Biology subjects

Dzemidzic, M.

Publications and source records attributed to Dzemidzic, M..

3 recordsLinked to original sources

bioRxiv2024

Intra-Striatal Dopaminergic Inter-Subject Covariance in Social Drinkers and Nontreatment-Seeking Alcohol Use Disorder Participants

One of the neurobiological correlates of alcohol use disorder (AUD) is the disruption of striatal dopaminergic function. While regional differences in dopamine (DA) function have been well studied, inter-regional relationships (represented as inter-subject covariance) have not been investigated and may offer a novel avenue for understanding DA function. Positron emission tomography (PET) data with [11C]raclopride in 22 social drinking controls and 17 AUD participants were used to generate group-level striatal covariance (partial Pearson correlation) networks, which were compared edgewise, also comparing global network metrics and community structure. An exploratory analysis examined the impact of tobacco cigarette use status. Striatal covariance was validated in an independent publicly available [18F]fallypride PET sample of healthy volunteers. Striatal covariance of control participants from both datasets showed a clear bipartition of the network into two distinct communities, one in the anterior and another in the posterior striatum. This organization was disrupted in the AUD participant network, with significantly lower network metrics in AUD compared to the control network. Stratification by cigarette use suggests differential consequences on group covariance networks. This work demonstrates that network neuroscience can quantify group differences in striatal DA and that its inter-regional interactions offer new insight into the consequences of AUD.

neuroscience↗

bioRxiv2018

Connectivity-Informed Adaptive Regularization for Generalized Outcomes

One 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.

neuroscience↗

bioRxiv2017

Brain connectivity-informed regularization methods for regression

A 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.

neuroscience↗