This article proposes a framework for understanding the macro-scale organization of anatomical pathways in the mammalian brain. The architecture supports flexible behavioral decisions across a ...spectrum of spatio-temporal scales. The proposal emphasizes the combinatorial, reciprocal, and reentrant connectivity-called CRR neuroarchitecture-between cortical, BG, thalamic, amygdala, hypothalamic, and brainstem circuits. Thalamic nuclei, especially midline/intralaminar nuclei, are proposed to act as hubs routing the flow of signals between noncortical areas and pFC. The hypothalamus also participates in multiregion circuits via its connections with cortex and thalamus. At slower timescales, long-range behaviors integrate signals across levels of the neuroaxis. At fast timescales, parallel engagement of pathways allows urgent behaviors while retaining flexibility. Overall, the proposed architecture enables context-dependent, adaptive behaviors spanning proximate to distant spatio-temporal scales. The framework promotes an integrative perspective and a distributed, heterarchical view of brain function.
This work presents the experimental assessment of a 20 mL batch reactor's efficacy in converting plastic and oil residues into biofuels. The reactor, designed for ease of use, is heated using a ...metallic system. The experiments explore plastic solubilization at various temperatures and residence times, employing a mixture of distilled water and ethylene glycol as the solvent. Initial findings reveal that plastic solubilization requires a temperature of 350 °C with an ethylene glycol mole fraction of 0.35, whereas 250 °C suffices with a mole fraction of 0.58. Additionally, the study includes a process simulation of a plant utilizing a double fluidized bed gasifier and an economic evaluation of the interesterification/pyrolysis plant. Simulation results support project feasibility, estimating a total investment cost of approximately $12.99 million and annual operating expenses of around $17.98 million, with a projected payback period of about 5 years.
If the amygdala is involved in shaping perceptual experience when affectively significant visual items are encountered, responses in this structure should be correlated with both visual cortex ...responses and behavioral reports. Here, we investigated how affective significance shapes visual perception during an attentional blink paradigm combined with aversive conditioning. Behaviorally, following aversive learning, affectively significant scenes (CS⁺) were better detected than neutral (CS⁻) ones. In terms of mean brain responses, both amygdala and visual cortical responses were stronger during CS⁺ relative to CS⁻ trials. Increased brain responses in these regions were associated with improved behavioral performance across participants and followed a mediationlike pattern. Importantly, the mediation pattern was observed in a trial-by-trial analysis, revealing that the specific pattern of trialby-trial variability in brain responses was closely related to singletrial behavioral performance. Furthermore, the influence of the amygdala on visual cortical responses was consistent with a mediation, although partial, via frontal brain regions. Our results thus suggest that affective significance potentially determines the fate of a visual item during competitive interactions by enhancing sensory processing through both direct and indirect paths. In so doing, the amygdala helps separate the significant from the mundane.
Neuroimaging faces the daunting challenge of multiple testing – an instance of multiplicity – that is associated with two other issues to some extent: low inference efficiency and poor ...reproducibility. Typically, the same statistical model is applied to each spatial unit independently in the approach of massively univariate modeling. In dealing with multiplicity, the general strategy employed in the field is the same regardless of the specifics: trust the local “unbiased” effect estimates while adjusting the extent of statistical evidence at the global level. However, in this approach, modeling efficiency is compromised because each spatial unit (e.g., voxel, region, matrix element) is treated as an isolated and independent entity during massively univariate modeling. In addition, the required step of multiple testing “correction” by taking into consideration spatial relatedness, or neighborhood leverage, can only partly recoup statistical efficiency, resulting in potentially excessive penalization as well as arbitrariness due to thresholding procedures. Moreover, the assigned statistical evidence at the global level heavily relies on the data space (whole brain or a small volume). The present paper reviews how Stein’s paradox (1956) motivates a Bayesian multilevel (BML) approach that, rather than fighting multiplicity, embraces it to our advantage through a global calibration process among spatial units. Global calibration is accomplished via a Gaussian distribution for the cross-region effects whose properties are not a priori specified, but a posteriori determined by the data at hand through the BML model. Our framework therefore incorporates multiplicity as integral to the modeling structure, not a separate correction step. By turning multiplicity into a strength, we aim to achieve five goals: 1) improve the model efficiency with a higher predictive accuracy, 2) control the errors of incorrect magnitude and incorrect sign, 3) validate each model relative to competing candidates, 4) reduce the reliance and sensitivity on the choice of data space, and 5) encourage full results reporting. Our modeling proposal reverberates with recent proposals to eliminate the dichotomization of statistical evidence (“significant” vs. “non-significant”), to improve the interpretability of study findings, as well as to promote reporting the full gamut of results (not only “significant” ones), thereby enhancing research transparency and reproducibility.
•BOLD response varies substantially across trials with no clear sequential patterns, but some extent of synchronization exists between contralateral regions.•Autocorrelation in the residuals of ...subject-level model varies across regions, tasks and subjects.•It is important to explicitly account for cross-trial variability to obtain more accurate effect estimation and to legitimize inference generalizability.•Cross-trial variability can be incorporated at the whole-brain voxel-wise level through a linear mixed-effects model with a crossed random-effects structure.•Region-based Bayesian multilevel modeling is conducive to handing multiplicity and specific data distribution at the trial level as well as full results reporting.
In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category (“face”), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.
Controllability over stressors has major impacts on brain and behavior. In humans, however, the effect of controllability on responses to stressors is poorly understood. Using functional magnetic ...resonance imaging (fMRI), we investigated how controllability altered responses to a shock-plus-sound stressor with a between-group yoked design, where participants in controllable and uncontrollable groups experienced matched stressor exposure. Employing Bayesian multilevel analysis at the level of regions of interest and voxels in the insula, and standard voxelwise analysis, we found that controllability decreased stressor-related responses across threat-related regions, notably in the bed nucleus of the stria terminalis and anterior insula. Posterior cingulate cortex, posterior insula, and possibly medial frontal gyrus showed increased responses during control over stressor. Our findings support the idea that the aversiveness of stressors is reduced when controllable, leading to decreased responses across key regions involved in anxiety-related processing, even at the level of the extended amygdala.
A comparative environmental impact analysis of the soybean biodiesel production by two different technologies was done in this study. The production routes evaluated were the alkali-catalyzed ...(catalyst: sodium hydroxide) methylic transesterification and the enzyme-catalyzed (catalyst: lipase) ethylic transesterification. In an early work, simulations of the biodiesel production processes with the software Aspen HYSYS, from AspenTech Inc., were carried out. Now, a life cycle assessment (LCA) of the entire biodiesel production chain is done. The inventories related to each production subsystem were developed based on the mass and energy balances obtained from the simulations and on literature information. The results clearly indicated the best environmental performance of ethanol over methanol and of the enzymatic technology over the traditional alkaline technology, but also demonstrated some bottlenecks that should be attacked in a seek for more sustainable solutions.
In The Cognitive-Emotional Brain (Pessoa 2013), I describe the many ways that emotion and cognition interact and are integrated in the brain. The book summarizes five areas of research that support ...this integrative view and makes four arguments to organize each area. (1) Based on rodent and human data, I propose that the amygdala's functions go beyond emotion as traditionally conceived. Furthermore, the processing of emotion-laden information is capacity limited, thus not independent of attention and awareness. (2) Cognitive-emotional interactions in the human prefrontal cortex (PFC) assume diverse forms and are not limited to mutual suppression. Particularly, the lateral PFC is a focal point for cognitive-emotional interactions. (3) Interactions between motivation and cognition can be seen across a range of perceptual and cognitive tasks. Motivation shapes behavior in specific ways--for example, by reducing response conflict or via selective effects on working memory. Traditional accounts, by contrast, typically describe motivation as a global activation independent of particular control demands. (4) Perception and cognition are directly influenced by information with affective or motivational content in powerful ways. A dual competition model outlines a framework for such interactions at the perceptual and executive levels. A specific neural architecture is proposed that embeds emotional and motivational signals into perception and cognition through multiple channels. (5) A network perspective should supplant the strategy of understanding the brain in terms of individual regions. More broadly, in a network view of brain architecture, "emotion" and "cognition" may be used as labels of certain behaviors, but will not map cleanly into compartmentalized pieces of the brain.
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