Biology subjects

Crimi, A.

Publications and source records attributed to Crimi, A..

At least 19 recordsLinked to original sources

bioRxiv2024

Structure-Function Relationship in Electrical and Hemodynamic Brain Networks: Insights from EEG and fNIRS during Rest and Task States

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, we characterize global and local structure-function coupling using source-reconstructed Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.

neuroscience↗

bioRxiv2024

Exploring the Impact of Ischemic Stroke on Brain Function: Insights from Functional Connectivity Gradients and Temporal Lag Correction

Ischemic brain stroke disrupts blood flow, leading to functional and structural changes associated with behavioral deficits. Importantly, despite this disruption occurring in localized regions, the resulting changes in the functional organization are both high-dimensional and widespread across the human cortex. However, the mechanisms with which these global patterns emerge and the subsequent behavioral deficits they entail, remain largely unexplored. Functional connectivity gradients provide consistent, reproducible, and robust low-dimensional representations of brain function that can be explored to reduce brain heterogeneity to a handful of axes along which brain function is organized. Here, we investigated how stroke disrupts this canonical gradient space by aligning each patient to a control-averaged gradient embedding and computing the distances to the "correct" positions to quantify functional deviations and their contribution to behavioral deficits. Importantly, we explicitly corrected these gradients for stroke-induced hemodynamic lags to further study their contribution. We found that lag correction enhanced the functional connectivity gradients most prominently in the second gradient, on which visual and somatomotor function is concentrated. Additionally, we identified significant functional deviations primarily within somatomotor, visual, and ventral attention networks, correlating with behavioral impairments. We studied the hemispheric asymmetries of these deviations finding that intact hemispheres preserve comparable patterns of asymmetry while damaged ones presented important changes. Lastly, right-sided lesions displayed more localized functional deviations than their contralateral lesions. Overall, we provide evidence that 1) correcting for hemodynamic lags improves gradient accuracy, as indicated by increased percentages of explained variance, and 2) behavioral impairments and hemispheric asymmetries result from a repositioning of region-based connectivity profiles in a low-dimensional, interpretable space. This suggests that large-scale brain function alterations manifest in slight, predictable movements largely confined to the visual-somatomotor axis.

neuroscience↗

bioRxiv2023

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

The brain is a complex system with functional and structural networks. Different neuroimaging methods have their strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques has gained interest, but understanding how the information derived from these modalities is related remains an exciting open question. Successful integration of these modalities requires a sophisticated mathematical framework that goes beyond simple comparative analyses. The multilayer network model has emerged as a promising approach. This study is an extended version of the conference paper "Resting State Brain Connectivity Analysis from EEG and FNIRS Signals" [5]. In this study, we explored the brain network properties obtained from EEG and fNIRS data using graph analysis. Additionally, we adopted the multilayer network model to evaluate the benefits of combining multiple modalities compared to using a single modality. A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which is dependent on the modality and on the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

neuroscience↗

bioRxiv2023

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for A Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features

Gliomas are primary brain tumors that arise from neural stem cells or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of the images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. Importantly, as an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. A challenge of the study was given by a small sample size of human leukocyte antigen stained on glioma tissue microarrays dataset - 203 images from 5 classes - and imbalanced data distribution. This has been addressed by image augmentation of the underrepresented classes. For this glioma multiclass classification task, a residual neural network architecture has been adapted. On the validation set the average accuracy was 0.72 when the model was trained from scratch, and 0.85 with the pre-trained model. Moreover, the tumor microenvironment analysis suggested a relevant role of the myeloid cells and their accumulation to characterize glioma grades. This promising approach can be used as an additional diagnostic tool to improve assessment during intra-operative examination or sub-typing tissues for treatment selection, despite the challenges caused by the difficult dataset. We present here the distributions and visualizations of extracted tumor inter-dependencies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/544739v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@a85f48org.highwire.dtl.DTLVardef@7a49forg.highwire.dtl.DTLVardef@1c8df75org.highwire.dtl.DTLVardef@c6bad7_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIResearch highlight 1: We demonstrate that the ResNet-18 architecture with simple data augmentation trained in 10-fold cross-validation performs the multiclass classification relatively well even with a small imbalanced dataset with a high degree of similarities between classes. C_LIO_LIResearch highlight 2: After supervised subtyping of the tumor, we investigated the usefulness of the protocol for discovery and learning tumor microenvironment elements for the same task. The protocol designed for deriving new biomarkers based on multiplex stained histological samples proved the ability to detect features characteristic of malignant tumors based only on single target stained tissue microarrays. We propose further studies on this topic can help in formulating specific criteria for improvements in diagnosis of gliomas, allowing to avoid the necessity of conducting advanced histopathological analysis or complementing genetic testing of tumor samples. C_LI

bioinformatics↗

bioRxiv2023

Directed networks and resting-state effective brain connectivity with state-space reconstruction using reservoir computing causality

Causality theory is a complex field involving philosophy, mathematics, and computer science. It relies on the temporal precedence of cause over a consequence or unidirectional propagation of changes. Despite these simple principles, normative modeling of causal relationships is conceptually and computationally challenging. Model-free approaches provide insights into large, complex, and chaotic networks, but suffer from false positive and false negative inferences caused by meaningless statistical and temporal correlations. Machine learning advancements have extended these data-driven methods to nonlinear systems, yet inherited similar drawbacks as linear approaches. Interestingly, newer proposals within this model-free paradigm reverse the temporal precedence using the internal structure of the driven variable to recover information from the driving one. Efficient machine learning models combined with these state space reconstruction methods automate part of the process, potentially reducing inductive biases during training and inference. However, their translation into neuroscience, especially neuroimaging, is limited due to complex interpretations and a lack of systematic analyses of the results. Here, we exploited these methods combining them with normative analyses to reconstruct chaotic relationships and networks emerging from neuroimaging data. We validated the proposed scores with a chaotic yet solved system and rebuilt brain networks both in synthetic and real scenarios. We compared our method and heuristics with well-established alternatives providing a comprehensive and transparent benchmark. We obtained higher accuracies and reduced false inferences compared to Granger causality in tasks with known ground truth. When tested to unravel directed influences in brain networks meaningful predictions were found to exist between nodes from the default mode network. The presented framework explores reservoir computing for causality detection, offering a conceptual detour from traditional premises and has the potential to provide theoretical guidance opening perspectives for studying cognition and neuropathologies. Author summaryIn sciences, reliable methods to distinguish causes from consequences are crucial. Despite some progress, researchers are often unsatisfied with the current understanding of causality modeling and its predictions. In neuroscience, causality detection requires imposing world models or assessing statistical utility to predict future values. These approaches, known as model-based and model-free, have advantages and drawbacks. A recent model-free approach augmented with artificial networks tries to autonomously explore the internal structure of the system, (i.e, the state space), to identify directed predictions from consequences to causes but not the other way around. This has not been extensively studied in large networks nor in the human brain, and systematic attempts to reveal its capabilities and inferences are lacking. Here, the proposal is expanded to large systems and further validated in chaotic systems, challenging neuronal simulations, and networks derived from real brain activity. Although the manuscript does not claim true causality, it presents new ideas in the context of current trends in data-driven causality theory. Directed networks encoding causality are hypothesized to contain more information than correlation-based relationships. Hence, despite its evident difficulties, causality detection methods can hold the key to new and more precise discoveries in brain health and disease.

neuroscience↗

bioRxiv2023

3D reconstructions of brain from MRI scans using neural radiance fields

The advent of 3D Magnetic Resonance Imaging (MRI) has revolutionized medical imaging and diagnostic capabilities, allowing for more precise diagnosis, treatment planning, and improved patient outcomes. 3D MRI imaging enables the creation of detailed 3D reconstructions of anatomical structures that can be used for visualization, analysis, and surgical planning. However, these reconstructions often require many scan acquisitions, demanding a long session to use the machine and requiring the patient to remain still, with consequent possible motion artifacts. The development of neural radiance fields (NeRF) technology has shown promising results in generating highly accurate 3D reconstructions of MRI images with less user input. Our approach is based on neural radiance fields to reconstruct 3D projections from 2D slices of MRI scans. We do this by using 3D convolutional neural networks to address challenges posed by variable slice thickness; incorporating multiple MRI modalities to ensure robustness and extracting the shape and volumetric depth of both surface and internal anatomical structures with slice interpolation. This approach provides more comprehensive and robust 3D reconstructions of both surface and internal anatomical structures and has significant potential for clinical applications, allowing medical professionals to better visualize and analyze anatomical structures with less available data, potentially reducing times and motion-related issues.

neuroscience↗

bioRxiv2023

Resting State Brain Connectivity analysis from EEG and FNIRS signals

Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both shortand longrange organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.

neuroscience↗

bioRxiv2023

An intact oculomotor neural circuit in congenital blindness

In the past three decades, multiple studies revealed that congenital blindness is associated with functional and structural reorganization in early visual areas and its interaction with other neural systems. Among the most reproducible findings is the weaker connectivity between the visual and sensorimotor cortices, which in sighted individuals plays a role in eye-motor coordination. Here we demonstrate an important exception to this reorganization phenomena: we find that in congenitally blind individuals (as for normally sighted ones), spontaneous, non-controlled eye movements correlate with connectivity between visual and sensorimotor cortices. Furthermore, using time-lagged regression, we show that eye movements drive activity in the visual cortex, which subsequently drives sensorimotor activity. Remarkably, this phenomenon persists even though blind participants often exhibit unsynchronized eye movements and cannot sense or regulate their eye positions. These findings provide evidence of a preserved, "non-functional" connectivity in congenitally blind individuals and reveal a firm, hard-wired constraint on connectivity that remains immune to any reorganization phenomena produced by lack of visual experience and oculomotor control.

neuroscience↗

bioRxiv2022

Community Detection in Brain Connectome using Quantum Annealer Devices

Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with segregated regions integrated to facilitate different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explore community detection within brain connectomes using the power of quantum annealers, and in particular the Leaps Hybrid Solver. Our results shows that quantum annealers can achieve higher modularity index compared to classical annealer while computing communities of brain connectomes. Those promising preliminary results points out that quantum annealers might be the better choice compared to classical computing optimization process.

neuroscience↗

bioRxiv2022

Structural Reorganization Following a Brain Tumor: A Machine Learning Study Considering Desynchronized Functional Oscillations

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and oedema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find that intra-tumor signals do exist and are correlated with alterations present both in healthy tissue and resting-state networks. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

neuroscience↗

bioRxiv2022

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning

The pervasive impact of Alzheimers disease on aging society represents one of the main challenges at this time. Current investigations highlight two specific misfolded proteins in its development: Amyloid-{beta} and{tau} . Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on a machine learning approach. The proposed method applies an autoregressive model, constrained by structural connectivity, to predict concentrations of Amyloid-{beta} two years after the provided baseline. In experiments, the autoregressive model generally outperformed the state-of-art models yielding the lowest average prediction error (mean-squared-error 0.0062). Moreover, we assess its effectiveness and suitability for real case scenarios, for which we provide a web service for physicians and researchers. Despite predicting amyloid pathology alone is not sufficient to clinical outcome, its prediction can be helpful to further plan therapies and other cures.

neuroscience↗

bioRxiv2022

The Rosenbluth sampling Calculation of Hydrophobic-Polar Model

AO_SCPLOWBSTRACTC_SCPLOWLattice proteins are models resembling real proteins. They comprise an energy function and a set of conditions specifying the interaction between elements occupying adjacent lattice sites. In this paper we present an approach examining the behavior of chains of a large number of molecules. We investigate this by solving a restricted random walk problem on a cubic lattice and square lattice. More specifically, we apply the hydrophobic-polar model to examine the spatial characteristics of protein folds using the Monte Carlo method. This technique is the so-called Rosenbluth sampling method for solving restricted random walk problems. Specifically, by solving such walks we obtain plausible folds. In addition, this method can be extended to solve the hydrophobic-polar model. In this paper, we describe this method as an algorithm that calculates the energy spectrum for the hydrophobic-polar model, and the related formula for estimating the number of folds. Moreover, we estimate the number of folds for each sequence using hydrophobic-polar model energy estimation. On test sequences the predicted protein folds were obtained with a mismatch of one unit according to the energy. We also observe that the estimated number of folds depends only on the length and not on the type of sequence. This promising strategy can be extended to quantify other proteins in nature.

bioinformatics↗

bioRxiv2020

Classification and Generation of MicroscopyImages with Plasmodium Falciparum via ArtificialNeural Networks

BackgroundRecent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. MethodsTaking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts. ResultsThe tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50% misclassification for another dataset of images. ConclusionThe proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

bioinformatics↗

bioRxiv2020

Whole brain optoacoustic tomography reveals strain-specific regional beta-amyloid densities in Alzheimer`s disease amyloidosis models

Deposition of beta-amyloid (A{beta}) deposits is one major histopathological hallmark of Alzheimers disease (AD). Here, we introduce volumetric multi-spectral optoacoustic tomography (vMSOT), which covers 10x10x10 mm3 field-of-view, capable of 3D whole mouse brain imaging. We show for the first time the optoacoustic properties of oxazine-derivative AOI987 probe, which binds to A{beta}, and the application of vMSOT for the quantification of brain-wide A{beta} deposition. Administration of AOI987 to two common transgenic mouse strains of AD amyloidosis led to a retention of the probe in A{beta}-laden brain regions. Co-registered of vMSOT data to a brain atlas revealed strain-specific pattern of AOI987 uptake. A comparison with ex vivo light-sheet microscopy in cleared mouse brains showed a good correspondence in A{beta} distribution. Lastly, we demonstrate the specificity of the AOI987 probe by immunohistochemistry. vMSOT with AOI987 facilitates preclinical brain region-specific studies of A{beta} spread and accumulation, and the monitoring of putative treatments targeting A{beta}.

neuroscience↗

bioRxiv2020

BiGen: Integrative Clinical and Brain-Imaging Genetics Analysis Using Structural Equation Model

The identification of genetic variants associated with complex brain diseases has evolved in the past decades. Studies in the field have taken different approaches and study designs including genome-wide association studies. Neuroimaging and connectomics have also improved our understanding of the structural connectivity of the human brain and produced reliable measurements. Combining both neuroimaging and genetic characteristics significantly contributes to understand their complex relationship in affecting behaviour and cognition. Throughout this thesis we proposed analysis pipeline to study the association between imaging and genetics of two different types of brain disease, which is, Alzheimers disease and glioblastoma. We observe the need for a unified model to study the complex interplay between genetic, environmental and clinical, neuroimaging and phenotype features. In this chapter, we developed BiGen, a mathematical model to measure the inter-correlation structure through the integration of genetic, environmental, neuroimaging and disease measurements. We utilised the structural equation model and used a path construct of latent variables to study the hidden association between genes and brain-related diseases, mediated by connectivity characteristics. We applied BiGen to simulated data and to a dataset from the Alzheimers Disease Neuroimaging Initiative.

bioinformatics↗

bioRxiv2019

An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of a-synuclein

Neuropathological and experimental evidence suggests that the cell-to-cell transfer of a-synuclein has an important role in the pathogenesis of Parkinsons disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook an siRNA, genome-wide high throughput screen to identify genes regulating the cell-to-cell transfer of a-synuclein. We transiently transfected HEK cells stably overexpressing a-synuclein with a construct encoding GFP-2a-aSynuclein-RFP. The cells expressing a-synuclein-RFP through transfection were double positive for GFP and RFP fluorescence, whereas the cells receiving it through transfer were positive only for RFP fluorescence. The amount of a-synuclein transfer was quantified by high content microscopy. A series of unbiased screens confirmed the involvement of 38 genes in the regulation of a-synuclein-RFP transfer. One of those hits was ITGA8, a candidate gene recently identified through a large PD genome wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) showed that the hits clustered in networks that included known PD Mendelian and GWAS risk genes more frequently than expected than random chance. Given the genetic overlap between a-synuclein transfer and PD, those findings provide supporting evidence for the importance of the cell-to-cell transfer of a-synuclein in the pathogenesis of PD, and expand our understanding of the mechanism of a-synuclein spread.

neuroscience↗

bioRxiv2019

Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer’s Disease

Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the genetic variations on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimers disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. Here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimers genetic risk factors in the estimation of regional brain connectivity alterations.

bioinformatics↗

bioRxiv2018

Pre-operarative Multivariate Connectome Analysis for Glioma Patients

Recent advances in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. A system-based analysis of gliomas can shed light on mechanisms underlying neuronal connectivity and plasticity and the recovery process, and it could support surgical decision-making. Surgery has been shifting from image-guided surgery to a functional mapping-guided resection where several structural and functional modalities are used. However, reliable identification of eloquent areas during planning of surgical resection is still a challenge. Pre-operative language mapping performed by navigated transcranial magnetic stimulation (nTMS) is of great value as it elucidates functional cortical organization, which might be different from patient to patient due to the heterogeneity of the lesions and individual plasticity. In this paper we propose the construction of an \"effective\" speech network used for surgical decision making. This is achieved by mapping functionally relevant areas identified by nTMS on tractography-based connectomics. Subsequently we compute graph metrics on the identified speech networks of patients who show preoperative aphasia aiming to identify relevant differences between graph metrics in patients with and without preoperative aphasia. Lastly, the validity of the speech networks is examined by checking the involved graph communities.

neuroscience↗