Hierarchical Multinomial Modeling Approaches: An Application to Prospective Memory and Working Memory.
Ontology highlight
ABSTRACT:
SUBMITTER: Arnold NR
PROVIDER: S-EPMC4544831 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Hierarchical Multinomial Modeling Approaches: An Application to Prospective Memory and Working Memory.
Arnold Nina R NR Bayen Ute J UJ Smith Rebekah E RE
Experimental psychology 20150101 3
Hierarchical extensions of multinomial processing tree (MPT) models have been developed to deal with heterogeneity in participants or items. In this study, the beta-MPT model ( J. B. Smith & Batchelder, 2010 ) and the latent-trait approach ( Klauer, 2010 ) were used to estimate individual model parameters for prospective and retrospective components of prospective memory (PM), which requires remembering to perform an action in the future. The data from two experiments investigating the relations ...[more]
Similar Datasets
Project description:Objective: Attention-deficit/hyperactivity disorder (ADHD) has been associated with large magnitude impairments in working memory, whereas short-term memory deficits, when detected, tend to be less pronounced. However, confidence in these findings is limited due to task impurity combined with methodological and statistical limitations of the current evidence base. Method: A well-characterized, clinically evaluated sample of 172 children ages 8-13 years (M = 10.30, SD = 1.42; 72 girls; 64% White/non-Hispanic) were administered multiple, counterbalanced working memory tests. Bifactor-(s-1) modeling was used to characterize the presence and magnitude of central executive working memory, phonological short-term memory, and visuospatial short-term memory deficits in pediatric ADHD. Results: ADHD status was associated with very large magnitude impairments in central executive working memory that are present in most pediatric cases (d = 1.63-2.03; 75%-81% impaired), and these deficits covaried with ADHD inattentive and hyperactive/impulsive symptom severity based on both parent and teacher report. There was also evidence for a unique, albeit significantly smaller, impairment in visuospatial short-term memory (d = 0.60; 38% impaired); however, visuospatial short-term memory abilities did not covary with ADHD symptom severity. There was no evidence linking ADHD with phonological short-term memory deficits across either the dimensional or categorical analyses. Conclusion: These findings provide strong evidence that ADHD is associated with marked central executive working memory deficits that covary with their behavioral symptom presentation across settings. In contrast, visuospatial short-term memory deficits, when present, are likely epiphenomenal, and the most parsimonious conclusion appears to be that phonological short-term memory is intact in pediatric ADHD. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
| S-EPMC7483636 | biostudies-literature
Project description:Current theories are divided as to whether prospective memory (PM) involves primarily sustained processes such as strategic monitoring, or transient processes such as the retrieval of intentions from memory when a relevant cue is encountered. The current study examined the neural correlates of PM using a functional magnetic resonance imaging design that allows for the decomposition of brain activity into sustained and transient components. Performance of the PM task was primarily associated with sustained responses in a network including anterior prefrontal cortex (lateral Brodmann area 10), and these responses were dissociable from sustained responses associated with active maintenance in working memory. Additionally, the sustained responses in anterior prefrontal cortex correlated with faster response times for prospective responses. Prospective cues also elicited selective transient activity in a region of interest along the right middle temporal gyrus. The results support the conclusion that both sustained and transient processes contribute to efficient PM and provide novel constraints on the functional role of anterior PFC in higher-order cognition.
| S-EPMC2665160 | biostudies-other
Project description:We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.
| S-EPMC8044545 | biostudies-literature
Project description:Although reduced working memory brain activation has been reported in several brain regions of cocaine-dependent subjects compared with controls, very little is known about whether there is altered connectivity of working memory pathways in cocaine dependence. This study addresses this issue by using functional magnetic resonance imaging-based stochastic dynamic causal modeling (DCM) analysis to study the effective connectivity of 19 cocaine-dependent subjects and 14 healthy controls while performing a working memory task. Stochastic DCM is an advanced method that has recently been implemented in SPM8 that can obtain improved estimates, relative to deterministic DCM, of hidden neuronal causes before convolution with the hemodynamic response. Thus, stochastic DCM may be less influenced by the confounding effects of variations in blood oxygen level-dependent response caused by disease or drugs. Based on the significant regional activation common to both groups and consistent with previous working memory activation studies, seven regions of interest were chosen as nodes for DCM analyses. Bayesian family level inference, Bayesian model selection analyses, and Bayesian model averaging (BMA) were conducted. BMA showed that the cocaine-dependent subjects had large differences compared with the control subjects in the strengths of prefrontal-striatal modulatory (B matrix) DCM parameters. These findings are consistent with altered cortical-striatal networks that may be related to reduced dopamine function in cocaine dependence. As far as we are aware, this is the first between-group DCM study using stochastic methodology.
| S-EPMC4440319 | biostudies-literature
Project description:Many cellular processes are governed by stochastic reaction events. These events do not necessarily occur in single steps of individual molecules, and, conversely, each birth or death of a macromolecule (e.g., protein) could involve several small reaction steps, creating a memory between individual events and thus leading to nonmarkovian reaction kinetics. Characterizing this kinetics is challenging. Here, we develop a systematic approach for a general reaction network with arbitrary intrinsic waiting-time distributions, which includes the stationary generalized chemical-master equation (sgCME), the stationary generalized Fokker-Planck equation, and the generalized linear-noise approximation. The first formulation converts a nonmarkovian issue into a markovian one by introducing effective transition rates (that explicitly decode the effect of molecular memory) for the reactions in an equivalent reaction network with the same substrates but without molecular memory. Nonmarkovian features of the reaction kinetics can be revealed by solving the sgCME. The latter 2 formulations can be used in the fast evaluation of fluctuations. These formulations can have broad applications, and, in particular, they may help us discover new biological knowledge underlying memory effects. When they are applied to generalized stochastic models of gene-expression regulation, we find that molecular memory is in effect equivalent to a feedback and can induce bimodality, fine-tune the expression noise, and induce switch.
| S-EPMC6876203 | biostudies-literature
Project description:Recent theories propose that the prefrontal cortex (PFC) is organized in a hierarchical fashion with more abstract, higher level information represented in anterior regions and more concrete, lower level information represented in posterior regions. This hierarchical organization affords flexible adjustments of action plans based on the context. Computational models suggest that such hierarchical organization in the PFC is achieved through interactions with the basal ganglia (BG) wherein the BG gate relevant contexts into the PFC. Here, we tested this proposal using functional magnetic resonance imaging (fMRI). Participants were scanned while updating working memory (WM) with 2 levels of hierarchical contexts. Consistent with PFC abstraction proposals, higher level context updates involved anterior portions of the PFC (BA 46), whereas lower level context updates involved posterior portions of the PFC (BA 6). Computational models were only partially supported as the BG were sensitive to higher, but not lower level context updates. The posterior parietal cortex (PPC) showed the opposite pattern. Analyses examining changes in functional connectivity confirmed dissociable roles of the anterior PFC-BG during higher level context updates and posterior PFC-PPC during lower level context updates. These results suggest that hierarchical contexts are organized by distinct frontal-striatal and frontal-parietal networks.
| S-EPMC3841420 | biostudies-literature
Project description:BackgroundRecently there has been an explosion in the availability of bacterial genomic sequences, making possible now an analysis of genomic signatures across more than 800 hundred different bacterial chromosomes, from a wide variety of environments.Using genomic signatures, we pair-wise compared 867 different genomic DNA sequences, taken from chromosomes and plasmids more than 100,000 base-pairs in length. Hierarchical clustering was performed on the outcome of the comparisons before a multinomial regression model was fitted. The regression model included the cluster groups as the response variable with AT content, phyla, growth temperature, selective pressure, habitat, sequence size, oxygen requirement and pathogenicity as predictors.ResultsMany significant factors were associated with the genomic signature, most notably AT content. Phyla was also an important factor, although considerably less so than AT content. Small improvements to the regression model, although significant, were also obtained by factors such as sequence size, habitat, growth temperature, selective pressure measured as oligonucleotide usage variance, and oxygen requirement.ConclusionThe statistics obtained using hierarchical clustering and multinomial regression analysis indicate that the genomic signature is shaped by many factors, and this may explain the varying ability to classify prokaryotic organisms below genus level.
| S-EPMC2770534 | biostudies-literature
Project description:In vitro modeling of human disease has recently become feasible with the adoption of induced pluripotent stem cell (iPSC) technology. Here, we established patient-derived iPSCs from an Li-Fraumeni Syndrome (LFS) family and investigated the role of mutant p53 in the development of osteosarcoma (OS). Several members of this family carried a heterozygous p53(G245D) mutation and presented with a broad spectrum of tumors including OS. Osteoblasts (OBs) differentiated from iPSC-derived mesenchymal stem cells (MSCs) recapitulated OS features including defective osteoblastic differentiation (OB differentiation) as well as tumorigenic ability. Systematic analyses revealed that the expression of genes enriched in LFS-derived OBs strongly correlated with decreased time to tumor recurrence and poor patient survival. In silico cytogenetic region enrichment analysis (CREA) demonstrated that LFS-derived OBs do not have genomic rearrangements and hence are a particularly valuable tool for elucidating early oncogenic events prior to the accumulation of secondary alterations. LFS OBs exhibited impaired upregulation of the imprinted gene H19 during osteogenesis. Restoration of H19 expression in LFS OBs facilitated osteogenic differentiation and repressed tumorigenic potential. By integrating human imprinted gene network (IGN) and functional genomic analyses, we found that H19-mediates suppression of LFS-associated OS through the IGN component DECORIN (DCN). Downregulation of DCN impairs H19-mediated osteogenic differentiation and tumor suppression. In summary, these findings demonstrate the feasibility of studying inherited human cancer syndromes with iPSCs and also provide molecular insights into the role of the IGN in p53 mutation-mediated tumorigenesis. mRNAseq profiling during mesenchymal stem cell differentiation to osteoblasts.
2015-04-10 | E-GEOD-58123 | biostudies-arrayexpress
Project description:Previous neuroimaging studies have shown that working memory load has marked effects on regional neural activation. However, the mechanism through which working memory load modulates brain connectivity is still unclear. In this study, this issue was addressed using dynamic causal modeling (DCM) based on functional magnetic resonance imaging (fMRI) data. Eighteen normal healthy subjects were scanned while they performed a working memory task with variable memory load, as parameterized by two levels of memory delay and three levels of digit load (number of digits presented in each visual stimulus). Eight regions of interest, i.e., bilateral middle frontal gyrus (MFG), anterior cingulate cortex (ACC), inferior frontal cortex (IFC), and posterior parietal cortex (PPC), were chosen for DCM analyses. Analysis of the behavioral data during the fMRI scan revealed that accuracy decreased as digit load increased. Bayesian inference on model structure indicated that a bilinear DCM in which memory delay was the driving input to bilateral PPC and in which digit load modulated several parieto-frontal connections was the optimal model. Analysis of model parameters showed that higher digit load enhanced connection from L PPC to L IFC, and lower digit load inhibited connection from R PPC to L ACC. These findings suggest that working memory load modulates brain connectivity in a parieto-frontal network, and may reflect altered neuronal processes, e.g., information processing or error monitoring, with the change in working memory load. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.
| S-EPMC3179779 | biostudies-literature
Project description:Novel adjuvant technologies have a key role in the development of next-generation vaccines, due to their capacity to modulate the duration, strength and quality of the immune response. The AS01 adjuvant is used in the malaria vaccine RTS,S/AS01 and in the licensed herpes-zoster vaccine (Shingrix) where the vaccine has proven its ability to generate protective responses with both robust humoral and T-cell responses. For many years, animal models have provided insights into adjuvant mode-of-action (MoA), generally through investigating individual genes or proteins. Furthermore, modeling and simulation techniques can be utilized to integrate a variety of different data types; ranging from serum biomarkers to large scale "omics" datasets. In this perspective we present a framework to create a holistic integration of pre-clinical datasets and immunological literature in order to develop an evidence-based hypothesis of AS01 adjuvant MoA, creating a unified view of multiple experiments. Furthermore, we highlight how holistic systems-knowledge can serve as a basis for the construction of models and simulations supporting exploration of key questions surrounding adjuvant MoA. Using the Systems-Biology-Graphical-Notation, a tool for graphical representation of biological processes, we have captured high-level cellular behaviors and interactions, and cytokine dynamics during the early immune response, which are substantiated by a series of diagrams detailing cellular dynamics. Through explicitly describing AS01 MoA we have built a consensus of understanding across multiple experiments, and so we present a framework to integrate modeling approaches into exploring adjuvant MoA, in order to guide experimental design, interpret results and inform rational design of vaccines.
| S-EPMC6751289 | biostudies-literature