Biology subjects

Waltz, J.

Publications and source records attributed to Waltz, J..

3 recordsLinked to original sources

bioRxiv2020

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age ({Delta}age) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on {Delta}age and whether this effect was moderated by chronological age using the regression spline model. The results showed that {Delta}age was significantly elevated in the age >30 group in patients (p < 0.001) but not in age [<=] 30 group (p = 0.364). {Delta}age in patients was significantly and negatively associated with both working memory ({beta} = -0.176, p = 0.007) and processing speed ({beta} = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the {Delta}age is elevated in SZs and become significantly from middle life stage; the increase of {Delta}age in SZs is associated with the decline neurocognitive performance.

neuroscience↗

bioRxiv2020

Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery

Group-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. However, extracting disease-related subnetworks from the whole brain connectome has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data is often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood-based adaptive dense subgraph discovery (ADSD) model to extract disease-related subgraphs from the group-level whole brain connectome data. Our method is robust to both false positive and false negative errors of edge-wise inference and thus can lead to a more accurate discovery of latent disease-related connectomic subnetworks. We develop computationally efficient algorithms to implement the novel ADSD objective function and derive theoretical results to guarantee the convergence properties. We apply the proposed approach to a brain fMRI study for schizophrenia research and identify well-organized and biologically meaningful subnetworks that exhibit schizophrenia-related salience network centered connectivity abnormality. Analysis of synthetic data also demonstrates the superior performance of the ADSD method for latent subnetwork detection in comparison with existing methods in various settings.

neuroscience↗

bioRxiv2020

Bayes Estimate of Primary Threshold in Cluster-wise fMRI Inferences

Cluster-wise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Cluster-wise statistical inference consists of two steps: i) primary thresholding that excludes less significant voxels by a pre-specified cut-off (e.g., p < 0.001); and ii) cluster-wise thresholding that controls the family-wise error rate (FWER) caused by clusters consisting of false positive suprathreshold voxels. It has been well known that the selection of the primary threshold is critical because it determines both statistical power and false discovery rate. However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (e.g., p < 0.001) without taking into account the information in the data. In this manuscript, we propose a data-driven approach to objectively select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and an fMRI data example. The results show that our method can effectively increase statistical power while effectively controlling the false discovery rate.

neuroscience↗