scholarly journals Connectivity-Informed Adaptive Regularization for Generalized Outcomes

2018 ◽  
Author(s):  
Damian Brzyski ◽  
Marta Karas ◽  
Beau Ances ◽  
Mario Dzemidzic ◽  
Joaquin Goni ◽  
...  
Keyword(s):  
Linear Regression ◽  
Brain Connectivity ◽  
Association Studies ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
External Information ◽  
Penalty Term ◽  
Generalized Linear Regression ◽  
The Brain ◽  
Structural Brain

AbstractOne of the challenging problems in the brain imaging research is a principled incorporation of information from different imaging modalities in association studies. Frequently, data from each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, griPEER (generalized ridgified Partially Empirical Eigenvectors for Regression) to estimate the association between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER provides a principled approach to use external information from the structural brain connectivity to improve the regression coefficient estimation. Our proposal incorporates a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. We address both theoretical and computational issues and show that our method is robust to the incomplete information about the structural brain connectivity. We also provide a significance testing procedure for performing inference on the estimated coefficients in this model. griPEER is evaluated in extensive simulation studies and it is applied in classification of the HIV+ and HIV- individuals.

scholarly journals Brain connectivity-informed regularization methods for regression

2017 ◽  
Author(s):  
Marta Karas ◽  
Damian Brzyski ◽  
Mario Dzemidzic ◽  
Joaquin Goni ◽  
David A. Kareken ◽  
...  
Keyword(s):  
Regularization Method ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Simulated Data ◽  
Penalized Regression ◽  
Laplacian Matrix ◽  
External Information ◽  
Penalty Term ◽  
Reduction Techniques

AbstractA challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to utilizing external information arising from the structural brain connectivity to inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. In the work presented, we address both theoretical and computational issues. The approach is illustrated using simulated data and compared with other penalized regression methods. Finally, we apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion tensor imaging (DTI) derived measure of structural connectivity.

scholarly journals Conductance-Based Structural Brain Connectivity in Aging and Dementia

2020 ◽  
Author(s):  
Aina Frau-Pascual ◽  
Jean Augustinack ◽  
Divya Varadarajan ◽  
Anastasia Yendiki ◽  
David H. Salat ◽  
...  
Keyword(s):  
Healthy Aging ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Relevant Information ◽  
Regional Effects ◽  
Structural Connections ◽  
Cortical Regions ◽  
The Brain ◽  
Structural Brain ◽  
Age Prediction

AbstractBackgroundStructural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer’s disease (AD) progression.MethodsIn this work, we used our recently proposed structural connectivity quantification measure derived from diffusion MRI, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the ADNI-2 and OASIS-3 datasets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these datasets to the HCP dataset, as a reference, and eventually validated externally on two cohorts of the EDSD database.ResultsOur analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among sub-cortical regions, and cingulate and temporal cortices.DiscussionResults indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anti-correlation in structural connections.Impact statementThis work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from dMRI, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and AD.

scholarly journals Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 871-890
Author(s):  
Arseny A. Sokolov ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Michael Erb ◽  
Philippe Ryvlin ◽  
...  
Keyword(s):  
White Matter ◽  
Dynamic Range ◽  
Diffusion Processes ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Brain Regions ◽  
High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

scholarly journals Differences in the major fiber-tracts of people with congenital and acquired blindness

2020 ◽  
Vol 2020 (11) ◽  
pp. 366-1-366-7
Author(s):  
Katherine E.M. Tregillus ◽  
Lora T. Likova
Keyword(s):  
Visual Loss ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Visual Deprivation ◽  
Visual Development ◽  
Superior Longitudinal Fasciculus ◽  
Brain Images ◽  
Fiber Tracts ◽  
Congenitally Blind ◽  
The Brain

In order to better understand how our visual system processes information, we must understand the underlying brain connectivity architecture, and how it can get reorganized under visual deprivation. The full extent to which visual development and visual loss affect connectivity is not well known. To investigate the effect of the onset of blindness on structural connectivity both at the whole-brain voxel-wise level and at the level of all major whitematter tracts, we applied two complementary Diffusion-Tension Imaging (DTI) methods, TBSS and AFQ. Diffusion-weighted brain images were collected from three groups of participants: congenitally blind (CB), acquired blind (AB), and fully sighted controls. The differences between these groups were evaluated on a voxel-wise scale with Tract-Based Spatial Statistics (TBSS) method, and on larger-scale with Automated Fiber Quantification (AFQ), a method that allows for between-group comparisons at the level of the major fiber tracts. TBSS revealed that both blind groups tended to have higher FA than sighted controls in the central structures of the brain. AFQ revealed that, where the three groups differed, congenitally blind participants tended to be more similar to sighted controls than to those participants who had acquired blindness later in life. These differences were specifically manifested in the left uncinated fasciculus, the right corticospinal fasciculus, and the left superior longitudinal fasciculus, areas broadly associated with a range of higher-level cognitive systems.

scholarly journals Genetic Influence underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts

2021 ◽  
Author(s):  
Shan Cong ◽  
Xiaohui Yao ◽  
Linhui Xie ◽  
Jingwen Yan ◽  
Li Shen ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Association Studies ◽  
Structural Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Middle Temporal Gyrus ◽  
Genome Wide Association Studies ◽  
Brain Connectome ◽  
Genome Wide ◽  
Changes Over Time

AbstractBackgroundHuman brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear.MethodsThis study analyzes diffusion weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies (GWAS) of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures.ResultsOur empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index (BMI) in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies.DiscussionThese imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.Impact StatementThe genetic architecture underlying brain connectivity, and whether this mechanism changes over time, remain largely unknown. To understand the inter-individual variability at different life stages, this study performed genome-wide association studies of brain network connectivity measures from two age-specific neuroimaging cohorts, and identified a common association between the minor allele (G) of rs7937515 and decreased network segregation measures of the left middle temporal gyrus. The mediation analysis further elucidated neurobiological pathway of brain connectivity mediators linking the genes FAM86C1/FOLR3 with body mass index. This study provided new insights into the genetic mechanism of inter-regional connectivity alteration in the brain.

scholarly journals Structurally constrained effective brain connectivity

2021 ◽  
Author(s):  
Alessandro Crimi
Keyword(s):  
Community Detection ◽  
Autoregressive Model ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Autism Spectrum ◽  
Case Control ◽  
Functional Dependencies ◽  
The Brain

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal.The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

2021 ◽  
pp. 2250029
Author(s):  
Geng Zhang ◽  
Qi Zhu ◽  
Jing Yang ◽  
Ruting Xu ◽  
Zhiqiang Zhang ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Medical Image Analysis ◽  
Brain Regions ◽  
Brain Network ◽  
Brain Diseases ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Structural Brain Network ◽  
The Brain ◽  
Structural Brain

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

scholarly journals Incorporating outlier information into diffusion MR tractogram filtering for robust structural brain connectivity and microstructural analyses

2021 ◽  
Author(s):  
Viljami Sairanen ◽  
Mario Ocampo-Pineda ◽  
Cristina Granziera ◽  
Simona Schiavi ◽  
Alessandro Daducci
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Drop Out ◽  
Severe Adverse Effect ◽  
Motion Artefacts ◽  
Process Data ◽  
Filtering Algorithms ◽  
Diffusion Weighted Images ◽  
Structural Brain

The white matter structures of the human brain can be represented via diffusion tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts that are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) filtering algorithm to adjust for signal drop-out artifacts due to subject motion present in diffusion-weighted images. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable to properly filter tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effect in the human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

scholarly journals Disorders of Consciousness Associated With COVID-19: A Prospective, Multimodal Study of Recovery and Brain Connectivity

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000013067
Author(s):  
David Fischer ◽  
Samuel B Snider ◽  
Megan E Barra ◽  
William R Sanders ◽  
Otto Rapalino ◽  
...  
Keyword(s):  
Brain Injury ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Disorders Of Consciousness ◽  
Healthy Controls ◽  
Post Discharge ◽  
Prospective Longitudinal Study ◽  
Link Type ◽  
Prospective Longitudinal ◽  
Structural Brain

Background and Objectives:In patients with severe coronavirus disease 2019 (COVID-19), disorders of consciousness (COVID-DoC) have emerged as a serious complication. The prognosis and pathophysiology of COVID-DoC remain unclear, complicating decisions about continuing life-sustaining treatment. We describe the natural history of COVID-DoC and investigate its associated brain connectivity profile.Methods:In a prospective, longitudinal study, we screened consecutive patients with COVID-19 at our institution. We enrolled critically ill adult patients with a DoC unexplained by sedation or structural brain injury, and who were planned to undergo a brain MRI. We performed resting state functional MRI and diffusion MRI to evaluate functional and structural connectivity, as compared to healthy controls and patients with DoC resulting from severe traumatic brain injury (TBI). We assessed the recovery of consciousness (command-following) and functional outcomes (Glasgow Outcome Scale Extended [GOSE] and the Disability Rating Scale [DRS]) at hospital discharge, three months post-discharge, and six months post-discharge. We also explored whether clinical variables were associated with recovery from COVID-DoC.Results:After screening 1,105 patients with COVID-19, we enrolled twelve with COVID-DoC. The median age was 63.5 years [interquartile range 55-76.3]. Excluding one who died shortly after enrollment, all of the remaining eleven patients recovered consciousness, after 0-25 days (median 7 [5-14.5]) following the cessation of continuous intravenous sedation. At discharge, all surviving patients remained dependent – median GOSE 3 [1-3], median DRS 23 [16-30]. However ultimately, except for two patients with severe polyneuropathy, all returned home with normal cognition and minimal disability – at three months, median GOSE 3 [3-3], median DRS 7 [5-13]; at six months, median GOSE 4 [4-5], median DRS 3 [3-5]. Ten patients with COVID-DoC underwent advanced neuroimaging; functional and structural brain connectivity in COVID-DoC was diminished compared to healthy controls, and structural connectivity was comparable to patients with severe TBI.Discussion:Patients who survived invariably recovered consciousness after COVID-DoC. Though disability was common following hospitalization, functional status improved over the ensuing months. While future research is necessary, these prospective findings inform the prognosis and pathophysiology of COVID-DoC.Trial Registration Information:Clinicaltrials.gov, NCT04476589, submitted 7/2020, first enrolled 7/20/2020, https://clinicaltrials.gov/ct2/show/NCT04476589

scholarly journals Quantifying structural connectivity in brain tumor patients

2021 ◽  
Author(s):  
Yiran Wei ◽  
Chao Li ◽  
Stephen John Price
Keyword(s):  
Brain Tumor ◽  
White Matter ◽  
Robust Estimation ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Mass Effect ◽  
Brain Regions ◽  
Brain Network ◽  
Tumor Patients ◽  
The Brain

AbstractBrain tumors are characterised by infiltration along the white matter tracts, posing significant challenges to precise treatment. Mounting evidence shows that an infiltrating tumor can interfere with the brain network diffusely. Therefore, quantifying structural connectivity has potential to identify tumor invasion and stratify patients more accurately. The tract-based statistics (TBSS) is widely used to measure the white matter integrity. This voxel-wise method, however, cannot directly quantify the connectivity of brain regions. Tractography is a fiber tracking approach, which has been widely used to quantify brain connectivity. However, the performance of tractography on the brain with tumors is complicated by the tumor mass effect. A robust method of quantifying the structural connectivity strength in brain tumor patients is still lacking.Here we propose a method which could provide robust estimation of tract strength for brain tumor patients. Specifically, we firstly construct an unbiased tract template in healthy subjects using tractography. The voxel projection of TBSS is employed to quantify the tract connectivity in patients, using the location of each tract fiber from the template. To further improve the standard TBSS, we propose an approach of iterative projection of tract voxels guided by the tract orientation measured using the voxel-wise eigenvectors. Compared to state-of-the-art tractography, our approach is more sensitive in reflecting more functional relevance. Further, the different extent of network disruption revealed by our approach correspond to the clinical prior knowledge of tumor histology. The proposed method could provide a robust estimation of the structural connectivity for brain tumor patients.

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