Reconstruction of the Frontal Aslant Tract (FAT) in patients with different types of frontal lobe tumors, considering diverse methods of delineation

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Reconstruction of the Frontal Aslant Tract (FAT) in patients with different types of frontal lobe tumors, considering diverse methods of delineation

https://doi.org/10.21203/rs.3.rs-3203697/v1

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The Frontal Aslant Tract (FAT) is a bundle of nerve fibers located in the brain that connects the frontal lobe to the pars aslant of Broca's area. It serves as a crucial neural pathway involved in language regulation, speech, and motor control. The FAT is part of the broader cingulo-fronto-parietal network, facilitating communication between the frontal and posterior regions of the brain.

The objective of this study is to investigate the connectivity of the Frontal Aslant Tract using diffusion tensor imaging-based fiber tractography analysis. We compared the fiber count, tract length, shape, and projections of the FAT in patients with frontal tumors to a control group.

Among patients diagnosed with brain tumors, we observed a reduction in the number of nerve fibers within the FAT and a decrease in pathway volume. When delineating the FAT using ROIs encompassing the superior frontal gyrus and endpoint in the pars opercularis of the inferior frontal gyrus, we obtained the highest fiber count. The specific anatomy of the FAT is heavily influenced by the choice of ROI and endpoint. Accurate identification of the FAT in patients with frontal lobe tumors is crucial to prevent postoperative aphasia.

TractographyMR

neurographyMRI

DTI

FAT

brain tumors

The frontal aslant tract (FAT) is a short fiber tract predominantly located in the left hemisphere, as described by Catani in 2012. It connects the superior frontal gyrus (SFG) with the inferior frontal gyrus (IFG) and supplementary motor area (SMA). Extensive analysis of the fiber course, fMRI findings, and direct cortical stimulation have demonstrated the significant role of FAT in speech processing(Catani et al. 2012).

The role of the FAT extends beyond mere connectivity, as it serves as a critical connection between motor planning, initiation, and language control. FAT is involved in various language functions, particularly speech production and language control. The pre-SMA, connected to the IFG through the FAT, plays a role in several aspects of language processing, such as articulatory planning and control. Conversely, the IFG plays a vital role in language production, speech fluency, and language control processes(Dick et al. 2019).

The FAT facilitates the transformation of linguistic intentions into motor plans, ensuring the seamless execution of speech production(Aron et al. 2007).

Despite the growing interest and an increase in publications on the Frontal Aslant Tract (FAT), there remains limited knowledge about this white matter bundle in clinical practice. FAT exhibits connections to the upper frontal lobe, lower frontal lobe, supplementary motor area (SMA), and pre-SMA. Damage to these white matter connections can have more significant clinical effects compared to damage to the cerebral cortex. This is because the consequences of cortical damage can be mitigated over time due to the high plasticity of the cortex(La Corte et al. 2021).

Therefore, and due to the numerous functions performed by FAT, there is a need to popularize knowledge about the anatomy of the SMA region and related white matter pathways. This subject should be particularly well understood, especially in the case of frontal lobe tumor surgery(Burkhardt et al. 2021).

The objective of this study is to present the anatomical variability of the Frontal Aslant Tract (FAT), with a particular emphasis on demonstrating the length and volume of the pathway based on the chosen Region of Interest (ROI). Additionally, this study aims to evaluate the variability of essential tractography parameters, including Fractional Anisotropy (FA), Mean Diffusivity (MD), and Apparent Diffusion Coefficient (ADC), based on the histopathological diagnosis of tumors located in the frontal lobe region.

Patients treated for frontal lobe tumors at a single institution were retrospectively reviewed. A total of thirty-eight adult patients (20 males, 18 females) who underwent resection surgery at the Neurosurgery and Neurology Department of Jan Biziel University Hospital in Bydgoszcz (blinded for peer review purposes) between 2020 and 2022 were included in the study. Additionally, thirty healthy individuals without any intracranial lesions were selected as a control group. The mean age in the treated group was 56.93 ± 15.46, while in the control group, it was 55.67 ± 18.04.

The study focused on patients diagnosed with a brain tumor involving the frontal lobe. Among them, seventeen patients had a tumor affecting the right frontal lobe, while twenty-one patients had a tumor affecting the left frontal lobe. All patients underwent surgical treatment.

The study protocol was approved by the local bioethical committee (KB 532/2020), and all participants in both the study and control groups provided informed consent to participate.

MRI and DTI Acquisition

In this study, MRI and DTI acquisition parameters similar to those previously developed by the authors in other publications were used. This is because all investigations in this study, as well as in the authors' previous publications, were conducted using the same MRI scanner, and tractography was generated using the same software (DSI studio). Therefore, the methodology section in the article has already been published before.

All patients underwent imaging at 3.0 T using a Philips Ingenia scanner manufactured in 2015 and a 32-channel head coil. The head was scanned without any angulation, with an angle of 0° in all directions (AP, RL, FH).

The axial DTI sequence was performed with the following parameters: scan type: imaging; scan mode: MS; scan technique: SE; acquisition mode: cartesian; fast imaging mode: EPI; EPI factor: 45; shot mode: single-shot; diffusion mode: DTI; gradient overpuls: no; directional resolution: medium (15); number of b-factors: 2 (b1-0, b2-800); echoes: 1; TE/TR: shortest: 85/3232 ms; slice thickness: 2.5 mm; slice gap: 0 mm; number of signal averages (NSA): 2; phase encoding: AP; FOV: 224 mm (FH) × 224 mm (AP) × 140 mm (RL); acquisition matrix: 92 × 90; reconstruction matrix: 128; acquisition voxel size: 2.43 mm (RL), 2.49 mm (AP), 2.50 mm (FH); reconstruction voxel size: 1.75 mm (RL), 1.75 mm (AP), 2.50 mm (FH); fold-over suppression: no; halfscan: yes-factor 0.8; water-fat shift: minimum; RF Shims: adaptive; shim: auto; fat suppression: SPIR; strength: strong; flip angle: 90°; acceleration factor (SENSE): P reduction (AP) = 2, minimum number of packages: 2; slice scan order: default, rest slabs/plan align/interactive positioning: no; SAR mode: high; SAR allow first level: yes; PNS mode: moderate; gradient mode: default; softtone mode: no; reconstruction mode: immediate; preparation phases: auto; reference tissue: white matter; preset window contrast: soft; image filter: system default; geometry correction: default; uniformity correction: no; total scan duration: about 7 min.

A deterministic fiber tracking method was used with a DTI diffusion scheme and a total of 60 diffusion sampling directions. The in-plane resolution was 1.87514 mm, and the slice thickness was 2 mm. The angular threshold for fiber tracking was set at 90 degrees. Regions of interest (ROIs) were automatically defined based on an anatomical atlas loaded into the DSI Studio program (http://dsi-studio.labsolver.org).(Kierońska et al. 2020; Szmuda et al. 2021a; Kierońska-Siwak et al. 2023)

Statistic analysis

The statistical analysis was performed using Statistica 13 software. The Shapiro-Wilk test was used to assess the normal distribution of qualitative data. Parametric tests, such as Student's t-test for dependent and independent variables, and Pearson's rank correlation test, were employed if the data exhibited normal distribution.

For data that did not follow a normal distribution, non-parametric tests were used. The Mann-Whitney test was utilized for group comparisons, the Wilcoxon test for dependent variables, and the Spearman's rank correlation test for analyzing correlations. A significance level of p < 0.05 was considered for all analyses.

DTI analysis

All analysis of images were provided using DSI Studio software (dsistudio.labsolver.org, BSD License.). We calculated the fractional anisotropy (FA), the mean diffusivity (MD) and the apparent diffusion coefficient (ADC). The anisotropy threshold was determined automatically by the software. A total of 15000 tracts were calculated. When reconstructing the FAT, we obtained tract statistics, including: the number of tracks, the mean length, the volume of the FAT, the FA, and the MD values.(Kierońska et al. 2020)

Fiber tracking

The ROI was selected according to brain landmarks that the FAT passed through and the anatomical automatic atlas provided by DSI Studio software. We reconstructed FAT based on region of interest (ROI): ROI 1- gyrus frontalis superior (SFG) and ROI 2- Supplementary motor area (SMA). End points based on pars opercularis of gyrus frontalis inferior (IFG-op) and pars triangularis of gyrus frontalis inferior (IFG-tri).

Start points were designated as: gyrus frontalis supertior-(SFG) and supplementary motor area (SMA). The end points were based as a: gyrus frontalis inferior pars triangularis (IFG-tri) and gyrus frontalis inferior pars opercularis (IFG-op). By mixing and matching of start and end points we obtainted FAT for analysis by four different types in each patients [Fig. 1].

Figure 2 also shows FAT plotted in 4 ways with individual ROIs and end points marked.

The FAT was reconstructed using four different algorithms for each patient in the study. The fiber tracts were visualized using DSI-studio to demonstrate the anatomical accuracy of the reconstruction results, and the CST fiber bundles were overlaid with the FA image.

Among the patients, 14 were diagnosed with primary tumors, specifically glioblastoma multiforme WHO IV (IDH-mutant or IDH wildtype), and 5 patients had astrocytoma anaplasticum WHO III. Additionally, 19 patients had metastatic tumors, including 10 with lung cancer metastasis, 6 with colorectal cancer, and 3 with melanoma.

Table 1 summarizes the average values of FA and MD. The average values of the number of fibers, tract volume, and length of tracts are provided in the supplementary material.

Significantly higher volumes (p < 0.001; 40806.7 ± 9284.5 vs. 52831.7 ± 2399.0; treated group vs. control group) and numbers of FAT tracts (p < 0.001; 1548.5 ± 397.1 vs. 2231.7 ± 119.5; treated group vs. control group) were observed when the ROI was set at the superior frontal gyrus (SFG) with an endpoint at the inferior frontal gyrus pars opercularis (IFG op), compared to other ROIs (all p < 0.05) in both the control and treated groups. Using ROI-2 at the SMA with an endpoint at the inferior frontal gyrus pars triangularis (IFG tra) resulted in over 90% fewer fibers and a smaller volume of FAT.

The longest FAT, in both the study and control groups, was obtained when selecting ROI 1 (superior frontal gyrus) with an endpoint at the inferior frontal gyrus pars triangularis (9.41; SD ± 3.9 vs 89.7; SD ± 4.0; study group vs. control group).

In the study group, the number of fibers and volume of FAT were lower than in the control group, regardless of the chosen method for FAT plotting. Additionally, a significant statistical difference (p < 0.05) in the number of fibers was identified between patients in the control and study groups, in each of the selected projections. Regarding the volume of the tract, statistical significance was achieved in the SMA-IFG tri projection (p < 0.001; 464.8 ± 92.5 vs. 632.3 ± 89.6; study group vs. control group), SMA-IFG op projection (p < 0.001; 2738.3 ± 1374.7 vs. 5028.3 ± 1238.1; study group vs. control group), and SFG-IFG op projection (p < 0.001; 40806.7 ± 9284.5 vs. 52831.7 ± 2399.0; study group vs. control group). However, in terms of fiber length, the threshold of statistical significance was reached only in the SMA-IFG projection (p = 0.045; 81.8 ± 6.4 vs. 86.5 ± 5.7; study group vs. control group).

The FA value was statistically significantly lower (p < 0.05) in the study group, while the MD value was statistically significantly higher in this group in each method of FAT determination. The exact data of the above parameters have been collected and presented in table no 1 however, no statistically significant differences were found between the results for the FA parameter in people from the glioblastoma and metastasis groups, regardless of the selected projection.. Comparison of FA and MD values depending on ROIs and endpoints in the group of patients with primary tumors and metastases was presented on Table 2.

Table 1

Comparison of FA and MD values in the study and control groups depending on the ROI and endpoints.
	MD study group			MD control group			p value	FA study group			FA control group			p value
	\(\overline{x}\)	SD	Me	\(\overline{x}\)	SD	Me	p value	\(\overline{x}\)	SD	Me	\(\overline{x}\)	SD	Me	p value
SFG-IFGop	0,77	0,13	0,72	0,49	0,06	0,50	< 0,001	0,42	0,05	0,42	0,69	0,09	0,69	< 0,001
SFG-IFG tra	0,78	0,09	0,77	0,48	0,07	0,49	< 0,001	0,42	0,04	0,42	0,74	0,06	0,74	< 0,001
SMA-IFGop	0,75	0,09	0,72	0,48	0,07	0,49	< 0,001	0,44	0,05	0,46	0,76	0,07	0,78	< 0,001
SMA-IFG tra	0,77	0,06	0,79	0,47	0,08	0,49	< 0,001	0,46	0,06	0,46	0,76	0,07	0,79	< 0,001

Comparison of the examined parameters between patients with glioblastoma and metastasis revealed statistically significant differences for MD (p < 0.001) regardless of the selected projection. The specific values are presented in Table 2. However, no statistically significant differences were found for the remaining parameters in the projections used, except for the SMA-IFG op projection. In this projection, statistically significant differences were also identified in the number of fibers (p = 0.028; 179.6 ± 90.0 vs. 304.6 ± 106.5; glioblastoma vs. metastasis) and tract volume (p = 0.032; 2040.8 ± 1042.0 vs. 3535.4 ± 1323.9; glioblastoma vs. metastasis).

Table 2

Comparison of FA and MD values depending on ROIs and endpoints in the group of patients with primary tumors and metastases.
	MD GBM group			MD metastasis group			p value	FA GBM group			FA metastasis group			p value
	\(\overline{x}\)	SD	Me	\(\overline{x}\)	SD	Me	p value	\(\overline{x}\)	SD	Me	\(\overline{x}\)	SD	Me	p value
SFG-IFGop	0,66	0,04	0,67	0,89	0,05	0,89	< 0,001	0,42	0,06	0,41	0,42	0,03	0,42	0,985
SFG-IFG tra	0,70	0,03	0,70	0,86	0,05	0,84	< 0,001	0,42	0,04	0,43	0,42	0,03	0,41	0.874
SMA-IFGop	0,68	0,04	0,69	0,84	0,04	0,82	< 0,001	0,44	0,06	0,43	0,45	0,03	0,46	0.862
SMA-IFG tra	0,72	0,04	0,71	0,82	0,03	0,82	< 0,001	0,46	0,08	0,45	0,46	0,03	0,46	0.875

Based on the analysis of the results obtained in the group of cancer patients, statistically significant differences (p < 0.05) were observed between the results recorded in the SFG-IFG op and SMA-IFG op ROIs for the parameters related to the number of fibers (p < 0.001; 1548.5 ± 397.1 vs. 237.9 ± 114.3; SFG vs. SMA) and tract volume (p < 0.001; 40806.7 ± 9284.5 vs. 2738.3 ± 1374.7; SFG vs. SMA). However, the parameters MD, FA, and fiber length did not reach the level of statistical significance. A strong positive correlation was identified for MD using Pearson's rank correlation test (0.9 < R < 1.0), and it reached statistical significance (p < 0.001).

In the same group, when analyzing data obtained in the SFG-IFG tra vs. SMA-IFG tra ROIs, statistical significance (p < 0.05) was observed for the number of fibers (p < 0.001; 470.1 ± 106.8 vs. 24.8 ± 11.0; SFG vs. SMA) and tract volume (p = 0.001; 6288.5 ± 3790.3 vs. 464.8 ± 92.5; SFG vs. SMA), as well as for fiber length (p = 0.008; 91.41 ± 3.92 vs. 87.18 ± 6.544; SFG vs. SMA) and FA (p = 0.037; 0.42 ± 0.004 vs. 0.46 ± 0.006; SFG vs. SMA). However, MD and fiber length analysis results in the above ROIs did not reach statistical significance. Additionally, two statistically significant (p < 0.05) positive correlations were identified for fiber length (p = 0.022) and MD (p < 0.001).

In the control group, comparisons were also made between the results obtained in the SFG-IFG op and SMA-IFG op ROIs. Differences exceeding the threshold of statistical significance (p < 0.05) were noted for the number of fibers (p < 0.001; 2231.7 ± 119.5 vs. 446.1 ± 59.2; SFG vs. SMA) and tract volume (p = 0.001; 52831.7 ± 2399.0 vs. 5028.3 ± 1238.1; SFG vs. SMA). However, when analyzing data obtained in the SFG-IFG tri and SMA-IFG tri projections, statistically significant differences (p < 0.05) were observed for the number of fibers (p < 0.001; 804.6 ± 143.1 vs. 40.9 ± 7.0; SFG vs. SMA), tract volume (p < 0.001; 8510.2 ± 1040.9 vs. 632.3 ± 89.6; SFG vs. SMA), and fiber length (p = 0.004; 89.69 ± 3.97 vs. 86.64 ± 6.08; SFG vs. SMA). No statistically significant differences were found for the remaining parameters.

In this study, we employed DTI diffusion tractography as a valuable non-invasive technique for mapping white matter fibers in patients with brain tumors. This method plays a critical role in surgical planning, allowing for a balance between radical tumor resection and preservation of neuronal functions (Dubey et al. 2018; Kieronska and Słoniewski 2020). However, DTI has limitations that can impact the quality of white matter fiber visualization. Factors such as reduced fractional anisotropy (FA), mass effect, tumor infiltration, neoplastic vascular edema, and destruction of white matter tracts can disrupt the spatial orientation of fibers and alter the direction of water diffusion, thereby affecting the effectiveness of the technique. (Alexander et al. 2001; Provenzale et al. 2004; Abhinav et al. 2014; Szmuda et al. 2021b)

DTI has shown promise in the differential diagnosis of glioblastoma and metastatic tumors, but consistent data from independent studies are lacking (Jiang et al. 2014a). Our results comparing glioblastoma and metastatic tumors demonstrated a statistically significant difference in the MD parameter across all four projections, with higher average diffusivity values observed in metastatic tumors.(Jiang et al. 2014a)

These findings differ from the existing literature, highlighting the lack of consensus regarding DTI metrics when comparing glioblastoma and metastatic tumors (Sternberg et al. 2014; Fordham et al. 2021; Yeh et al. 2021). Byrnes et al. reported higher MD values in the glioblastoma area, with even higher values in the tissue surrounding the metastasis compared to the peri-glioblastoma region (Byrnes et al. 2011)

In our study, we did not find statistically significant differences in the FA parameter between patients with glioblastoma and metastases. Moreover, the values obtained were practically identical regardless of the defined fiber assignment by continuous tracking (FAT) projections. This result could be attributed to the small sample size in our study. However, existing literature suggests higher FA values in glioblastoma compared to metastatic tumors (Bauer et al. 2015; Bette et al. 2016; Holly et al. 2017). The FA parameter reflects water diffusion direction for each voxel, determining the degree of anisotropy in the studied structure (Beaulieu 2002). Several variables, such as fiber structural integrity, packing density, myelination degree, and fiber diameter, significantly influence this parameter (Kinoshita et al. 2008; Chen et al. 2010; O’Donnell and Westin 2011). Wang et al. proposed that the increase in fractional anisotropy in glioblastoma might be related to the spatial orientation of the tumor-overproduced extracellular matrix infiltrating adjacent healthy tissue, suggesting a relationship between scalar values and cell orientation in the imaged voxel. These authors also reported significantly higher FA values in glioblastoma compared to metastatic tumors and did not find statistically significant differences in the MD parameter between the two (Wang et al. 2009) However, there is a lack of consistency in the available literature regarding FA and MD parameters. For instance, Wang et al. and Reiche et al. reported lower fractional anisotropy in gliomas compared to brain metastases (Wang et al. 2009; Reiche et al. 2010)

The heterogeneity of results obtained in subsequent studies may be attributed to differences in the choice of techniques for defining the region of interest (ROI) and the small size of patient groups. Various hypotheses have been proposed to explain the inconsistency of results. Rees et al. suggested that discrepancies arise from the inclusion of different tumor areas in the study. The solid part of the tumor, which corresponds to the enhanced area, contains fibers that have been damaged or displaced by tumor expansion. On the other hand, the non-enhanced area consists of both solid tumor tissue and remnants of white matter fibers, which can result in increased FA values. (Tsuchiya et al. 2005; Reiche et al. 2010; Tsougos et al. 2012). On the other hand, Wang et al. suggested that higher FA values result from increased cellularity in glioblastoma compared to metastasis, leading to a reduction in extracellular space volume and an increase in water diffusion directivity (Tsougos et al. 2012).

Higher FA values have also been associated with highly differentiated brain tumors, along with lower MD values compared to intracranial metastases, likely due to differences in tumor structure organization (Tsuchiya et al. 2005). However, even in this case, the data on DTI metrics for differentiating high-grade gliomas and metastases are inconsistent (Jiang et al. 2014b).

It is important to consider that developing tumors impact surrounding tissues, resulting in changes such as vascular edema and fiber infiltration, sometimes leading to damage (Alexander et al. 2001; Provenzale et al. 2004; Abhinav et al. 2014). These changes are reflected in FA and MD values. Byrnes et al. demonstrated significant differences in these parameters in the edema area surrounding glioblastoma and metastases. MD was significantly higher, and FA was significantly reduced in the edema surrounding metastases compared to edema around glioblastoma multiforme. Additionally, the glioblastoma area showed significantly higher MD. Based on these findings, the authors concluded that simultaneous determination of DTI metrics in both the tumor area and adjacent peritumoral tissue could serve as a reliable tool for differentiating glioblastoma from intracranial metastasis (Byrnes et al. 2011). Lu et al. suggested that lower FA values in the tissue around glioblastoma are a consequence of the destructive effect of tumor-derived cells on white matter fibers (Lu et al. 2004).

Furthermore, it should be considered whether the histological type of intratumoral metastasis may influence DTI parameters in the surrounding tissue.

During our analysis, we observed a statistically significant difference in the MD parameter between metastatic tumors and primary lesions, consistently observed in each selected projection.

Another proposed explanation for the differences in FA and MD between primary tumors and metastases is the dissimilarity in the nature of edema surrounding the lesions. Peritumoral edema predominantly forms around metastatic tumors, leading to an increase in MD. In contrast, tumor infiltration edema, characteristic of glioblastoma, results in lower MD values. Additionally, the FA value around the tumor does not show a significant difference for both types of lesions due to overlapping effects of both types of edema to varying degrees(Lee et al. 2013). This theory partly explains our MD results for metastatic tumors.

In our study, we investigated the characteristics of FAT and presented different methods for its determination. The presence of a brain tumor affects the parameters of FAT tractography, leading to a reduction in the volume of the pathway and the number of fibers within the bundle. Furthermore, we observed differences in MD depending on the type of brain tumor. However, due to the use of various ROIs and endpoints, it is not possible to determine a single optimal method for determining FAT.

Funding: This research received no external funding.

Human ethics: All patients signed informed consent to participate in the study. The consent for the study was issued by the local bioethics committee with the number KB 532

Conflicts of interest: The authors declare no conflict of interest.

Data availability: No experimental data or materials have been used for this review other than the published and reviewed bibliography.

Abhinav K, Yeh FC, Pathak S, et al (2014) Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review. Biochim Biophys Acta Mol Basis Dis 1842:2286–2297
Alexander AL, Hasan KM, Lazar M, et al (2001) Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med 45:770–780. https://doi.org/10.1002/mrm.1105
Aron AR, Behrens TE, Smith S, et al (2007) Triangulating a cognitive control network using diffusion-weighted Magnetic Resonance Imaging (MRI) and functional MRI. Journal of Neuroscience 27:3743–3752. https://doi.org/10.1523/JNEUROSCI.0519-07.2007
Bauer AH, Erly W, Moser FG, et al (2015) Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion. Neuroradiology 57:697–703. https://doi.org/10.1007/s00234-015-1524-6
Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system - A technical review. NMR Biomed 15:435–455
Bette S, Huber T, Wiestler B, et al (2016) Analysis of fractional anisotropy facilitates differentiation of glioblastoma and brain metastases in a clinical setting. Eur J Radiol 85:2182–2187. https://doi.org/10.1016/j.ejrad.2016.10.002
Burkhardt E, Kinoshita M, Herbet G, Herbet Guillaume G (2021) Functional anatomy of the frontal aslant tract and surgical perspectives. J Neurosurg Sci 10. https://doi.org/10.23736/S0390
Byrnes TJD, Barrick TR, Bell BA, Clark CA (2011) Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo. NMR Biomed 24:54–60. https://doi.org/10.1002/nbm.1555
Catani M, Dell’Acqua F, Vergani F, et al (2012) Short frontal lobe connections of the human brain. Cortex 48:273–291. https://doi.org/10.1016/j.cortex.2011.12.001
Chen Y, Shi Y, Song Z (2010) Differences in the architecture of low-grade and high-grade gliomas evaluated using fiber density index and fractional anisotropy. Journal of Clinical Neuroscience 17:824–829. https://doi.org/10.1016/j.jocn.2009.11.022
Dick AS, Garic D, Graziano P, Tremblay P (2019) The frontal aslant tract (FAT) and its role in speech, language and executive function. Cortex 111:148–163
Dubey A, Kataria R, Sinha V (2018) Role of diffusion tensor imaging in brain tumor surgery. Asian J Neurosurg 13:302–306. https://doi.org/10.4103/ajns.ajns_226_16
Fordham AJ, Hacherl CC, Patel N, et al (2021) Differentiating glioblastomas from solitary brain metastases: An update on the current literature of advanced imaging modalities. Cancers (Basel) 13
Holly KS, Barker BJ, Murcia D, et al (2017) High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases. Front Surg 4:. https://doi.org/10.3389/fsurg.2017.00018
Jiang R, Du FZ, He C, et al (2014a) The value of diffusion tensor imaging in differentiating high-grade gliomas from brain metastases: A systematic review and meta-analysis. PLoS One 9
Jiang R, Du FZ, He C, et al (2014b) The value of diffusion tensor imaging in differentiating high-grade gliomas from brain metastases: A systematic review and meta-analysis. PLoS One 9
Kieronska S, Słoniewski P (2020) The usefulness and limitations of diffusion tensor imaging – a review study. Eur J Transl Clin Med 2:43–51. https://doi.org/10.31373/ejtcm/112437
Kierońska S, Sokal P, Dura M, et al (2020) Tractography-based analysis of morphological and anatomical characteristics of the uncinate fasciculus in human brains. Brain Sci 10:1–15. https://doi.org/10.3390/brainsci10100709
Kierońska-Siwak S, Sokal P, Jabłońska M, et al (2023) Structural Connectivity Reorganization Based on DTI after Cingulotomy in Obsessive–Compulsive Disorder. Brain Sci 13:. https://doi.org/10.3390/brainsci13010044
Kinoshita M, Hashimoto N, Goto T, et al (2008) Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage 43:29–35. https://doi.org/10.1016/j.neuroimage.2008.06.041
La Corte E, Eldahaby D, Greco E, et al (2021) The Frontal Aslant Tract: A Systematic Review for Neurosurgical Applications. Front Neurol 12
Lee EJ, Ahn KJ, Lee EK, et al (2013) Potential role of advanced MRI techniques for the peritumoural region in differentiating glioblastoma multiforme and solitary metastatic lesions. Clin Radiol 68
Lu S, Ahn D, Johnson G, et al (2004) Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: Introduction of the tumor infiltration index. Radiology 232:221–228. https://doi.org/10.1148/radiol.2321030653
O’Donnell LJ, Westin CF (2011) An introduction to diffusion tensor image analysis. Neurosurg Clin N Am 22:185–196
Provenzale JM, McGraw P, Mhatre P, et al (2004) Peritumoral brain regions in glionias and meningiomas: Investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 232:451–460. https://doi.org/10.1148/radiol.2322030959
Reiche W, Schuchardt V, Hagen T, et al (2010) Differential diagnosis of intracranial ring enhancing cystic mass lesions-Role of diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI). Clin Neurol Neurosurg 112:218–225. https://doi.org/10.1016/j.clineuro.2009.11.016
Sternberg EJ, Lipton ML, Burns J (2014) Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors. American Journal of Neuroradiology 35:439–444
Szmuda T, Kierońska S, Ali S, et al (2021a) Tractography-guided surgery of brain tumours: what is the best method to outline the corticospinal tract? Folia Morphologica (Poland) 80:40–46. https://doi.org/10.5603/FM.A2020.0016
Szmuda T, Kierońska S, Ali S, et al (2021b) Tractography-guided surgery of brain tumours: what is the best method to outline the corticospinal tract? Folia Morphologica (Poland) 80:40–46. https://doi.org/10.5603/FM.A2020.0016
Tsougos I, Svolos P, Kousi E, et al (2012) Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T. Cancer Imaging 12:423–436. https://doi.org/10.1102/1470-7330.2012.0038
Tsuchiya K, Fujikawa A, Nakajima M, Honya K (2005) Differentiation between solitary brain metastasis and high-grade glioma by diffusion tensor imaging. British Journal of Radiology 78:533–537. https://doi.org/10.1259/bjr/68749637
Wang W, Steward CE, Desmond PM (2009) Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. American Journal of Neuroradiology 30:203–208. https://doi.org/10.3174/ajnr.A1303
Yeh FC, Irimia A, Bastos DC de A, Golby AJ (2021) Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 245:. https://doi.org/10.1016/j.neuroimage.2021.118651

No competing interests reported.

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Reconstruction of the Frontal Aslant Tract (FAT) in patients with different types of frontal lobe tumors, considering diverse methods of delineation

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Abstract

Figures

Introduction

Material and methods

MRI and DTI Acquisition

Statistic analysis

DTI analysis

Fiber tracking

Result

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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