
29
Data acquisition
4
1
All patients and healthy control subjects underwent MRI examination using a 3-Tesla
2
Scanner (Skyra Siemens AG Medical Solution, Munich, Germany). The MRI acquisition
3
included volumetric high-resolution anatomical images (T1 or T2), as well as rs-fMRI echo-
4
planar imaging (EPI) sequences (repetition time (TR): 2000 msec, echo time (TE): 30 msec,
5
voxel size: 3×3×3mm, slice thickness: 3 mm, number of slices: 36, number of volumes: 178).
6
Preprocessing
7
The preprocessing was performed using the Statistical Parametric Mapping (SPM 12,
8
http://www.fil.ion.ucl.ac.uk.) CONN[16] toolbox that run on MATLAB 2016a (The
9
MathWorks, Inc., Natick, MA, USA). MRIcron was used for inspection of the images and
10
overlay of the resulting components on the structural images[17].
11
We manually reoriented the structural and functional images into Talairach orientation.
12
Manual reorientation reduces the chance of being trapped into a local minimum. After
13
reorientation, we used the CheckReg (SPM) function to check the matching of the functional
14
and structural images in the subject space.
15
We realigned functional images to correct motion artifacts, then applied slice time correction.
16
The functional images were co-registered to the structural images. The structural images were
17
normalized to the Montreal Neurological Institute (MNI) space and were then segmented into
18
grey matter, white matter, and cerebrospinal fluid. The functional images were also
19
normalized using the parameters established for the structural image into MNI space. The
20
normalized functional images were smoothed with a 6-mm Gaussian Full Width Half
21
Maximum (FWHM) kernel that was twice the voxel size. A Band-Pass filter [0.008 - 0.09
22
Hz] to remove noise and linear detrending were applied.
23
We performed bivariate correlation analyses at the level of each subject; the resulting
24
correlations were Fischer Z-transformed. Used nodes included 132 ROIs from the AAL atlas
25
in addition to 32 ROIs representing the different functional brain networks. Each networks is
26
is a group of ROIs.We investigated second-level effects (group analysis) by contrasting
27
dummy-encoded co-variates representing the effects of group membership on functional
28
connectivity. The significant two-sided P-value with corrected false discovery rate was set to
29
0.05. We set the t contrast between the patient group and healthy control group to detect the
30
effects of the tumor on inter-network connectivity.
31
Results 5
1
This study included 34 patients suffering from supratentorial gliomas and 14 healthy subjects.
2
Rs-fMRI and high resolution T1 images were performed in each participant. We performed a
3
group analysis and the findings were averaged across the subjects.
4
In patients with brain tumors compared to healthy subjects, the anterior (Figure 1 A, B) and
5
posterior cerebellar networks (Figure 1 C, D) had significantly higher connectivity with the
6
supratentorial networks, including sensorimotor (right side in Figure 2 A and left side in
7
Figure 2 B) (Anterior cerebellar network: P=0.000497, posterior cerebellar network:
8
P=0.000115), visual (P=0.001311, 0.000903), default mode (P=0.053984, 0.000144), dorsal
9
attention (P=0.01, 0.007665) and salience (P=0.031809, 0.000096) networks. We also
10
identified the following changes in inter-network connectivity in the supratentorial networks:
11
the default mode network (DMN) had significantly higher connectivity to the frontoparietal
12
network in patients with brain tumors (DMN posterior cingulate gyrus P=0.003885, DMN
13
frontal P=0.014842, DMN lateral parietal P=0.005238). The dorsal attention network had
14
significantly higher connectivity with the sensorimotor network (SensoriMotorSuperior
15
P=0.019197, SensoriMotorLateral P=0.049582) and the frontoparietal network (P=0.010245).
16
The salience network displayed higher connectivity with the visual networks (P=0.004424) in
17
patients with brain tumors. The frontoparietal ( Figure 3 A, B) and salience (Figure 3 C, D)
18
networks had significantly higher connectivity with different supra and infratentorial
19
networks in patients with brain tumors than in healthy control subjects
20
Discussion
21
Our current study, uncovered changes in inter-network connectivity in 32 patients with brain
22
tumors as compared to 14 healthy control subjects. We found that patients with brain tumors
23
showed an overall increase in inter-network connectivity, as well as a specific increase in
24
connectivity between cerebellar and supratentorial networks. A previous study from our
25
research group showed a decrease in connectivity between the posterior cingulate gyrus and
26
the parietal components of the DMN and the medial frontal components[18]. A similar
27
decrease in the connectivity of the components of the dorsal attention network and the
28
salience networks has been observed. A wide variation in the spatial characteristics of each
29
network has been observed between subjects, even in subjects with similar tumor
30
locations[6]. This could be explained by decreased long-range connectivity[19] or subtle
31
changes in cerebral function that require further investigation.
6
1
In neurosurgical practice, functional connectivity and resting-state network imaging is now
2
frequently used for preoperative brain mapping. Shimony et al. identified different networks
3
in patients with brain tumors using rs-fMRI and then used the identified networks in
4
preoperative planning[8]. Zhang et al. showed that the sensorimotor cortex could be reliably
5
detected using rs-fMRI in tumor patients, and its location corresponded with cortical
6
stimulation mapping[9]. Associated language cortices can also be identified using rs-fMRI in
7
patients with brain tumors[6, 20]. FC has also been evaluated as a prognostic tool in patients
8
with brain tumors where the strenght in the connectivity of certain networks like motor
9
network could be linked to the outcome of the motor functions.[20, 21]. The use of rs-fMRI
10
for presurgical planning in the pediatric age group was reported by Roland et al.[12].
11
Functional connectivity can also be detected in patients under anesthesia for use in
12
intraoperative brain mapping[22].
13
Although FC is useful in preoperative brain mapping, brain tumors can affect functional
14
connectivity via several different mechanisms[18, 22, 23]. Alterations in DMN connectivity
15
have been identified in patients with gliomas compared to healthy controls using FC[24] and
16
pseudo-resting-state fMRI[25]. Furthermore, long-distance functional connectivity was
17
decreased, and the network topology showed changes in patients with supratentorial
18
gliomas[19]. Frontal network connectivity was increased in adult survivors of childhood
19
cerebellar tumors[26]. Mechanisms that contribute to alterations in FC and DMN in patients
20
with
21
characteristics[28, 29] that modulate the BOLD signal within the tumor region [30]. There is
22
also neurovascular uncoupling (NVU) due to disruption of the coupling between neurons and
23
astrocytes that regulates cerebral blood flow[31]. NVU can confound the interpretation of
24
resting-state connectivity in brain tumor patients[32]. Therefore, there is an urgent need for
25
new tools to elucidate how the BOLD fMRI signal is modulated in patients with brain
26
tumors.
27
The effect of brain tumors on inter-network connectivity has been vastly understudied. In the
28
current study we performed a group analysis and demonstrated enhanced connectivity
29
between the cerebellar and suprasellar networks and enhanced connectivity between different
30
supratentorial networks. This may suggest the involvement of compensatory mechanisms for
31
cerebral function, or functional compensatory mechanisms such as neural plasticity. Our
32
findings support the idea that cerebral function exists along a dynamic spectrum. This altered
33
connectivity in brain tumor patients may influence preoperative planning since certain
brain
tumors
include abnormal
vascular architecture[27]
and
blood
flow
7
1
pathways may take on different functions or become more critically involved in new
2
functions. Further studies are required to analyze potential changes in structural connectivity.
3
Single subject analysis and analysis of affection of a certain network affected by a tumor will
4
require a more homogenous group of patients having certain tumor grade in certain location.
5
We are hoping to present this study in near future.
6
Conclusion
7
Inter-network connectivity was significantly higher in patients with brain tumors compared to
8
a group of healthy subjects. Additionally, cerebellar networks had significantly higher
9
connectivity with supratentorial networks. Increased inter-network connectivity could
10
represent an intrinsic cerebral compensatory mechanism.
11
8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
References 1. 2. 3. 4. 5. 6. 7. 8. 9.
10. 11. 12. 13.
14. 15.
16. 17. 18. 19. 20. 21.
22.
Allen, E.A., et al., A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci, 2011. 5: p. 2. Beckmann, C.F., et al., Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 2005. 360(1457): p. 1001-13. Biswal, B., et al., Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 1995. 34(4): p. 537-41. Biswal, B.B., et al., Toward discovery science of human brain function. Proc Natl Acad Sci U S A, 2010. 107(10): p. 4734-9. FitzGerald, D.B., et al., Location of language in the cortex: a comparison between functional MR imaging and electrocortical stimulation. AJNR Am J Neuroradiol, 1997. 18(8): p. 1529-39. Metwali, H. and A. Samii, Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a practical approach. World Neurosurg, 2019. Otten, M.L., et al., Motor deficits correlate with resting state motor network connectivity in patients with brain tumours. Brain, 2012. 135(Pt 4): p. 1017-26. Shimony, J.S., et al., Resting-state spontaneous fluctuations in brain activity: a new paradigm for presurgical planning using fMRI. Acad Radiol, 2009. 16(5): p. 578-83. Zhang, D., et al., Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with functional magnetic resonance imaging: initial experience. Neurosurgery, 2009. 65(6 Suppl): p. 226-36. Kokkonen, S.M., et al., Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI. Magn Reson Imaging, 2009. 27(6): p. 733-40. Liu, H., et al., Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity. J Neurosurg, 2009. 111(4): p. 746-54. Hart, M.G., S.J. Price, and J. Suckling, Functional connectivity networks for preoperative brain mapping in neurosurgery. J Neurosurg, 2017. 126(6): p. 1941-1950. Sair, H.I., et al., Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI. Hum Brain Mapp, 2016. 37(3): p. 913-23. Rosazza, C., et al., Preoperative mapping of the sensorimotor cortex: comparative assessment of task-based and resting-state FMRI. PLoS One, 2014. 9(6): p. e98860. Metwali, H., et al., Intraoperative Resting-State Functional Connectivity and Resting-State Networks in Patients with Intracerebral Lesions: Detectability and Variations Between Sessions. World Neurosurg, 2019. Whitfield-Gabrieli, S. and A. Nieto-Castanon, Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2012. 2(3): p. 125-41. Rorden, C. and M. Brett, Stereotaxic display of brain lesions. Behav Neurol, 2000. 12(4): p. 191-200. Metwali, H. and A. Samii, Seed-Based Connectivity Analysis of Resting-State fMRI in Patients with Brain Tumors: A Feasibility Study. World Neurosurg, 2019. Park, J.E., et al., Alteration of long-distance functional connectivity and network topology in patients with supratentorial gliomas. Neuroradiology, 2016. 58(3): p. 311-20. Tie, Y., et al., Defining language networks from resting-state fMRI for surgical planning--a feasibility study. Hum Brain Mapp, 2014. 35(3): p. 1018-30. Roder, C., et al., Resting-state functional MRI in an intraoperative MRI setting: proof of feasibility and correlation to clinical outcome of patients. J Neurosurg, 2016. 125(2): p. 4019. Metwali, H., et al., Resting-State Functional Connectivity in Neurosurgical Patients Under Propofol Anesthesia: Detectability and Variability Between Patients and Between Sessions. World Neurosurg, 2019. 9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
23. 24. 25. 26. 27. 28.
29. 30. 31. 32.
Metwali, H., et al., The reliability of fMRI in brain tumor patients: a critical review and metaanalysis. World Neurosurg, 2019. Esposito, R., et al., Modifications of default-mode network connectivity in patients with cerebral glioma. PLoS One, 2012. 7(7): p. e40231. Harris, R.J., et al., Altered functional connectivity of the default mode network in diffuse gliomas measured with pseudo-resting state fMRI. J Neurooncol, 2014. 116(2): p. 373-9. Chen, H., et al., Increased frontal functional networks in adult survivors of childhood brain tumors. Neuroimage Clin, 2016. 11: p. 339-346. Kim, E., et al., Vascular phenotyping of brain tumors using magnetic resonance microscopy (muMRI). J Cereb Blood Flow Metab, 2011. 31(7): p. 1623-36. Deane, B.R. and P.L. Lantos, The vasculature of experimental brain tumours. Part 1. A sequential light and electron microscope study of angiogenesis. J Neurol Sci, 1981. 49(1): p. 55-66. Jain, R.K., et al., Angiogenesis in brain tumours. Nat Rev Neurosci, 2007. 8(8): p. 610-22. Chow, D.S., et al., Glioblastoma Induces Vascular Dysregulation in Nonenhancing Peritumoral Regions in Humans. AJR Am J Roentgenol, 2016. 206(5): p. 1073-81. Watkins, S., et al., Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nat Commun, 2014. 5: p. 4196. Agarwal, S., et al., Neurovascular uncoupling in resting state fMRI demonstrated in patients with primary brain gliomas. J Magn Reson Imaging, 2016. 43(3): p. 620-6.
21 22
10
1
Figure legends
2
Figure 1: Significantly higher connectivity of the anterior (A, B) and posterior (C, D)
3
cerebellar networks with the supratentorial networks was seen in patients with brain tumors
4
than in healthy control subjects.
5
Figure 2: Significantly higher connectivity of the right (A) and left (B) sensorimotor cortices
6
with the cerebellum was seen in patients with brain tumors than in healthy control subjects.
7
Figure 3: The frontoparietal (A, B) and salience (C, D) networks had significantly higher
8
connectivities with different supra and infratentorial networks in patients with brain tumors
9
than in healthy control subjects.
10 11
Table 1: Patient characteristics.
11
Age Sex (M:F) Tumor locaton
Glioma grade
Recurrent tumor Previous radiation
18-77 years (mean 54 years) 18:16 Frontal: 14 patients Temporal: 10 Parietal: 7 patients Occipital: 3 patients Grade I: 2 patients Grade II: 7 patients Grade III: 9 patients Grade VI: 16 patients 3 patients 3 patients Table 1: Characteristics of the patients
Disclosure I, Hussam Metwali, certify that this manuscript is a unique submission and is not being considered for publication, in part or in full, with any other source in any medium. All authors have seen and approved the final version of this manuscript before submission. None of the authors have any undisclosed conflicts of interest related to the submission of this manuscript. We have no financial interest in material or devices described in this article and have received no financial support in conjunction with the generation of this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Best Regards Dr. Hussam Metwali, MD MSc. Neurosurgeon INI-Hannover, Germany 03.11.2019
Abbreviations fMRI: functional magnetic resonance imaging, BOLD: blood oxygen level dependent, RSN: resting state networks
Metwali H, Raemaekers M: Conceptualization, Methodology, Software, Statistical analysis. Metwali H, Tamer I: Writing- Original draft preparation. Samii A: Supervision. Metwali H, Raemaekers M, Samii A: Writing- Reviewing and Editing.
S1878-8750(20)30195-9
DOI:
https://doi.org/10.1016/j.wneu.2020.01.177
Reference:
WNEU 14207
To appear in:
World Neurosurgery
Received Date: 22 December 2019 Revised Date:
22 January 2020
Accepted Date: 23 January 2020
Please cite this article as: Metwali H, Raemaekers M, Ibrahim T, Samii A, Inter-network functional connectivity changes in patients with brain tumors: a resting-state functional magnetic resonance imaging study, World Neurosurgery (2020), doi: https://doi.org/10.1016/j.wneu.2020.01.177. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier Inc. All rights reserved.
1
Inter-network functional connectivity changes in patients with brain tumors: a resting-
2
state functional magnetic resonance imaging study
3 4
Authors
5
Dr. Hussam Metwali, MD MSc.1,2,
6
Dr. Mathijs Raemaekers, PhD 3
7
Associate Professor Tamer Ibrahim 4
8
Professor Dr. Amir Samii, MD PhD 1,2
9
1)International Neuroscience Institute, Department of Neurosurgery
10
Rudolf-Pichlmayr-Straße 4
11
D - 30625 Hannover
12
Germany
13
2) Leibniz Institute for neurobiology, Magdeburg, Germany
14
3) Brain Center Rudolf Magnus, University Medical Center Utrecht
15
4) Department of neurosurgery, University of Alexandria, Egypt
16
Rudolf-Pichlmayr-Straße 4
17
D - 30625 Hannover
18
Germany
19 20
Corresponding author
21
Dr. Hussam Metwali, MD MSc.
22
Rudolf-Pichlmayr-Straße 4
1
1
D - 30625 Hannover
2
Germany
3
Telephone: +49 (0)511 27092-837
4
Fax: +49 (0)511 27092-453
5
Email: [email protected]
6
According to the ethical committee of Hannover medical school, voting on the study is
7
not required due to its retrospective design.
8
Conflict of Interest: None. Disclosure of Funding: None.
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2
1
Abstract
2
Background:
3
Objective: Measuring functional connectivity (FC) and resting state networks (RSNs) using
4
resting state functional MRI is a method of preoperative planning in patients with brain
5
tumors. However, the baseline FC and RSNs are altered in patients with brain tumors. In this
6
study, we examined changes in inter-network FC in patients with brain tumors.
7
Methods: We performed region of interest (ROI)-ROI analysis of FC in 34 patients with
8
supratentorial gliomas and 14 healthy subjects. We performed bivariate correlation analyses
9
at the level of each subject. Resulting correlations were Fischer Z-transformed. The used
10
nodes included 132 ROIs from the Automated Anatomical Labeling (AAL) atlas in addition
11
to 32 ROIs representing the different functional brain networks. We investigated second-level
12
effects by contrasting dummy encoded co-variates representing the effects of group
13
membership on functional connectivity. The significant two-sided P-value with corrected
14
false discovery rate was set to 0.05. We set the t contrast between the group of patients with
15
brain tumors and the group of healthy subjects to detect the effects of tumors on inter-
16
network connectivity.
17
Results: Overall, the inter-network FC was significantly higher in patients with brain tumors
18
compared to healthy subjects. The anterior and posterior cerebellar networks, as well as the
19
supratentorial network, showed significantly higher connectivity in patients with brain tumors
20
than in healthy subjects.
21
Conclusion: Although brain tumors affect the FC and RSNs, the current study showed higher
22
baseline inter-network connectivity in patients with brain tumors, which could indicate an
23
intrinsic neural compensatory mechanism.
24 25 26
Running title: Inter-network functional connectivity changes in patients with brain tumors
27
Key words: Brain tumor, Functional connectivity, resting state networks.
3
1
Introduction
2
Detection of blood oxygen level-dependent (BOLD) signal changes with functional magnetic
3
resonance imaging (fMRI) has become a powerful tool for in vivo analysis of a wide
4
spectrum of brain functions and resting-state networks (RSNs). These RSNs show relatively
5
consistent and identifiable patterns of activation at rest. Moreover, RSNs show a high level of
6
reproducibility. Accordingly, RSNs could be a robust method for examination of the intrinsic
7
functional architecture, or “connectome,” of the human brain[1-5].
8
Resting state (rs)-fMRI can be used for preoperative planning in neuro-oncology and epilepsy
9
surgeries[6-9]. Diverse eloquent RSNs, such as motor, visual, or language networks, can be
10
identified and spatially represented on co-registered high-resolution structural images (T1 or
11
T2). In addition, rs-fMRI can potentially identify the high-order network[10-15].
12
Understanding RSNs in patients with brain tumors is of paramount importance for presurgical
13
brain mapping. A previous study from our research group identified different networks in
14
patients with brain tumors that displayed variable degrees of disconnections[6]. In the present
15
study, we analyzed changes in inter-network connections in patients with brain tumors in
16
comparison to a group of healthy subjects to gain a better understanding of differential
17
dynamics in functional connectivity in patients with brain tumors. These findings support
18
further investigation into the mechanisms of compensatory cerebral plasticity. Additionally,
19
our data suggest the development of new strategies for preoperative brain mapping and
20
identification of the eloquent regions, particularly in patients with brain tumors.
21
Methods
22
We performed a retrospective analysis of the resting-state fMRI data of 34 patients that had
23
preoperative rs-fMRI before undergoing surgery for supratentorial glioma between March
24
2017 and March 2019. Patient characteristics are summarized in table 1. The control group
25
comprised 14 healthy volunteers. According to the local ethical committee, voting was not
26
required due to the retrospective nature of the study. The data was anonymously analyzed.
27
All patients performed a preoperative rs-fMRI and high resolution T1 images beside the
28
routine diagnostic and preoperative imaging.
29
Data acquisition
4
1
All patients and healthy control subjects underwent MRI examination using a 3-Tesla
2
Scanner (Skyra Siemens AG Medical Solution, Munich, Germany). The MRI acquisition
3
included volumetric high-resolution anatomical images (T1 or T2), as well as rs-fMRI echo-
4
planar imaging (EPI) sequences (repetition time (TR): 2000 msec, echo time (TE): 30 msec,
5
voxel size: 3×3×3mm, slice thickness: 3 mm, number of slices: 36, number of volumes: 178).
6
Preprocessing
7
The preprocessing was performed using the Statistical Parametric Mapping (SPM 12,
8
http://www.fil.ion.ucl.ac.uk.) CONN[16] toolbox that run on MATLAB 2016a (The
9
MathWorks, Inc., Natick, MA, USA). MRIcron was used for inspection of the images and
10
overlay of the resulting components on the structural images[17].
11
We manually reoriented the structural and functional images into Talairach orientation.
12
Manual reorientation reduces the chance of being trapped into a local minimum. After
13
reorientation, we used the CheckReg (SPM) function to check the matching of the functional
14
and structural images in the subject space.
15
We realigned functional images to correct motion artifacts, then applied slice time correction.
16
The functional images were co-registered to the structural images. The structural images were
17
normalized to the Montreal Neurological Institute (MNI) space and were then segmented into
18
grey matter, white matter, and cerebrospinal fluid. The functional images were also
19
normalized using the parameters established for the structural image into MNI space. The
20
normalized functional images were smoothed with a 6-mm Gaussian Full Width Half
21
Maximum (FWHM) kernel that was twice the voxel size. A Band-Pass filter [0.008 - 0.09
22
Hz] to remove noise and linear detrending were applied.
23
We performed bivariate correlation analyses at the level of each subject; the resulting
24
correlations were Fischer Z-transformed. Used nodes included 132 ROIs from the AAL atlas
25
in addition to 32 ROIs representing the different functional brain networks. Each networks is
26
is a group of ROIs.We investigated second-level effects (group analysis) by contrasting
27
dummy-encoded co-variates representing the effects of group membership on functional
28
connectivity. The significant two-sided P-value with corrected false discovery rate was set to
29
0.05. We set the t contrast between the patient group and healthy control group to detect the
30
effects of the tumor on inter-network connectivity.
31
Results 5
1
This study included 34 patients suffering from supratentorial gliomas and 14 healthy subjects.
2
Rs-fMRI and high resolution T1 images were performed in each participant. We performed a
3
group analysis and the findings were averaged across the subjects.
4
In patients with brain tumors compared to healthy subjects, the anterior (Figure 1 A, B) and
5
posterior cerebellar networks (Figure 1 C, D) had significantly higher connectivity with the
6
supratentorial networks, including sensorimotor (right side in Figure 2 A and left side in
7
Figure 2 B) (Anterior cerebellar network: P=0.000497, posterior cerebellar network:
8
P=0.000115), visual (P=0.001311, 0.000903), default mode (P=0.053984, 0.000144), dorsal
9
attention (P=0.01, 0.007665) and salience (P=0.031809, 0.000096) networks. We also
10
identified the following changes in inter-network connectivity in the supratentorial networks:
11
the default mode network (DMN) had significantly higher connectivity to the frontoparietal
12
network in patients with brain tumors (DMN posterior cingulate gyrus P=0.003885, DMN
13
frontal P=0.014842, DMN lateral parietal P=0.005238). The dorsal attention network had
14
significantly higher connectivity with the sensorimotor network (SensoriMotorSuperior
15
P=0.019197, SensoriMotorLateral P=0.049582) and the frontoparietal network (P=0.010245).
16
The salience network displayed higher connectivity with the visual networks (P=0.004424) in
17
patients with brain tumors. The frontoparietal ( Figure 3 A, B) and salience (Figure 3 C, D)
18
networks had significantly higher connectivity with different supra and infratentorial
19
networks in patients with brain tumors than in healthy control subjects
20
Discussion
21
Our current study, uncovered changes in inter-network connectivity in 32 patients with brain
22
tumors as compared to 14 healthy control subjects. We found that patients with brain tumors
23
showed an overall increase in inter-network connectivity, as well as a specific increase in
24
connectivity between cerebellar and supratentorial networks. A previous study from our
25
research group showed a decrease in connectivity between the posterior cingulate gyrus and
26
the parietal components of the DMN and the medial frontal components[18]. A similar
27
decrease in the connectivity of the components of the dorsal attention network and the
28
salience networks has been observed. A wide variation in the spatial characteristics of each
29
network has been observed between subjects, even in subjects with similar tumor
30
locations[6]. This could be explained by decreased long-range connectivity[19] or subtle
31
changes in cerebral function that require further investigation.
6
1
In neurosurgical practice, functional connectivity and resting-state network imaging is now
2
frequently used for preoperative brain mapping. Shimony et al. identified different networks
3
in patients with brain tumors using rs-fMRI and then used the identified networks in
4
preoperative planning[8]. Zhang et al. showed that the sensorimotor cortex could be reliably
5
detected using rs-fMRI in tumor patients, and its location corresponded with cortical
6
stimulation mapping[9]. Associated language cortices can also be identified using rs-fMRI in
7
patients with brain tumors[6, 20]. FC has also been evaluated as a prognostic tool in patients
8
with brain tumors where the strenght in the connectivity of certain networks like motor
9
network could be linked to the outcome of the motor functions.[20, 21]. The use of rs-fMRI
10
for presurgical planning in the pediatric age group was reported by Roland et al.[12].
11
Functional connectivity can also be detected in patients under anesthesia for use in
12
intraoperative brain mapping[22].
13
Although FC is useful in preoperative brain mapping, brain tumors can affect functional
14
connectivity via several different mechanisms[18, 22, 23]. Alterations in DMN connectivity
15
have been identified in patients with gliomas compared to healthy controls using FC[24] and
16
pseudo-resting-state fMRI[25]. Furthermore, long-distance functional connectivity was
17
decreased, and the network topology showed changes in patients with supratentorial
18
gliomas[19]. Frontal network connectivity was increased in adult survivors of childhood
19
cerebellar tumors[26]. Mechanisms that contribute to alterations in FC and DMN in patients
20
with
21
characteristics[28, 29] that modulate the BOLD signal within the tumor region [30]. There is
22
also neurovascular uncoupling (NVU) due to disruption of the coupling between neurons and
23
astrocytes that regulates cerebral blood flow[31]. NVU can confound the interpretation of
24
resting-state connectivity in brain tumor patients[32]. Therefore, there is an urgent need for
25
new tools to elucidate how the BOLD fMRI signal is modulated in patients with brain
26
tumors.
27
The effect of brain tumors on inter-network connectivity has been vastly understudied. In the
28
current study we performed a group analysis and demonstrated enhanced connectivity
29
between the cerebellar and suprasellar networks and enhanced connectivity between different
30
supratentorial networks. This may suggest the involvement of compensatory mechanisms for
31
cerebral function, or functional compensatory mechanisms such as neural plasticity. Our
32
findings support the idea that cerebral function exists along a dynamic spectrum. This altered
33
connectivity in brain tumor patients may influence preoperative planning since certain
brain
tumors
include abnormal
vascular architecture[27]
and
blood
flow
7
1
pathways may take on different functions or become more critically involved in new
2
functions. Further studies are required to analyze potential changes in structural connectivity.
3
Single subject analysis and analysis of affection of a certain network affected by a tumor will
4
require a more homogenous group of patients having certain tumor grade in certain location.
5
We are hoping to present this study in near future.
6
Conclusion
7
Inter-network connectivity was significantly higher in patients with brain tumors compared to
8
a group of healthy subjects. Additionally, cerebellar networks had significantly higher
9
connectivity with supratentorial networks. Increased inter-network connectivity could
10
represent an intrinsic cerebral compensatory mechanism.
11
8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
References 1. 2. 3. 4. 5. 6. 7. 8. 9.
10. 11. 12. 13.
14. 15.
16. 17. 18. 19. 20. 21.
22.
Allen, E.A., et al., A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci, 2011. 5: p. 2. Beckmann, C.F., et al., Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 2005. 360(1457): p. 1001-13. Biswal, B., et al., Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med, 1995. 34(4): p. 537-41. Biswal, B.B., et al., Toward discovery science of human brain function. Proc Natl Acad Sci U S A, 2010. 107(10): p. 4734-9. FitzGerald, D.B., et al., Location of language in the cortex: a comparison between functional MR imaging and electrocortical stimulation. AJNR Am J Neuroradiol, 1997. 18(8): p. 1529-39. Metwali, H. and A. Samii, Seed-based connectivity analysis of resting-state fMRI in patients with brain tumors: a practical approach. World Neurosurg, 2019. Otten, M.L., et al., Motor deficits correlate with resting state motor network connectivity in patients with brain tumours. Brain, 2012. 135(Pt 4): p. 1017-26. Shimony, J.S., et al., Resting-state spontaneous fluctuations in brain activity: a new paradigm for presurgical planning using fMRI. Acad Radiol, 2009. 16(5): p. 578-83. Zhang, D., et al., Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with functional magnetic resonance imaging: initial experience. Neurosurgery, 2009. 65(6 Suppl): p. 226-36. Kokkonen, S.M., et al., Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI. Magn Reson Imaging, 2009. 27(6): p. 733-40. Liu, H., et al., Task-free presurgical mapping using functional magnetic resonance imaging intrinsic activity. J Neurosurg, 2009. 111(4): p. 746-54. Hart, M.G., S.J. Price, and J. Suckling, Functional connectivity networks for preoperative brain mapping in neurosurgery. J Neurosurg, 2017. 126(6): p. 1941-1950. Sair, H.I., et al., Presurgical brain mapping of the language network in patients with brain tumors using resting-state fMRI: Comparison with task fMRI. Hum Brain Mapp, 2016. 37(3): p. 913-23. Rosazza, C., et al., Preoperative mapping of the sensorimotor cortex: comparative assessment of task-based and resting-state FMRI. PLoS One, 2014. 9(6): p. e98860. Metwali, H., et al., Intraoperative Resting-State Functional Connectivity and Resting-State Networks in Patients with Intracerebral Lesions: Detectability and Variations Between Sessions. World Neurosurg, 2019. Whitfield-Gabrieli, S. and A. Nieto-Castanon, Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect, 2012. 2(3): p. 125-41. Rorden, C. and M. Brett, Stereotaxic display of brain lesions. Behav Neurol, 2000. 12(4): p. 191-200. Metwali, H. and A. Samii, Seed-Based Connectivity Analysis of Resting-State fMRI in Patients with Brain Tumors: A Feasibility Study. World Neurosurg, 2019. Park, J.E., et al., Alteration of long-distance functional connectivity and network topology in patients with supratentorial gliomas. Neuroradiology, 2016. 58(3): p. 311-20. Tie, Y., et al., Defining language networks from resting-state fMRI for surgical planning--a feasibility study. Hum Brain Mapp, 2014. 35(3): p. 1018-30. Roder, C., et al., Resting-state functional MRI in an intraoperative MRI setting: proof of feasibility and correlation to clinical outcome of patients. J Neurosurg, 2016. 125(2): p. 4019. Metwali, H., et al., Resting-State Functional Connectivity in Neurosurgical Patients Under Propofol Anesthesia: Detectability and Variability Between Patients and Between Sessions. World Neurosurg, 2019. 9
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
23. 24. 25. 26. 27. 28.
29. 30. 31. 32.
Metwali, H., et al., The reliability of fMRI in brain tumor patients: a critical review and metaanalysis. World Neurosurg, 2019. Esposito, R., et al., Modifications of default-mode network connectivity in patients with cerebral glioma. PLoS One, 2012. 7(7): p. e40231. Harris, R.J., et al., Altered functional connectivity of the default mode network in diffuse gliomas measured with pseudo-resting state fMRI. J Neurooncol, 2014. 116(2): p. 373-9. Chen, H., et al., Increased frontal functional networks in adult survivors of childhood brain tumors. Neuroimage Clin, 2016. 11: p. 339-346. Kim, E., et al., Vascular phenotyping of brain tumors using magnetic resonance microscopy (muMRI). J Cereb Blood Flow Metab, 2011. 31(7): p. 1623-36. Deane, B.R. and P.L. Lantos, The vasculature of experimental brain tumours. Part 1. A sequential light and electron microscope study of angiogenesis. J Neurol Sci, 1981. 49(1): p. 55-66. Jain, R.K., et al., Angiogenesis in brain tumours. Nat Rev Neurosci, 2007. 8(8): p. 610-22. Chow, D.S., et al., Glioblastoma Induces Vascular Dysregulation in Nonenhancing Peritumoral Regions in Humans. AJR Am J Roentgenol, 2016. 206(5): p. 1073-81. Watkins, S., et al., Disruption of astrocyte-vascular coupling and the blood-brain barrier by invading glioma cells. Nat Commun, 2014. 5: p. 4196. Agarwal, S., et al., Neurovascular uncoupling in resting state fMRI demonstrated in patients with primary brain gliomas. J Magn Reson Imaging, 2016. 43(3): p. 620-6.
21 22
10
1
Figure legends
2
Figure 1: Significantly higher connectivity of the anterior (A, B) and posterior (C, D)
3
cerebellar networks with the supratentorial networks was seen in patients with brain tumors
4
than in healthy control subjects.
5
Figure 2: Significantly higher connectivity of the right (A) and left (B) sensorimotor cortices
6
with the cerebellum was seen in patients with brain tumors than in healthy control subjects.
7
Figure 3: The frontoparietal (A, B) and salience (C, D) networks had significantly higher
8
connectivities with different supra and infratentorial networks in patients with brain tumors
9
than in healthy control subjects.
10 11
Table 1: Patient characteristics.
11
Age Sex (M:F) Tumor locaton
Glioma grade
Recurrent tumor Previous radiation
18-77 years (mean 54 years) 18:16 Frontal: 14 patients Temporal: 10 Parietal: 7 patients Occipital: 3 patients Grade I: 2 patients Grade II: 7 patients Grade III: 9 patients Grade VI: 16 patients 3 patients 3 patients Table 1: Characteristics of the patients
Disclosure I, Hussam Metwali, certify that this manuscript is a unique submission and is not being considered for publication, in part or in full, with any other source in any medium. All authors have seen and approved the final version of this manuscript before submission. None of the authors have any undisclosed conflicts of interest related to the submission of this manuscript. We have no financial interest in material or devices described in this article and have received no financial support in conjunction with the generation of this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Best Regards Dr. Hussam Metwali, MD MSc. Neurosurgeon INI-Hannover, Germany 03.11.2019
Abbreviations fMRI: functional magnetic resonance imaging, BOLD: blood oxygen level dependent, RSN: resting state networks
Metwali H, Raemaekers M: Conceptualization, Methodology, Software, Statistical analysis. Metwali H, Tamer I: Writing- Original draft preparation. Samii A: Supervision. Metwali H, Raemaekers M, Samii A: Writing- Reviewing and Editing.