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 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 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 Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Simon Wein ◽  
Gustavo Deco ◽  
Ana Maria Tomé ◽  
Markus Goldhacker ◽  
Wilhelm M. Malloni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Short Survey ◽  
Functional Dynamics ◽  
Axonal Fiber

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

scholarly journals Brain Connectivity Affecting Gait Function after Unilateral Supratentorial Stroke

2021 ◽  
Vol 11 (7) ◽  
pp. 870
Author(s):  
Hyun-Ah Lee ◽  
Dae-Hyun Kim
Keyword(s):  
Diffusion Tensor Imaging ◽  
Corpus Callosum ◽  
Fractional Anisotropy ◽  
Motor Coordination ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Mean Diffusivity ◽  
Medial Lemniscus ◽  
Gait Function

Gait dysfunction is a leading cause of long-term disability after stroke. The mechanisms underlying recovery of gait function are unknown. We retrospectively evaluated the association between structural connectivity and gait function in 127 patients with unilateral supratentorial stroke (>1 month after stroke). All patients underwent T1-weighted, diffusion tensor imaging and functional ambulation categorization. Voxel-wise linear regression analyses of the images were conducted using fractional anisotropy, mean diffusivity, and mode of anisotropy mapping as dependent variables, while the functional ambulation category was used as an independent variable with age and days after stroke as covariates. The functional ambulation category was positively associated with increased fractional anisotropy in the lesioned cortico-ponto-cerebellar system, corona radiata of the non-lesioned corticospinal tract pathway, bilateral medial lemniscus in the brainstem, and the corpus callosum. The functional ambulation category was also positively associated with increased mode of anisotropy in the lesioned posterior corpus callosum. In conclusion, structural connectivity associated with motor coordination and feedback affects gait function after stroke. Diffusion tensor imaging for evaluating structural connectivity can help to predict gait recovery and target rehabilitation goals after stroke.

scholarly journals Mapping covariance in brain FDG uptake to structural connectivity

2021 ◽  
Author(s):  
Igor Yakushev ◽  
Isabelle Ripp ◽  
Min Wang ◽  
Alex Savio ◽  
Michael Schutte ◽  
...  
Keyword(s):  
White Matter ◽  
Fdg Pet ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Whole Brain ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Sparse Inverse Covariance Estimation ◽  
Structural Networks

Abstract Purpose Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks. Methods We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking. Results For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections. Conclusion Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.

scholarly journals Differential patterns of change in brain connectivity resulting from traumatic brain injury

2021 ◽  
Author(s):  
Johan Nakuci ◽  
Matthew McGuire ◽  
Ferdinand Schweser ◽  
David Poulsen ◽  
Sarah F Muldoon
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Clinical Outcomes ◽  
Structural Damage ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Global Network ◽  
Network Measures

Background: Traumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. Methods: We utilize the rat lateral fluid percussion injury (FPI) model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion Tensor Imaging (DTI) is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. Results: We find that 1) global network measures are not able to distinguish between healthy and injured animals; 2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and 3) injured animals can be divided into subgroups based on changes in network motifs, measures of local structural connectivity. Additionally, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes such as the risk of developing post-traumatic epilepsy. Discussion: These results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries but different pathological trajectories.

scholarly journals Reduced Brain Connectivity in Clinical and Dimensional Autism Phenotypes Beyond Familial Confounding – A Twin Study

2021 ◽  
Author(s):  
Janina Neufeld ◽  
Simon Maier ◽  
Mirian Revers ◽  
Marco Reisert ◽  
Ralf Kuja-Halkola ◽  
...  
Keyword(s):  
Visual Processing ◽  
Diffusion Tensor ◽  
Twin Pair ◽  
Quantitative Estimation ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Medium Size ◽  
Genes And Environment

Abstract BackgroundPrevious studies on brain connectivity in clinical and dimensional autism have largely focused on selective connections and yielded inconsistent results. This study aimed to overcome these limitations. Global fiber tracking allowed a more unbiased assessment of white matter connectivity and utilizing a within-twin pair design introduced implicit control for genetic and environmental factors shared by twins and allowed conclusions regarding their impact. MethodsThe study examined the within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism spectrum diagnoses and dimensional autistic traits in 85 twin pairs (n=170; 56% monozygotic; 25 individuals with autism spectrum diagnosis). Structural connectivity was estimated using diffusion tensor imaging and linear regression models were fit, adjusted for IQ, other neurodevelopmental and psychiatric conditions and multiple testing. ResultsOverall, both clinical and dimensional autism phenotypes were associated with localized reductions in structural connectivity, despite comprehensively controlling for possible confounders, including all factors shared by twins. Twins fulfilling autism spectrum diagnostic criteria showed decreased brainstem-cuneus connectivity compared to their co-twins without the diagnosis. Further, twins with higher autistic traits showed decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations pointed into the same direction in mono- and dizygotic sub-cohorts, but were only significant in dizygotic twins.LimitationsThe recruitment approach of selecting primarily twin pairs discordant for autistic traits prevented a quantitative estimation of genetic and environmental contributions to brain correlates of clinical and dimensional autism. Further, assessing twins and excluding individuals with an IQ below 75 limited the generalizability of the findings. The statistical power allowed detecting medium-size or larger effects of dimensional autism. Finally, due the relatively small number of twin pairs discordant for a clinical autism, the results for clinical autism need to be interpreted with caution.ConclusionsReduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while reduced connectivity in networks crucial for visual and especially face processing seem to be more associated with dimensional aspects of autism. The results further suggest that the observed associations were potentially influenced by both genes and environment.

scholarly journals Structural brain signature of cognitive decline in Parkinson’s disease: DTI-based evidence from the LANDSCAPE study

2019 ◽  
Vol 12 ◽  
pp. 175628641984344 ◽  
Author(s):  
Martin Gorges ◽  
Hans-Peter Müller ◽  
Inga Liepelt-Scarfone ◽  
Alexander Storch ◽  
Richard Dodel ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Corpus Callosum ◽  
Cognitive Decline ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Structural Data ◽  
The Body ◽  
Normal Cognitive Function

Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia. Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures. Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score. Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.

scholarly journals Insights into the Connectivity of the Human Brain Using DTI

2012 ◽  
Vol 1 (1) ◽  
pp. 78-91 ◽  
Author(s):  
S Kollias
Keyword(s):  
Human Brain ◽  
Molecular Diffusion ◽  
Diffusion Tensor ◽  
Wide Spectrum ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Nerve Tissue ◽  
Mammalian Brain ◽  
Fibre Direction

Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. DOI: http://dx.doi.org/10.3126/njr.v1i1.6330 Nepalese Journal of Radiology Vol.1(1): 78-91

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