Browsing by Subject "Computational model"

Now showing 1 - 4 of 4
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    Distinct functional alterations in SCN8A epilepsy mutant channels
    (The Physiological Society, 2020-01) Pan, Yanling; Cummins, Theodore R.; Biology, School of Science
    SCN8A is a novel causal gene for early infantile epileptic encephalopathy. It is well accepted that gain-of-function mutations in SCN8A underlie the disorder, but the remarkable heterogeneity of its clinical presentation and poor treatment response demand for better understanding of the disease mechanisms. Here, we characterize a new epilepsy-related SCN8A mutation, R850Q, in human Nav1.6. We show that it is a gain-of-function mutation, with a hyperpolarizing shift in voltage dependence of activation, a 2-fold increase of persistent current and a slowed decay of resurgent current. We systematically compare its biophysics with three other SCN8A epilepsy mutations, T767I, R1617Q and R1872Q, in the human Nav1.6 channel. Although all of these mutations are gain-of-function, the mutations affect different aspects of channel properties. One commonality we discovered is an alteration of resurgent current kinetics, but the mechanisms by which resurgent currents are augmented is not yet clear for all of the mutations. Computational simulations predict increased excitability of neurons carrying these mutations with differential enhancement by open channel block.
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    Improving the prospective prediction of a near-term suicide attempt in veterans at risk for suicide, using a go/no-go task
    (Cambridge University Press, 2023) Myers, Catherine E.; Dave, Chintan V.; Callahan, Michael; Chesin, Megan S.; Keilp, John G.; Beck, Kevin D.; Brenner, Lisa A.; Goodman, Marianne S.; Hazlett, Erin A.; Niculescu, Alexander B.; St. Hill, Lauren; Kline, Anna; Stanley, Barbara H.; Interian, Alejandro; Psychiatry, School of Medicine
    Background: Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide. Method: 136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences. Results: On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA. Conclusions: These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.
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    Impulsivity in rodents with a genetic predisposition for excessive alcohol consumption is associated with a lack of a prospective strategy
    (Springer, 2017-04) Linsenbardt, David N.; Smoker, Michael P.; Janetsian-Fritz, Sarine S.; Lapish, Christopher C.; Psychology, School of Science
    Increasing evidence supports the hypothesis that impulsive decision-making is a heritable risk factor for an alcohol use disorder (AUD). Clearly identifying a link between impulsivity and AUD risk, however, is complicated by the fact that both AUDs and impulsivity are heterogeneous constructs. Understanding the link between the two requires identifying the underlying cognitive factors that lead to impulsive choices. Rodent models have established that a family history of excessive drinking can lead to the expression of a transgenerational impulsive phenotype, suggesting heritable alterations in the decision-making process. In the present study, we explored the cognitive processes underlying impulsive choice in a validated, selectively bred rodent model of excessive drinking-the alcohol-preferring ("P") rat. Impulsivity was measured via delay discounting (DD), and P rats exhibited an impulsive phenotype as compared to their outbred foundation strain-Wistar rats. Steeper discounting in P rats was associated with a lack of a prospective behavioral strategy, which was observed in Wistar rats and was directly related to DD. To further explore the underlying cognitive factors mediating these observations, a drift diffusion model of DD was constructed. These simulations supported the hypothesis that prospective memory of the delayed reward guided choice decisions, slowed discounting, and optimized the fit of the model to the experimental data. Collectively, these data suggest that a deficit in forming or maintaining a prospective behavioral plan is a critical intermediary to delaying reward, and by extension, may underlie the inability to delay reward in those with increased AUD risk.
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    Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa
    (PNAS, 2014-12-16) Anyan, Morgen E.; Amiri, Aboutaleb; Harvey, Cameron W.; Tierra, Giordano; Morales-Soto, Nydia; Driscoll, Callan M.; Alber, Mark S.; Shrout, Joshua D.; Department of Medicine, IU School of Medicine
    Pseudomonas aeruginosa is a ubiquitous bacterium that survives in many environments, including as an acute and chronic pathogen in humans. Substantial evidence shows that P. aeruginosa behavior is affected by its motility, and appendages known as flagella and type IV pili (TFP) are known to confer such motility. The role these appendages play when not facilitating motility or attachment, however, is unclear. Here we discern a passive intercellular role of TFP during flagellar-mediated swarming of P. aeruginosa that does not require TFP extension or retraction. We studied swarming at the cellular level using a combination of laboratory experiments and computational simulations to explain the resultant patterns of cells imaged from in vitro swarms. Namely, we used a computational model to simulate swarming and to probe for individual cell behavior that cannot currently be otherwise measured. Our simulations showed that TFP of swarming P. aeruginosa should be distributed all over the cell and that TFP-TFP interactions between cells should be a dominant mechanism that promotes cell-cell interaction, limits lone cell movement, and slows swarm expansion. This predicted physical mechanism involving TFP was confirmed in vitro using pairwise mixtures of strains with and without TFP where cells without TFP separate from cells with TFP. While TFP slow swarm expansion, we show in vitro that TFP help alter collective motion to avoid toxic compounds such as the antibiotic carbenicillin. Thus, TFP physically affect P. aeruginosa swarming by actively promoting cell-cell association and directional collective motion within motile groups to aid their survival.