CNTM-01. Evaluating Traditional and Non-Traditional Eloquent Areas in Patients with Brain Tumors: Large-scale Network Analysis Using a Machine Learning-Based Platform

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi224-vi224
Author(s):  
Alexis Morell ◽  
Daniel Eichberg ◽  
Ashish Shah ◽  
Evan Luther ◽  
Victor Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Networks ◽  
Tumor Resection ◽  
Central Executive ◽  
Default Mode ◽  
Eloquent Areas ◽  
Neurologic Deficits

Abstract BACKGROUND Developing mapping tools that allow identification of traditional or non-traditional eloquent areas is necessary to minimize the risk of postoperative neurologic deficits. The objective of our study is to evaluate the use of a novel cloud-based platform that uses machine learning to identify cerebral networks in patients with brain tumors. METHODS We retrospectively included all adult patients who underwent surgery for brain tumor resection or thermal ablation at our Institution between the 16th of February and the 15th of May of 2021. Pre and postoperative contrast-enhanced MRI with T1-weighted and high-resolution Diffusion Tensor Imaging (DTI) sequences were uploaded into the Quicktome platform. After processing the data, we categorized the integrity of seven large-scale brain networks: sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention and limbic. Affected networks were correlated with pre and postoperative clinical data, including neurologic deficits. RESULTS Thirty-five (35) patients were included in the study. The average age of the sample was 63.2 years, and 51.4% (n=18) were females. The most affected network was the central executive network (40%), followed by the dorsal attention and default mode networks (31.4%), while the least affected were the visual (11%) and ventral attention networks (17%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (p=0.021), compared to patients without deficits. In addition, we found that patients without neurologic deficits had an average of 2.06 large-scale networks affected, with 75% of them not being related to traditional eloquent areas as the sensorimotor, language or visual circuits. CONCLUSIONS The Quicktome platform is a practical tool that allows automatic visualization of large-scale brain networks in patients with brain tumors. Although further studies are needed, it may assist in the surgical management of traditional and non-traditional eloquent areas.

Associations between clinical outcome and tractography based on navigated transcranial magnetic stimulation in patients with language-eloquent brain lesions

2020 ◽  
Vol 132 (4) ◽  
pp. 1033-1042 ◽  
Author(s):  
Nico Sollmann ◽  
Alessia Fratini ◽  
Haosu Zhang ◽  
Claus Zimmer ◽  
Bernhard Meyer ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Brain Tumors ◽  
Risk Stratification ◽  
Magnetic Stimulation ◽  
Diffusion Tensor ◽  
Tumor Resection ◽  
Navigated Transcranial Magnetic Stimulation ◽  
Language Mapping ◽  
Significant Difference ◽  
Related Pathway

OBJECTIVENavigated transcranial magnetic stimulation (nTMS) in combination with diffusion tensor imaging fiber tracking (DTI FT) is increasingly used to locate subcortical language-related pathways. The aim of this study was to establish nTMS-based DTI FT for preoperative risk stratification by evaluating associations between lesion-to-tract distances (LTDs) and aphasia and by determining a cut-off LTD value to prevent surgery-related permanent aphasia.METHODSFifty patients with left-hemispheric, language-eloquent brain tumors underwent preoperative nTMS language mapping and nTMS-based DTI FT, followed by tumor resection. nTMS-based DTI FT was performed with a predefined fractional anisotropy (FA) of 0.10, 0.15, 50% of the individual FA threshold (FAT), and 75% FAT (minimum fiber length [FL]: 100 mm). The arcuate fascicle (AF), superior longitudinal fascicle (SLF), inferior longitudinal fascicle (ILF), uncinate fascicle (UC), and frontooccipital fascicle (FoF) were identified in nTMS-based tractography, and minimum LTDs were measured between the lesion and the AF and between the lesion and the closest other subcortical language-related pathway (SLF, ILF, UC, or FoF). LTDs were then associated with the level of aphasia (no/transient or permanent surgery-related aphasia, according to follow-up examinations).RESULTSA significant difference in LTDs was observed between patients with no or only surgery-related transient impairment and those who developed surgery-related permanent aphasia with regard to the AF (FA = 0.10, p = 0.0321; FA = 0.15, p = 0.0143; FA = 50% FAT, p = 0.0106) as well as the closest other subcortical language-related pathway (FA = 0.10, p = 0.0182; FA = 0.15, p = 0.0200; FA = 50% FAT, p = 0.0077). Patients with surgery-related permanent aphasia showed the lowest LTDs in relation to these tracts. Thus, LTDs of ≥ 8 mm (AF) and ≥ 11 mm (SLF, ILF, UC, or FoF) were determined as cut-off values for surgery-related permanent aphasia.CONCLUSIONSnTMS-based DTI FT of subcortical language-related pathways seems suitable for risk stratification and prediction in patients suffering from language-eloquent brain tumors. Thus, the current role of nTMS-based DTI FT might be expanded, going beyond the level of being a mere tool for surgical planning and resection guidance.

Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

2018 ◽  
Vol 30 (9) ◽  
pp. 1209-1228 ◽  
Author(s):  
David Rothlein ◽  
Joseph DeGutis ◽  
Michael Esterman
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Networks ◽  
Relevant Information ◽  
Mental States ◽  
Attention Task ◽  
Default Mode ◽  
Neural Representations ◽  
Similarity Structure ◽  
Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

Subcortical Stimulation with Tip of Ultrasound Aspirator

2021 ◽  
Author(s):  
Henry Colle ◽  
David Colle ◽  
Bonny Noens ◽  
Bob Dhaen ◽  
Giovanni Alessi ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Current Practice ◽  
Tumor Resection ◽  
Intracranial Lesion ◽  
Eloquent Areas ◽  
Fiber Tracts ◽  
Current Transmission ◽  
Subcortical Stimulation ◽  
Tissue Trauma ◽  
White Fiber

Background During resection of intrinsic brain tumors in eloquent areas, particularly under awake mapping, subcortical stimulation is mandatory to avoid irreversible deficits by damaging white fiber tracts. The current practice is to alternate between subcortical stimulation with an appropriate probe and resection of tumoral tissue with an ultrasound aspiration device. Switching between different devices induces supplementary movement and possible tissue trauma, loss of time, and inaccuracies in the localization of the involved area. Objective To use one device for both stimulation as well as a resecting tool. Methods The tip of different ultrasound aspiration devices is currently used for monopolar current transmission (e.g., for vessel coagulation in liver surgery). We use the same circuitry for monopolar subcortical stimulation when connected with the usual stimulator devices. Results We have applied this method since 2004 in over 500 patients during tumor resection with cortical and subcortical stimulation, mostly with awake language and motor monitoring. Conclusion A method is presented using existing stimulation and wiring devices by which simultaneous subcortical stimulation and ultrasonic aspiration are applied with the same tool. The accuracy, safety, and speed of intrinsic intracranial lesion resection can be improved when subcortical stimulation is applied.

scholarly journals Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning

2020 ◽  
Vol 27 ◽  
pp. 102262 ◽  
Author(s):  
Andrew A. Nicholson ◽  
Sherain Harricharan ◽  
Maria Densmore ◽  
Richard W.J. Neufeld ◽  
Tomas Ros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Executive ◽  
Default Mode

Neurodegeneration of brain networks in the amyotrophic lateral sclerosis–frontotemporal lobar degeneration (ALS–FTLD) continuum: evidence from MRI and MEG studies

CNS Spectrums ◽  
2017 ◽  
Vol 23 (6) ◽  
pp. 378-387 ◽  
Author(s):  
Francesca Trojsi ◽  
Pierpaolo Sorrentino ◽  
Giuseppe Sorrentino ◽  
Gioacchino Tedeschi
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Frontotemporal Lobar Degeneration ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Imaging Techniques ◽  
Brain Networks ◽  
Treatment Strategies ◽  
Early Onset Dementia ◽  
Connectivity Patterns ◽  
Lateral Sclerosis

Brain imaging techniques, especially those based on magnetic resonance imaging (MRI) and magnetoencephalography (MEG), have been increasingly applied to study multiple large-scale distributed brain networks in healthy people and neurological patients. With regard to neurodegenerative disorders, amyotrophic lateral sclerosis (ALS), clinically characterized by the predominant loss of motor neurons and progressive weakness of voluntary muscles, and frontotemporal lobar degeneration (FTLD), the second most common early-onset dementia, have been proven to share several clinical, neuropathological, genetic, and neuroimaging features. Specifically, overlapping or mildly diverging brain structural and functional connectivity patterns, mostly evaluated by advanced MRI techniques—such as diffusion tensor and resting-state functional MRI (DT–MRI, RS–fMRI)—have been described comparing several ALS and FTLD populations. Moreover, though only pioneering, promising clues on connectivity patterns in the ALS–FTLD continuum may derive from MEG investigations. We will herein overview the current state of knowledge concerning the most advanced neuroimaging findings associated with clinical and genetic patterns of neurodegeneration across the ALS–FTLD continuum, underlying the possibility that network-based approaches may be useful to develop novel biomarkers of disease for adequately designing and monitoring more appropriate treatment strategies.

NCOG-46. WHITE MATTER TRACT INVOLVEMENT BY INTRA-AXIAL BRAIN TUMORS; DIAGNOSTIC & THERAPEUTIC IMPLICATIONS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi162-vi162
Author(s):  
Saqib Kamran Bakhshi ◽  
Ayesha Quddusi ◽  
Danish Mahmood ◽  
Muhammad Waqas ◽  
Muhammad Shahzad Shamim ◽  
...  
Keyword(s):  
White Matter ◽  
Brain Tumors ◽  
Functional Outcome ◽  
Diffusion Tensor ◽  
Tumor Resection ◽  
Karnofsky Performance Scale ◽  
Low Grade ◽  
High Grade ◽  
Poor Functional Outcome

Abstract Diffusion tensor imaging (DTI) is a relatively recent modality which aids in visualization of WMT and their relation to intracranial lesions. Despite almost two decades since the beginning of its use in tumor resection, there is still dearth of data on its diagnostic and prognostic value from low- and middle-income countries. We aimed to assess the pattern of involvement of white matter tracts (WMT) by intra-axial brain tumors on DTI. Secondary objectives were to evaluate implications of involvement of WMT on surgical resection, and post-operative functional outcome. This was a retrospective study of 77 consecutive patients, who underwent DTI guided surgery for brain tumors. The involvement of WMT by tumors on DTI was assessed by a radiologist (who was blind to the pathology) using the Witwer classification. The pathology was reported by histopathologists using WHO brain tumor classification. Karnofsky Performance Scale (KPS) was used for assessing patients’ neurological status at admission, and at follow-up. Forty-five (58.4%) out of 77 tumors reviewed, caused infiltration of WMT, whereas only 22 (28.6%) tumors caused displacement of WMT (p = 0.040). Among 32 cases of astrocytoma, involvement of WMTs was influenced by the grade of tumor (p = 0.012), as high-grade tumors caused infiltration (19; 59.4%), unlike low grade tumors which commonly caused displacement (2; 50%). Oligodendroglioma caused infiltration/disruption of WMTs in most cases, irrespective of the grade (19 out of 25 cases; 76%). At last follow-up, 27 (35.1%) patients showed improvement in KPS and 14 (18.2%) reported deterioration, while there was no change observed in 36 (46.8%) patients. Infiltration of WMTs was associated with poor functional outcome. We conclude that intra-axial brain tumors mostly cause infiltration of WMTs, particularly high-grade astrocytoma, and oligodendroglioma of any grade. Infiltration of WMTs is associated with poor functional outcome at follow-up.

Intraoperative magnetic resonance imaging–guided tractography with integrated monopolar subcortical functional mapping for resection of brain tumors

2011 ◽  
Vol 114 (3) ◽  
pp. 719-726 ◽  
Author(s):  
Sujit S. Prabhu ◽  
Jaime Gasco ◽  
Sudhakar Tummala ◽  
Jefrey S. Weinberg ◽  
Ganesh Rao
Keyword(s):  
Brain Tumors ◽  
Diffusion Tensor ◽  
Tumor Resection ◽  
Motor Evoked Potential ◽  
Neurological Deficits ◽  
Resection Margins ◽  
Neurological Outcomes ◽  
Close Proximity ◽  
The Mean ◽  
Subcortical Mapping

Object The object of this study was to describe the utility and safety of using a single probe for combined intraoperative navigation and subcortical mapping in an intraoperative MR (iMR) imaging environment during brain tumor resection. Methods The authors retrospectively reviewed those patients who underwent resection in the iMR imaging environment, as well as functional electrophysiological monitoring with continuous motor evoked potential (MEP) and direct subcortical mapping combined with diffusion tensor imaging tractography. Results As a navigational tool the monopolar probe used was safe and accurate. Positive subcortical fiber MEPs were obtained in 10 (83%) of the 12 cases. In 10 patients in whom subcortical MEPs were recorded, the mean stimulus intensity was 10.4 ± 5.2 mA and the mean distance from the probe tip to the corticospinal tract (CST) was 7.4 ± 4.5 mm. There was a trend toward worsening neurological deficits if the distance to the CST was short, and a small minimum stimulation threshold was recorded indicating close proximity of the CST to the resection margins. Gross-total resection (95%–100% tumor removal) was achieved in 11 cases (92%), whereas 1 patient (8%) had at least a 90% tumor resection. At the end of 3 months, 2 patients (17%) had persistent neurological deficits. Conclusions The monopolar probe can be safely implemented in an iMR imaging environment both for navigation and stimulation purposes during the resection of intrinsic brain tumors. In this study there was a trend toward worsening neurological deficits if the distance from the probe to the CST was short (< 5 mm) indicating close proximity of the resection cavity to the CST. This technology can be used in the iMR imaging environment as a surgical adjunct to minimize adverse neurological outcomes.

Surgical benefits of combined awake craniotomy and intraoperative magnetic resonance imaging for gliomas associated with eloquent areas

2017 ◽  
Vol 127 (4) ◽  
pp. 790-797 ◽  
Author(s):  
Kazuya Motomura ◽  
Atsushi Natsume ◽  
Kentaro Iijima ◽  
Shunichiro Kuramitsu ◽  
Masazumi Fujii ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Tumor Resection ◽  
Residual Tumor ◽  
Awake Craniotomy ◽  
The Other ◽  
Intraoperative Mri ◽  
Awake Surgery ◽  
Eloquent Areas ◽  
Mri Studies ◽  
The Brain

OBJECTIVEMaximum extent of resection (EOR) for lower-grade and high-grade gliomas can increase survival rates of patients. However, these infiltrative gliomas are often observed near or within eloquent regions of the brain. Awake surgery is of known benefit for the treatment of gliomas associated with eloquent regions in that brain function can be preserved. On the other hand, intraoperative MRI (iMRI) has been successfully used to maximize the resection of tumors, which can detect small amounts of residual tumors. Therefore, the authors assessed the value of combining awake craniotomy and iMRI for the resection of brain tumors in eloquent areas of the brain.METHODSThe authors retrospectively reviewed the records of 33 consecutive patients with glial tumors in the eloquent brain areas who underwent awake surgery using iMRI. Volumetric analysis of MRI studies was performed. The pre-, intra-, and postoperative tumor volumes were measured in all cases using MRI studies obtained before, during, and after tumor resection.RESULTSIntraoperative MRI was performed to check for the presence of residual tumor during awake surgery in a total of 25 patients. Initial iMRI confirmed no further tumor resection in 9 patients (36%) because all observable tumors had already been removed. In contrast, intraoperative confirmation of residual tumor during awake surgery led to further tumor resection in 16 cases (64%) and eventually an EOR of more than 90% in 8 of 16 cases (50%). Furthermore, EOR benefiting from iMRI by more than 15% was found in 7 of 16 cases (43.8%). Interestingly, the increase in EOR as a result of iMRI for tumors associated mainly with the insular lobe was significantly greater, at 15.1%, than it was for the other tumors, which was 8.0% (p = 0.001).CONCLUSIONSThis study revealed that combining awake surgery with iMRI was associated with a favorable surgical outcome for intrinsic brain tumors associated with eloquent areas. In particular, these benefits were noted for patients with tumors with complex anatomy, such as those associated with the insular lobe.

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme

2019 ◽  
Vol 61 (7) ◽  
pp. 757-765 ◽  
Author(s):  
Shai Shrot ◽  
Moshe Salhov ◽  
Nir Dvorski ◽  
Eli Konen ◽  
Amir Averbuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumors ◽  
Perfusion Imaging ◽  
Diffusion Tensor ◽  
Learning Scheme

scholarly journals A graph neural network framework for causal inference in brain networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Wein ◽  
W. M. Malloni ◽  
A. M. Tomé ◽  
S. M. Frank ◽  
G. -I. Henze ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Structural Information ◽  
Brain Networks ◽  
Scale Up ◽  
Brain Regions ◽  
Neural Activation ◽  
Dynamic Interactions ◽  
The Brain

AbstractA central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

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