Innate social behaviors, such as mating and fighting, are fundamental to animal reproduction and survival. However, social engagements can also put an individual at risk. Little is known about the neural mechanisms that allow for appropriate risk assessment and the suppression of hazardous social interactions. We have identified the posteromedial nucleus of the cortical amygdala (COApm) as a locus required for the suppression of male mating when a female is sick. Using anatomical tracing, functional imaging, and circuit-level epistatic analyses, we show that suppression of mating with an unhealthy female is mediated by the COApm projections onto the glutamatergic population of the medial amygdalar nucleus (MEA). We further show that the role of the COApm to MEA connection in regulating male mating behavior relies on the neuromodulator thyrotropin-releasing hormone (TRH). TRH is expressed in the COApm while
Transcranial static magnetic field stimulation (tSMS) is a novel non-invasive brain stimulation technique that has been shown to locally increase alpha power in the parietal and occipital cortex. We investigated if tSMS locally increased alpha power in the left or right prefrontal cortex, as the balance of left/right prefrontal alpha power (frontal alpha asymmetry) has been linked to emotional processing and mood disorders. Therefore, altering frontal alpha asymmetry with tSMS may serve as a novel treatment to psychiatric diseases. We performed a crossover, double-blind, sham-controlled pilot study to assess the effects of prefrontal tSMS on neural oscillations. Twenty-four right-handed healthy participants were recruited and received left dorsolateral prefrontal cortex (DLPFC) tSMS, right DLPFC tSMS, and sham tSMS in a randomized order. Electroencephalography data were collected before (2 minutes eyes-closed, 2 minutes eyes-open), during (10 minutes eyes-open), and after (2 minutes eyes-open) stimulation. In contrast to our hypothesis, neither left nor right tSMS locally increased frontal alpha power. However, alpha power increased in occipital cortex during left DLPFC tSMS. Right DLPFC tSMS increased post-stimulation fronto-parietal theta power, indicating possible relevance to memory and cognition. Left and right DLPFC tSMS increased post-stimulation left hemisphere beta power, indicating possible changes to motor behavior. Left DLPFC tSMS also increased post-stimulation right frontal beta power, demonstrating complex network effects that may be relevant to aggressive behavior. We concluded that DLPFC tSMS modulated the network oscillations in regions distant from the location of stimulation and that tSMS has region specific effects on neural oscillations.
Adaptive information processing, comprised of local computations and their efficient routing, is crucial for flexible brain function. Spatial attention is a quintessential example of this adaptive process. It is critical for recognizing and interacting with behaviorally relevant objects in a cluttered environment. Object recognition is mediated by ensembles of computational units distributed across the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Based on pairwise correlation analysis, two key mechanisms have been proposed: First is an improvement in the efficacy of unique information directed from one encoding stage to another, suggested by evidence along the visual hierarchy. Based on the theoretical results that even weak correlated variability can substantially limit the encoding capacity of a neuronal pool, a second proposal is an improvement in the sensory information capacity of an encoding stage through a reduction in shared fluctuations. However, pairwise analyses capture both unique and shared components of fluctuations, and therefore cannot disambiguate the proposed mechanisms. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention. Using network-based statistical modeling, we estimated the strength of inter-layer information flow by measuring statistical dependencies that reflect how the cortical layers uniquely drive each other's neural activity. We quantified their modulation across attention conditions (attend-in vs. attend-away) in a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework, we estimated modulation of shared dependencies and found that they are reduced within laminar populations, specifically the putative excitatory subpopulations. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed across behavioral outcomes (hit vs. miss) that are thought to be mediated by endogenous state fluctuations. By "decomposing" the modulation of dependency components and in combination with prior theoretical work, our results suggest the following computational model of optimal sensory states that are attained by either task demands or endogenous fluctuations in brain state: enhanced information flow between and improved information capacity within encoding stages.
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