ABSTRACT. Human neuroimaging datasets provide rich multi-scale spatiotemporal information about the state of the brain. Most current methods, such as spectral analysis, focus on a single facet of these datasets and do not take full advantage of the inherited spatiotemporal information. Here, we consider a multilayer cross-frequency functional connectivity analysis to capture the complex spatiotemporal features of neural datasets at multiple scales and show that such features could potentially provide a better description of the neural activity. We demonstrate the effectiveness of this approach by applying the proposed method to capture disruptions of multilayer brain networks in Alzheimer’s patients. More specifically, we compared the multi-scale features extracted from electroencephalogram (EEG) data with traditional features in a machine learning framework to distinguish Alzheimer’s patients from control subjects. Our results show that such multi-scale features improve prediction accuracy when compared to traditional feature extraction methods in EEG analysis.

ABSTRACT. Alzheimer’s disease is a neurological disorder characterized by functional and structural atrophy, leading to symptoms like memory loss and cognitive decline. This study seeks to analyze the disruptions of functional connectivity pathways within the brain caused by Alzheimer’s disease from the maximum flow perspective. More specifically, we computed the maximum flow pathways within the functional brain networks, and compared it between healthy controls and Alzheimer’s patients. Our results suggest that the Alzheimer’s patients utilize pathways related to the default mode network (DMN) more frequently and display significant alterations in the usage of paths connected to the sensorimotor network (SMN). The increased usage of DMN pathways might point to a compensation mechanism that facilitates interregional communications in Alzheimer’s patients.

ABSTRACT. The Izhikevich neuron model is a widely adopted computational neuron model that comprises a set of quadratic differential equations involving two variables. Consequently, obtaining a closed-form solution is unattainable, making it challenging to perform further rate-coding analysis. In this study, we establish a balanced background noise Izhikevich neuron model with periodic signal input. Treating the system of differential equations as a velocity vector field, we are able to compute the Hamiltonian energy function for this model. The interspike-interval firing rate function is then derived with the aid of the Hamiltonian function. Using the firing rate function, we propose a solution curve on the novel γ-γ′ phase plane for better understanding the timing when extreme values of the firing rate function occur. Additionally, we address a phase advance phenomenon that occurs between the sinusoidal current injection and the interspike-interval firing rate curve, attempting to provide a qualitative explanation for this phenomenon.

ABSTRACT. With recent advancements in single cell sequencing technologies, routine data analysis activities include identifying cell subtypes in tissues and understanding their relationships at the single cell level. While existing algorithms excel in distinguishing different cell types based on known markers, subtyping cells based on their functions remains to be a challenge. To address this limitation, we propose a new single cell subtyping method, called Abstract Functional Analysis (AFA), which incorporates a priori known context-specific biological processes into the analysis. The key premise of AFA is that interject-ing “some form of prior knowledge” (GO Biological Processes in our case) into the oth-erwise unbiased analysis is amenable to deriving, namely, biological function centric subtyping. We assessed our method on eight publicly available Alzheimer’s Disease re-lated single-cell mRNA datasets and demonstrated that AFA can subgroup cells based on their functional roles into subtypes such as disease associated microglia (DAM) and late-stage homeostatic microglia exhibiting DAM signature. Advantages of AFA include labeling subtypes based on functions and discovering additional biological processes en-riched within each identified subtype. AFA offers a new way of subgrouping and nam-ing cells thereby enhancing our understanding of cellular heterogeneity in a more intui-tive and useful way.

ABSTRACT. Tinnitus, or ringing in the ear, is estimated to affect worldwide over 740 million people, with 10-20% experiencing bothersome tinnitus. Currently the most successful treatment option is cognitive behavioral therapy, which does not treat tinnitus itself but instead helps the patient to live with it. As the underlying mechanisms are poorly understood, the development of more targeted therapies is difficult. One big challenge in developing a treatment or cure for tinnitus is the lack of an objective test. While different behavioral tests are used in animal models, they all have limitations are very time consuming and are controversial in the field. Developing an objective electrophysiological test would allow a for a faster throughput in therapy development, that is independent of subjective interpretation of behavioral responses. Tinnitus has been associated with an increased spontaneous activity in the central auditory system. A major hub in auditory processing is the inferior colliculus (IC). A subset of neurons in the IC shows a phenomenon in which they have an increased spontaneous firing rate and increased firing rates in response to sound stimuli following a long-duration sound (LDS). We tested if this phenomenon is altered in tinnitus, and if this effect can be measured both directly in the IC or via non-invasive surface recordings. We used a mouse model of noise-induced tinnitus to confirm the potential use of our LDS stimulus paradigm as an objective test for tinnitus. Surface recordings as well as recordings directly from the IC in sound-exposed mice show a difference in response to tone pips following an LDS depending on tinnitus status. This might reflect the reported increased spontaneous activity in tinnitus mice and is a promising basis to develop an objective electrophysiological test for tinnitus.

This work was funded by DOD/MEDCOM/CDMRP W81XWH-18-1-0135

ABSTRACT. Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from high throughput sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here, we rigorously compare the performance of 9 HLA callers on 652 RNA-seq samples across 6 datasets with molecularly defined gold standard. We find that OptiType has the highest accuracy at both low and high resolution with an accuracy above 99%, followed by arcasHLA and seq2HLA with accuracies above 96%. Despite OptiType’s high accuracy, it is only capable of Class I predictions, thereby limiting its application to clinical procedures like transplantation requiring Class II predictions. Furthermore, our findings reveal significant variation in accuracy across each HLA locus, with HLA-A exhibiting the highest accuracy and HLA-DRB1 exhibiting the lowest accuracy. We also find that class II genes are generally more challenging to impute than class I genes, with most typing algorithms capable of making Class I predictions to >97% accuracy whereas the best Class II tool predicts with 94.2% accuracy. Moreover, we identify notable differences in the computational resources necessary to run each tool. We find that the most computationally expensive tools are OptiType and HLA-HD which require 105 and 102 times greater RAM and CPU, respectively, than the least computationally expensive tools, seq2HLA and RNA2HLA. Furthermore, all tools have decreased accuracy for African samples with respect to European samples at four digit resolution. We conclude that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy, consistency, and computational expensiveness are yet to be developed.

ABSTRACT. It has recently been noticed that dense subgraphs of SARS- CoV-2 epistatic networks correspond to future unobserved variants of concern. This phenomenon can be interpreted as multiple correlated mu- tations occurring in a rapid succession, resulting in a new variant rela- tively distant from the current population. We refer to this phenomenon as an evolutionary jump and propose to use it for enhancing genetic al- gorithm. Evolutionary jumps were implemented using C-SNV algorithm which find cliques in the epistatic network. We have applied the ge- netic algorithm enhanced with evolutionary jumps (GA+EJ) to the 0-1 Knapsack Problem, and found that evolutionary jumps allow the ge- netic algorithm to escape local minima and find solutions closer to the optimum.

ABSTRACT. Mathematical modeling have greatly enhanced our understanding of biological processes and search of cures to various diseases. These models are created from observation through Data Mining. Observations contain information about reality and models are our perception of reality. Often there is a gap between reality and our perception. Data Assimilation is the process of bridging this gap by fusing data into the model- be it static/dynamic and deterministic/stochastic. This involves estimation of parameters, initial conditions and boundary conditions. It is well known that the assimilated model has a better predictive power than the model or data alone.