Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

Sources of Method Bias in Social Science Research and Recommendations on How to Control It

A neurotrophic model for stress-related mood disorders.

The psychology of extinction has been studied for decades. Approximately 10 years ago, however, there began a concerted effort to understand the neural circuits of extinction of fear conditioning, in both animals and humans. Progress during this period has been facilitated by a high degree of coordination between rodent and human researchers examining fear extinction. Here we review the major advances and highlight new approaches to understanding and exploiting fear extinction. Research in fear extinction could serve as a model for translational research in other areas of behavioral neuroscience.

Fear Extinction as a Model for Translational Neuroscience: Ten Years of Progress

This review summarizes the brain mechanisms controlling sleep and wakefulness. Wakefulness promoting systems cause low-voltage, fast activity in the electroencephalogram (EEG). Multiple interacting neurotransmitter systems in the brain stem, hypothalamus, and basal forebrain converge onto common effector systems in the thalamus and cortex. Sleep results from the inhibition of wake-promoting systems by homeostatic sleep factors such as adenosine and nitric oxide and GABAergic neurons in the preoptic area of the hypothalamus, resulting in large-amplitude, slow EEG oscillations. Local, activity-dependent factors modulate the amplitude and frequency of cortical slow oscillations. Non-rapid-eye-movement (NREM) sleep results in conservation of brain energy and facilitates memory consolidation through the modulation of synaptic weights. Rapid-eye-movement (REM) sleep results from the interaction of brain stem cholinergic, aminergic, and GABAergic neurons which control the activity of glutamatergic reticular formation neurons leading to REM sleep phenomena such as muscle atonia, REMs, dreaming, and cortical activation. Strong activation of limbic regions during REM sleep suggests a role in regulation of emotion. Genetic studies suggest that brain mechanisms controlling waking and NREM sleep are strongly conserved throughout evolution, underscoring their enormous importance for brain function. Sleep disruption interferes with the normal restorative functions of NREM and REM sleep, resulting in disruptions of breathing and cardiovascular function, changes in emotional reactivity, and cognitive impairments in attention, memory, and decision making.

/pdf/control-of-sleep-and-wakefulness-51vkuya5ho.pdf

Control of Sleep and Wakefulness

The last 10 years have witnessed a surge of interest for the mechanisms underlying the acquisition and extinction of classically conditioned fear responses. In part, this results from the realization that abnormalities in fear learning mechanisms likely participate in the development and/or maintenance of human anxiety disorders. The simplicity and robustness of this learning paradigm, coupled with the fact that the underlying circuitry is evolutionarily well conserved, make it an ideal model to study the basic biology of memory and identify genetic factors and neuronal systems that regulate the normal and pathological expressions of learned fear. Critical advances have been made in determining how modified neuronal functions upon fear acquisition become stabilized during fear memory consolidation and how these processes are controlled in the course of fear memory extinction. With these advances came the realization that activity in remote neuronal networks must be coordinated for these events to take place. In this paper, we review these mechanisms of coordinated network activity and the molecular cascades leading to enduring fear memory, and allowing for their extinction. We will focus on Pavlovian fear conditioning as a model and the amygdala as a key component for the acquisition and extinction of fear responses.

/pdf/plastic-synaptic-networks-of-the-amygdala-for-the-kdlqfz445j.pdf

Plastic Synaptic Networks of the Amygdala for the Acquisition, Expression, and Extinction of Conditioned Fear

Neuropeptide S: A Neuropeptide Promoting Arousal and Anxiolytic-like Effects

Neuropeptide S-mediated control of fear expression and extinction: role of intercalated GABAergic neurons in the amygdala.

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

Yan Ling Xu

Papers

Neuropeptide S: A Neuropeptide Promoting Arousal and Anxiolytic-like Effects

Neuropeptide S-mediated control of fear expression and extinction: role of intercalated GABAergic neurons in the amygdala.

[PM2.5 Source Apportionment Based on a Variety of New Receptor Models].