Fear generalization is a hallmark of anxiety disorders. Experimentally, fear generalization can be difficult to dissociate from its counterpart, fear discrimination. Here, we use minimal threat learning procedures to reveal such a dissociation. We show that in Long–Evans rats, an auditory threat cue predicting footshock on 10% of trials produces a discriminated fear response that does not generalize to a neutral auditory cue. In contrast, even slightly higher footshock probabilities (30 and 20%) produce fear generalization. AAV-mediated, caspase-3 deletion of ventral pallidum neurons abolishes fear generalization and reduces threat cue responding during extinction. The ventral pallidum's contribution to fear generalization and extinction threat responding does not depend on inputs from the nucleus accumbens. The results demonstrate a minimal threat learning approach to dissociate fear discrimination from fear generalization and a novel role for the ventral pallidum in generalizing and expressing fear.

Relationships among membrane currents allow central pattern generator (CPG) neurons to reliably drive motor programs. We hypothesize that continually active CPG neurons utilize activity-dependent feedback to correlate expression of ion channel genes to balance essential membrane currents. However, episodically activated neurons experience absences of activity-dependent feedback and, thus, presumably employ other strategies to coregulate the balance of ionic currents necessary to generate appropriate output after periods of quiescence. To investigate this, we compared continually active pyloric dilator (PD) neurons with episodically active lateral gastric (LG) CPG neurons of the stomatogastric ganglion (STG) in male Cancer borealis crabs. After experimentally activating LG for 8 h, we measured three potassium currents and abundances of their corresponding channel mRNAs. We found that ionic current relationships were correlated in LG's silent state, but ion channel mRNA relationships were correlated in the a...

Age-related brain changes affect sleep and are reflected in properties of sleep slow-waves, however, the precise mechanisms behind these changes are still not completely understood. Here, we adapt a previously established whole-brain model relating structural connectivity changes to resting state dynamics, and extend it to a slow-wave sleep brain state. In particular, starting from a representative connectome at the beginning of the aging trajectory, we have gradually reduced the inter-hemispheric connections, and simulated sleep-like slow-wave activity. We show that the main empirically observed trends, namely a decrease in duration and increase in variability of the slow waves are captured by the model. Furthermore, comparing the simulated EEG activity to the source signals, we suggest that the empirically observed decrease in amplitude of the slow waves is caused by the decrease in synchrony between brain regions.

This study aims to elucidate the methodology and compare the accuracy of different blood biomarkers for diagnosing ischemic stroke (IS). We reviewed 29 articles retrieved from PubMed, MEDLINE, Web of Science, and CINAHL Plus with Full Text. Among these, 23 articles involving 3,494 participants were suitable for meta-analysis. The pooled area under the curve (AUC) of all studies for meta-analysis was 0.89. The pooled sensitivity and specificity were 0.76 (0.74–0.78) and 0.84 (0.83–0.86), respectively. Blood biomarkers from noninpatient settings demonstrated better diagnostic performance than those in inpatient settings (AUC 0.91 vs 0.88). Smaller sample sizes (<100) showed better performance than larger ones (≥100; AUC 0.92 vs 0.86). Blood biomarkers from acute IS (AIS) patients showed higher diagnostic values than those from IS and other stroke types (AUC 0.91 vs 0.87). The diagnostic performance of multiple blood biomarkers was superior to that of a single biomarker (AUC 0.91 vs 0.88). The diagnostic valu...

Here we test the stochastic dynamic operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered average (STA) or stimulus-triggered average techniques currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. In simulated data, SDO methods were more sensitive and specific than the STA for identifying state-dependent relationships. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike–signal relationships to be captured, analyzed, and interpreted visually and i...

In the article “Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision-Making,” by Samantha R. White, Michael W. Preston, Kyra Swanson, and Mark Laubach …

Impulsive individuals excessively discount the value of delayed rewards, and this is thought to reflect deficits in brain regions critical for impulse control such as the anterior cingulate cortex (ACC). Delay discounting (DD) is an established measure of cognitive impulsivity, referring to the devaluation of rewards delayed in time. This study used male Wistar rats performing a DD task to test the hypothesis that neural activity states in ACC ensembles encode strategies that guide decision-making. Optogenetic silencing of ACC neurons exclusively increased impulsive choices at the 8 s delay by increasing the number of consecutive low-value, immediate choices. In contrast to shorter delays where animals preferred the delay option, no immediate or delay preference was detected at 8 s. These data suggest that ACC was critical for decisions requiring more deliberation between choice options. To address the role of ACC in this process, large-scale multiple single-unit recordings were performed and revealed that...

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7–14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time—expressed as different gamma band bursts (>30 Hz)—along the alpha phase. Since the processing capacity per alpha cycle is...

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that modulates brain activity by inducing electric fields in the brain. Real-time, state-dependent stimulation with TMS has shown that neural oscillation phase modulates corticospinal excitability. However, such motor evoked potentials (MEPs) only indirectly reflect motor cortex activation and are unavailable at other brain regions of interest. The direct and secondary cortical effects of phase-dependent brain stimulation remain an open question. In this study, we recorded the cortical responses during single-pulse TMS using electroencephalography (EEG) concurrently with the MEP measurements in 20 healthy human volunteers (11 female). TMS was delivered at peak, rising, trough, and falling phases of mu (8–13 Hz) and beta (14–30 Hz) oscillations in the motor cortex. The cortical responses were quantified through TMS evoked potential components N15, P50, and N100 as peak-to-peak amplitudes (P50-N15 and P50-N100). We further anal...

The cortico-basal ganglia loop has traditionally been conceptualized as consisting of three distinct information networks: motor, limbic, and associative. However, this three-loop concept is insufficient to comprehensively explain the diverse functions of the cortico-basal ganglia system, as emerging evidence suggests its involvement in sensory processing, including the auditory systems. In the present study, we demonstrate the auditory cortico-basal ganglia loop by using transgenic mice and viral-assisted labelings. The caudal part of the external globus pallidus (GPe) emerged as a major output nucleus of the auditory cortico-basal ganglia loop with the cortico-striato-pallidal projections as its input pathway and pallido-cortical and pallido–thalamo–cortical projections as its output pathway. GABAergic neurons in the caudal GPe dominantly innervated the nonlemniscal auditory pathway. They also projected to various regions, including the substantia nigra pars lateralis, cuneiform nucleus, and periaqueduct...