scholarly journals Modelling cortical laminar connectivity in the macaque brain

2020 ◽  
Author(s):  
Ittai Shamir ◽  
Yaniv Assaf
Keyword(s):  
Visual Cortex ◽  
Grey Matter ◽  
Ex Vivo ◽  
Brain Mri ◽  
Structural Connectivity ◽  
Brain Network ◽  
Mri Imaging ◽  
Whole Brain ◽  
Hierarchical Processing ◽  
Cortical Laminar

AbstractIn 1991, Felleman and Van Essen published their seminal study regarding hierarchical processing in the primate cerebral cortex. Their work encompassed a widescale analysis of connections reported through tracing between 35 regions in the macaque visual cortex, extending from cortical regions to the laminar level. Since then, great strides have been made in the field of MRI neuroimaging of white matter connectivity, also known as structural connectomics, as well as grey matter laminar composition. In this work, we revisit laminar-level connectivity in the macaque brain using a whole-brain MRI-based approach. We use multi-modal ex-vivo MRI imaging of the macaque brain in both white and grey matter, which are then integrated via a simple model of laminar connectivity. This model uses a granularity-based approach to define a set of rules that expands cortical connections to the laminar level. The resulting whole-brain network of macaque cortical laminar connectivity is then validated in the visual cortex by comparison to results from the 1991 study by Felleman and Van Essen. By using an unbiased definition of the cortex that addresses its heterogenous laminar composition, we are able to explore a new avenue of structural connectivity on the laminar level.

scholarly journals Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Temporal Structure ◽  
Structural Connectivity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Network ◽  
Dynamical Analysis ◽  
Whole Brain

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.

scholarly journals Exploring the laminar connectome

2021 ◽  
Author(s):  
Ittai Shamir ◽  
Omri Tomer ◽  
Ronnie Krupnik ◽  
Yaniv Assaf
Keyword(s):  
White Matter ◽  
Grey Matter ◽  
Structural Description ◽  
Mri Imaging ◽  
Healthy Human ◽  
Cortical Grey Matter ◽  
Cortical Laminar ◽  
White Matter Connectivity ◽  
Human Brains

The human connectome is the complete structural description of the network of connections and elements that form the wiring diagram of the brain. Because of the current scarcity of information regarding laminar end points of white matter tracts inside cortical grey matter, tractography remains focused on cortical partitioning into regions, while ignoring radial partitioning into laminar components. To overcome this biased representation of the cortex as a single homogenous unit, we use a recent data-derived model of cortical laminar connectivity, which has been further explored and corroborated in the macaque brain by comparison to published studies. The model integrates multimodal MRI imaging datasets regarding both white matter connectivity and grey matter laminar composition into a laminar-level connectome. In this study we model the laminar connectome of healthy human brains (N=20) and explore them via a set of neurobiologically meaningful complex network measures. Our analysis demonstrates a subdivision of network hubs that appear in the standard connectome into each individual component of the laminar connectome, giving a fresh look into the role of laminar components in cortical connectivity and offering new prospects in the fields of both structural and functional connectivity.

scholarly journals Heritability of hierarchical structural brain network

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.

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

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.

scholarly journals Mapping migraine to a common brain network

Brain ◽  
2020 ◽  
Vol 143 (2) ◽  
pp. 541-553 ◽  
Author(s):  
Matthew J Burke ◽  
Juho Joutsa ◽  
Alexander L Cohen ◽  
Louis Soussand ◽  
Danielle Cooke ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Grey Matter ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Region Of Interest ◽  
Generalized Linear Mixed Model ◽  
Brain Network ◽  
Grey Matter Volume ◽  
Matter Volume ◽  
Network Mapping

Abstract Inconsistent findings from migraine neuroimaging studies have limited attempts to localize migraine symptomatology. Novel brain network mapping techniques offer a new approach for linking neuroimaging findings to a common neuroanatomical substrate and localizing therapeutic targets. In this study, we attempted to determine whether neuroanatomically heterogeneous neuroimaging findings of migraine localize to a common brain network. We used meta-analytic coordinates of decreased grey matter volume in migraineurs as seed regions to generate resting state functional connectivity network maps from a normative connectome (n = 1000). Network maps were overlapped to identify common regions of connectivity across all coordinates. Specificity of our findings was evaluated using a whole-brain Bayesian spatial generalized linear mixed model and a region of interest analysis with comparison groups of chronic pain and a neurologic control (Alzheimer’s disease). We found that all migraine coordinates (11/11, 100%) were negatively connected (t ≥ ±7, P < 10−6 family-wise error corrected for multiple comparisons) to a single location in left extrastriate visual cortex overlying dorsal V3 and V3A subregions. More than 90% of coordinates (10/11) were also positively connected with bilateral insula and negatively connected with the hypothalamus. Bayesian spatial generalized linear mixed model whole-brain analysis identified left V3/V3A as the area with the most specific connectivity to migraine coordinates compared to control coordinates (voxel-wise probability of ≥90%). Post hoc region of interest analyses further supported the specificity of this finding (ANOVA P = 0.02; pairwise t-tests P = 0.03 and P = 0.003, respectively). In conclusion, using coordinate-based network mapping, we show that regions of grey matter volume loss in migraineurs localize to a common brain network defined by connectivity to visual cortex V3/V3A, a region previously implicated in mechanisms of cortical spreading depression in migraine. Our findings help unify migraine neuroimaging literature and offer a migraine-specific target for neuromodulatory treatment.

scholarly journals Microstructural changes in the reward system are associated with post-stroke depression

2020 ◽  
Author(s):  
Lena KL Oestreich ◽  
Paul Wright ◽  
Michael J O’Sullivan
Keyword(s):  
Major Depressive Disorder ◽  
White Matter ◽  
Grey Matter ◽  
Reward System ◽  
Brain Network ◽  
Healthy Controls ◽  
Depression Severity ◽  
Major Depressive ◽  
Whole Brain ◽  
Post Stroke Depression

AbstractBackgroundStudies of lesion location have been unsuccessful in identifying simple mappings between single brain regions and post-stroke depression (PSD). This might partly reflect the involvement of multiple interconnected regions in the regulation of mood. In this study, we set out to investigate whole-brain network structure and white matter connectivity in the genesis of PSD. Based on studies implicating regions of the reward system in major depressive disorder without stroke, we investigated the overlap of whole-brain correlates of PSD with this system and performed a focused analysis of grey matter and white matter projections within the reward system and their associations with the development of PSD.MethodsThe study enrolled 46 patients with first ischemic stroke, 12 were found to have PSD (D+ group) and 34 were free of PSD (D-) based on scores on the Geriatric Depression Scale. A group of 16 healthy controls were also recruited. Participants underwent research MRI with 3T structural and diffusion sequences. Graph theoretical measures derived from measures of microstructure were used to examine global topology and whole-brain connectome analyses were employed to assess differences in the interregional connectivity matrix between the three groups. Structural correlates specific to the reward system were examined by measuring grey matter volumes from regions in this circuit and by reconstructing its main white matter pathways, namely the medial forebrain bundle and connections within the cingulum bundle with deterministic tractography. For network connections and tracts, we derived measures of microstructural organization (FA), and also extracellular free-water content (FW) as a possible proxy of neuroinflammation.ResultsThe topology of structural networks differed across the three groups. Network modularity, weighted by extracellular FW content, increased with depression severity and connectome analysis identified networks of decreased FA-weighted and increased FW-weighted connectivity in patients with PSD relative to healthy controls. Intrinsic frontal and fronto-subcortical connections were a notable feature of these networks, which also subsumed the majority of regions defined as constituting the reward system. Within the reward system, grey matter volume of cortical and subcortical regions, as well as FA and FW of major connection pathways, were collectively predictive of PSD severity, explaining 76.8% of the variance in depression severity.ConclusionsTaken together, these findings indicate that PSD is associated with microstructural characteristics of the reward system, similar to those observed in major depressive disorder without stroke. Alterations in the reward system appear to drive differences in whole-brain network structure found in patients with PSD. Even in the absence of a simple relationship with lesion size and location, neuroimaging measures can explain much of the variance in depression scores. Structural characterization of the reward system is a promising biomarker of vulnerability to depression after stroke.

scholarly journals 3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI

2021 ◽  
Vol 5 (2) ◽  
pp. 200
Author(s):  
Bijen Khagi ◽  
Goo-Rak Kwon
Keyword(s):  
Grey Matter ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Statistical Parameter ◽  
Matter Content ◽  
Grey Matter Volume ◽  
Whole Brain ◽  
Mapping Tool ◽  
Positron Emission ◽  
A Minor

A recent study from MRI has revealed that there is a minor increase in cerebral-spinal fluid (CSF) content in brain ventricles and sulci, along with a substantial decrease in grey matter (GM) content and brain volume among Alzheimer's disease (AD) patients. It has been discovered that the grey matter volume shrinkage may indicate the possible case of dementia and related diseases like AD. Clinicians and radiologists use imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scan, and Positron Emission Tomography (PET) to diagnose and visualize the tissue contents of the brain. Using the whole brain MRI as the feature is an on-going approach among machine learning researchers, however, we are interested only in grey matter content. First, we segment the MRI using the SPM (Statistical parameter mapping) tool and then apply the smoothing technique to get a 3D image of grey matter (later called as grey version) from each MRI. This image file is then fed into 3D convolutional neural network (CNN) with necessary pre-processing so that it can train the network, to produce a classifying model. Once trained, an untested MRI (i.e. its grey version) can be passed through the CNN to determine whether it is a healthy control (HC), or Mild Cognitive Impairment (MCI) due to AD (mAD) or AD dementia (ADD). Our validation and testing accuracy are reported here and compared with normal MRI and its grey version.

Segmentation and clustering in brain MRI imaging

2018 ◽  
Vol 30 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Grey Matter ◽  
State Of The Art ◽  
Brain Mri ◽  
Brain Regions ◽  
Mri Imaging ◽  
Resonance Imaging ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Different Parts

AbstractClustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

scholarly journals Dynamic properties of simulated brain network models and empirical resting-state data

2019 ◽  
Vol 3 (2) ◽  
pp. 405-426 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Whole Brain

Brain network models (BNMs) have become a promising theoretical framework for 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 obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. 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 explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions 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.

scholarly journals How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

2021 ◽  
Author(s):  
Priyanka Chakraborty ◽  
Shubham Kumar ◽  
Amit Naskar ◽  
Arpan Banerjee ◽  
Dipanjan Roy
Keyword(s):  
Time Scales ◽  
Large Scale ◽  
Network Dynamics ◽  
Structural Connectivity ◽  
Brain Network ◽  
Conduction Delay ◽  
Whole Brain ◽  
Network Measures ◽  
Network Measure ◽  
Working Point

Both healthy and pathological aging exhibits gradual deterioration of structure but interestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically long-range anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

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