147 research outputs found
Eigenvalue spectral properties of sparse random matrices obeying Dale's law
Understanding the dynamics of large networks of neurons with heterogeneous
connectivity architectures is a complex physics problem that demands novel
mathematical techniques. Biological neural networks are inherently spatially
heterogeneous, making them difficult to mathematically model. Random recurrent
neural networks capture complex network connectivity structures and enable
mathematically tractability. Our paper generalises previous classical results
to sparse connectivity matrices which have distinct excitatory (E) or
inhibitory (I) neural populations. By investigating sparse networks we
construct our analysis to examine the impacts of all levels of network
sparseness, and discover a novel nonlinear interaction between the connectivity
matrix and resulting network dynamics, in both the balanced and unbalanced
cases. Specifically, we deduce new mathematical dependencies describing the
influence of sparsity and distinct E/I distributions on the distribution of
eigenvalues (eigenspectrum) of the networked Jacobian. Furthermore, we
illustrate that the previous classical results are special cases of the more
general results we have described here. Understanding the impacts of sparse
connectivities on network dynamics is of particular importance for both
theoretical neuroscience and mathematical physics as it pertains to the
structure-function relationship of networked systems and their dynamics. Our
results are an important step towards developing analysis techniques that are
essential to studying the impacts of larger scale network connectivity on
network function, and furthering our understanding of brain function and
dysfunction.Comment: 18 pages, 6 figure
Neural Field Models: A mathematical overview and unifying framework
Rhythmic electrical activity in the brain emerges from regular non-trivial
interactions between millions of neurons. Neurons are intricate cellular
structures that transmit excitatory (or inhibitory) signals to other neurons,
often non-locally, depending on the graded input from other neurons. Often this
requires extensive detail to model mathematically, which poses several issues
in modelling large systems beyond clusters of neurons, such as the whole brain.
Approaching large populations of neurons with interconnected constituent
single-neuron models results in an accumulation of exponentially many
complexities, rendering a realistic simulation that does not permit
mathematical tractability and obfuscates the primary interactions required for
emergent electrodynamical patterns in brain rhythms. A statistical mechanics
approach with non-local interactions may circumvent these issues while
maintaining mathematically tractability. Neural field theory is a
population-level approach to modelling large sections of neural tissue based on
these principles. Herein we provide a review of key stages of the history and
development of neural field theory and contemporary uses of this branch of
mathematical neuroscience. We elucidate a mathematical framework in which
neural field models can be derived, highlighting the many significant inherited
assumptions that exist in the current literature, so that their validity may be
considered in light of further developments in both mathematical and
experimental neuroscience.Comment: 55 pages, 10 figures, 2 table
Autoregressive models for biomedical signal processing
Autoregressive models are ubiquitous tools for the analysis of time series in
many domains such as computational neuroscience and biomedical engineering. In
these domains, data is, for example, collected from measurements of brain
activity. Crucially, this data is subject to measurement errors as well as
uncertainties in the underlying system model. As a result, standard signal
processing using autoregressive model estimators may be biased. We present a
framework for autoregressive modelling that incorporates these uncertainties
explicitly via an overparameterised loss function. To optimise this loss, we
derive an algorithm that alternates between state and parameter estimation. Our
work shows that the procedure is able to successfully denoise time series and
successfully reconstruct system parameters. This new paradigm can be used in a
multitude of applications in neuroscience such as brain-computer interface data
analysis and better understanding of brain dynamics in diseases such as
epilepsy
Path Signatures for Seizure Forecasting
Forecasting the state of a system from an observed time series is the subject
of research in many domains, such as computational neuroscience. Here, the
prediction of epileptic seizures from brain measurements is an unresolved
problem. There are neither complete models describing underlying brain
dynamics, nor do individual patients exhibit a single seizure onset pattern,
which complicates the development of a `one-size-fits-all' solution. Based on a
longitudinal patient data set, we address the automated discovery and
quantification of statistical features (biomarkers) that can be used to
forecast seizures in a patient-specific way. We use existing and novel feature
extraction algorithms, in particular the path signature, a recent development
in time series analysis. Of particular interest is how this set of complex,
nonlinear features performs compared to simpler, linear features on this task.
Our inference is based on statistical classification algorithms with in-built
subset selection to discern time series with and without an impending seizure
while selecting only a small number of relevant features. This study may be
seen as a step towards a generalisable pattern recognition pipeline for time
series in a broader context
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
Societal-level versus individual-level predictions of ethical behavior: a 48-society study of collectivism and individualism
Is the societal-level of analysis sufficient today to understand the values of those in the global workforce? Or are individual-level analyses more appropriate for assessing the influence of values on ethical behaviors across country workforces? Using multi-level analyses for a 48-society sample, we test the utility of both the societal-level and individual-level dimensions of collectivism and individualism values for predicting ethical behaviors of business professionals. Our values-based behavioral analysis indicates that values at the individual-level make a more significant contribution to explaining variance in ethical behaviors than do values at the societal-level. Implicitly, our findings question the soundness of using societal-level values measures. Implications for international business research are discussed
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