Evaluating cellularity and structural connectivity on whole brain slides using a custom-made digital pathology pipeline

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.

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Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

10.1101/2020.12.03.410688 ◽

2020 ◽

Author(s):

Kyesam Jung ◽

Simon B. Eickhoff ◽

Oleksandr V. Popovych

Keyword(s):

Data Processing ◽

Resting State ◽

Optimal Parameter ◽

Structural Connectivity ◽

Model Performance ◽

Brain Dynamics ◽

Dynamical Modeling ◽

Whole Brain ◽

Brain Connectome ◽

Network Properties

AbstractDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

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Towards an Efficient Validation of Dynamical Whole-brain Models

10.21203/rs.3.rs-1139051/v1 ◽

2021 ◽

Author(s):

Kevin J. Wischnewski ◽

Simon B. Eickhoff ◽

Viktor K. Jirsa ◽

Oleksandr V. Popovych

Keyword(s):

Structural Connectivity ◽

Three Dimensional ◽

Bayesian Optimization ◽

High Dimensional ◽

Brain Dynamics ◽

Model Parameters ◽

Phase Oscillators ◽

Dimensional Parameter ◽

Whole Brain ◽

Brain Models

Abstract Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

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Clinically Weighted Probabilistic Tractography Mapping of Therapeutic Thalamic Deep Brain Stimulation for Essential Tremor

Neurosurgery ◽

10.1093/neuros/nyz310_364 ◽

2019 ◽

Vol 66 (Supplement_1) ◽

Author(s):

Evangelia Tsolaki ◽

Alon Kashanian ◽

Nader Pouratian

Keyword(s):

Deep Brain Stimulation ◽

Clinical Outcome ◽

Essential Tremor ◽

Clinical Improvement ◽

Brain Stimulation ◽

Structural Connectivity ◽

Precentral Gyrus ◽

Probabilistic Tractography ◽

Whole Brain ◽

Deep Brain

Abstract INTRODUCTION Traditional targeting methods rely on indirect targeting with atlas-defined coordinates that induce interpatient anatomical and functional variability. Precise targeting is crucial for successful surgical intervention associated with improved surgical outcomes. Here, we use clinically weighted probabilistic tractography to investigate the connectivity from volume of tissue activated (VTA) to whole brain in order to evaluate the relationship between structural connectivity and clinical outcome of patients that underwent thalamic deep brain stimulation (DBS). METHODS Magnetic resonance imaging and clinical outcomes from 10 essential tremor (ET) patients who were treated by VIM-DBS at the University of California Los Angeles were evaluated. LeadBDS was used for the VTA calculation and FSL was used to evaluate the whole brain probabilistic tractography of VTA. Tractography maps were binarized and weighted based on the percent of clinical improvement using the Fahn-Tolosa-Martin Tremor Rating Score. The resulting clinically weighted maps were non-linearly fused to MNI space and averaged. These population maps provide a voxel-by-voxel map of the average clinical improvement observed when the VTA demonstrates structural connectivity to the whole brain. RESULTS The VTA connectivity to the whole brain was delineated. Superior clinical improvement was associated with connectivity to voxels connecting the thalamus to the precentral gyrus and to the brainstem/cerebellum. Also, the clinical efficacy map showed that patients with higher clinical improvement (>70%) presented stronger structural connectivity to the precentral gyrus and to the caudal projection to the cerebellum. CONCLUSION Stronger connectivity to the precentral gyrus and to brainstem/cerebellum is associated with superior clinical outcome in thalamic DBS for ET. In the future, rather than focusing on connectivity to predetermined targets, these clinically weighted tractography maps can be used with a reverse algorithm to identify the optimal region of the thalamus to provide clinically superior results.

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TP3-5 Structural connectivity driven stereoelectroencephalography (SEEG) electrode targeting in suspected pseudotemporal and temporal plus epilepsy

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2019-abn.60 ◽

2019 ◽

Vol 90 (3) ◽

pp. e19.1-e19

Author(s):

M Della Costanza ◽

VN Vakharia ◽

K Li ◽

M Mancini ◽

SB Vos ◽

...

Keyword(s):

Temporal Lobe ◽

Structural Connectivity ◽

Posterior Part ◽

Middle Part ◽

Fusiform Gyrus ◽

Angular Gyrus ◽

Computer Assisted ◽

Epileptogenic Zone ◽

Whole Brain ◽

Lingual Gyrus

ObjectivesOne third of patients with drug resistant focal mesial temporal lobe epilepsy (MTLE) fail to achieve long-term seizure freedom following temporal lobe resections. Reasons for failure may include ictal onset outside the temporal lobe (TL), termed ‘pseudotemporal lobe epilepsy’ (pTLE), with propagation from strongly connected neighboring areas or temporal plus (TL+) epilepsy, when the epileptogenic zone primarily involves the temporal lobe and also extends to neighboring regions. In such cases the perisylvian and orbito-frontal (OF) cortices, cingulum and temporo-parieto-occipital junction may be implicated. Stereoelectroencephalography (SEEG) is a procedure in which electrodes are stereotactically placed within predefined brain regions to delineate the SOZ and allows evaluation of deep anatomical structures adjacent to the TL. SEEG electrode contacts sample from a core radius of 3–5 mm. It is unclear which sub-regions of target structures should be preferentially implanted to optimally detect the network involved in seizure onset and rapid propagation. Using normalized average group templates of structural connectivity from patients with hippocampal sclerosis (HS), we determine the greatest connectivity to critical sub-regions and based upon this propose optimal locations for SEEG targeting.DesignObservational cross-sectional study.SubjectsTwelve patients with HS (6 right) that had undergone SEEG and pre-operative diffusion imaging were identified from a prospectively maintained database.MethodsWhole brain connectomes with 10 million tracts were generated using cortical seed regions derived from whole brain GIF parcellations. Normalized group templates were generated separately for right and left HS patients. Orbitofrontal cortex (OF), insula (INS), cingulum (Cing) and temporo-parietal-occipital junction (supramarginal gyrus, angular gyrus, precuneus, fusiform gyrus and lingual gyrus) were segmented into surgically targetable subregions. All subregions had similar volumes. Connectivity of the amygdalohippocampal complex (AHC) was defined based on the number of streamlines terminating in the subregions of interest.ResultsLeft HS showed preferential connections to the ipsilateral: posterior part of lateral OF cortex, posterior short gyrus of anterior INS, posterior part of the posterior Cing, middle part of lingual gyrus, posterior part of precuneus and middle part of fusiform gyrus. Right HS showed preferential connections to the ipsilateral: posterior part of the lateral OF cortex, anterior long gyrus of posterior INS, posterior part of posterior Cing, anterior part of lingual gyrus and posterior part of precuneus.ConclusionsUsing whole brain connectomes we determine surgically feasible targets in sub-regions based on greatest connectivity to the AHC. We propose that SEEG targeting utilizing computer-assisted planning may improve the understanding of the overall network connectivity in order to enhance the diagnostic utility of the SEEG implantation. SEEG electrode placement within structures associated with pTLE and TL +may aid in delineating the SOZ if the correct sub-regions are targeted. This should be evaluated prospectively.

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Heritability of hierarchical structural brain network

10.1101/209635 ◽

2017 ◽

Author(s):

Moo K. Chung ◽

Zhan Luo ◽

Nagesh Adluru ◽

Andrew L. Alexander ◽

Davidson J. Richard ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Brain Networks ◽

Structural Connectivity ◽

Imaging Study ◽

Brain Network ◽

Genetic Contribution ◽

Whole Brain ◽

Diffusion Weighted ◽

Structural Brain Network ◽

Structural Brain

ABSTRACTWe present a new structural brain network parcellation scheme that can subdivide existing parcellations into smaller subregions in a hierarchically nested fashion. The hierarchical parcellation was used to build multilayer convolutional structural brain networks that preserve topology across different network scales. As an application, we applied the method to diffusion weighted imaging study of 111 twin pairs. The genetic contribution of the whole brain structural connectivity was determined. We showed that the overall heritability is consistent across different network scales.

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neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

Cognitive Computation ◽

10.1007/s12559-021-09931-9 ◽

2021 ◽

Author(s):

Caglar Cakan ◽

Nikola Jajcay ◽

Klaus Obermayer

Keyword(s):

Empirical Data ◽

Brain Region ◽

Structural Connectivity ◽

Brain Regions ◽

Brain Network ◽

Whole Brain ◽

Neural Mass ◽

Set Up ◽

Function Relationship ◽

Relationship Of

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

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The Lamellar Structure of the Brain Fiber Pathways

Neural Computation ◽

10.1162/neco_a_00896 ◽

2016 ◽

Vol 28 (11) ◽

pp. 2533-2556 ◽

Cited By ~ 8

Author(s):

Vitaly L. Galinsky ◽

Lawrence R. Frank

Keyword(s):

Angular Distribution ◽

Vector Field ◽

Lamellar Structure ◽

Structural Connectivity ◽

High Confidence ◽

Whole Brain ◽

Fiber Tracts ◽

The Individual ◽

The Brain

We present a quantitative statistical analysis of pairwise crossings for all fibers obtained from whole brain tractography that confirms with high confidence that the brain grid theory (Wedeen et al., 2012a ) is not supported by the evidence. The overall fiber tracts structure appears to be more consistent with small angle treelike branching of tracts rather than with near-orthogonal gridlike crossing of fiber sheets. The analysis uses our new method for high-resolution whole brain tractography that is capable of resolving fibers crossing of less than 10 degrees and correctly following a continuous angular distribution of fibers even when the individual fiber directions are not resolved. This analysis also allows us to demonstrate that the whole brain fiber pathway system is very well approximated by a lamellar vector field, providing a concise and quantitative mathematical characterization of the structural connectivity of the human brain.

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Global scaling for semi-quantitative analysis in FP-CIT SPECT

Nuklearmedizin ◽

10.3413/nukmed-0659-14-04 ◽

2014 ◽

Vol 53 (06) ◽

pp. 234-241 ◽

Cited By ~ 13

Author(s):

C. Lange ◽

G. Ulrich ◽

H. Amthauer ◽

W. Brenner ◽

D. Kupitz ◽

...

Keyword(s):

Quantitative Analysis ◽

Single Photon ◽

Photon Emission ◽

Statistical Parametric Mapping ◽

Performance Measure ◽

Emission Computed Tomography ◽

Reference Region ◽

Whole Brain ◽

Custom Made ◽

The Mean

SummarySemi-quantitative characterization of dopamine transporter availability from single photon emission computed tomography (SPECT) with 123I-ioflupane (FP-CIT) is based on uptake ratios relative to a reference region. The aim of this study was to evaluate the whole brain as reference region for semiquantitative analysis of FP-CIT SPECT. The rationale was that this might reduce statistical noise associated with the estimation of non-displaceable FP-CIT uptake. Patients, methods: 150 FP-CIT SPECTs were categorized as neurodegenerative or non-neurode- generative by an expert. Semi-quantitative analysis of specific binding ratios (SBR) was performed with a custom-made tool based on the Statistical Parametric Mapping software package using predefined regions of interest (ROIs) in the anatomical space of the Montreal Neurological Institute. The following reference regions were compared: predefined ROIs for frontal and occipital lobe and whole brain (without striata, thalamus and brainstem). Tracer uptake in the reference region was characterized by the mean, median or 75th percentile of its voxel intensities. The area (AUC) under the receiver operating characteristic curve was used as performance measure. Results: The highest AUC of 0.973 was achieved by the SBR of the putamen with the 75th percentile in the whole brain as reference. The lowest AUC for the putamen SBR of 0.937 was obtained with the mean in the frontal lobe as reference. Conclusion: We recommend the 75th percentile in the whole brain as reference for semi-quantitative analysis in FP-CIT SPECT. This combination provided the best agreement of the semi-quantitative analysis with visual evaluation of the SPECT images by an expert and, therefore, is appropriate to support less experienced physicians.

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Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

10.1101/421883 ◽

2018 ◽

Author(s):

Matthieu Gilson ◽

Nikos E. Kouvaris ◽

Gustavo Deco ◽

Jean-François Mangin ◽

Cyril Poupon ◽

...

Keyword(s):

Network Analysis ◽

Network Theory ◽

Random Network ◽

Effective Connectivity ◽

Structural Connectivity ◽

Real Data ◽

Brain Regions ◽

Network Activity ◽

Whole Brain ◽

The Brain

AbstractNeuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often “static” despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

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