Dissociating representations of affect and motion in visual cortices.

While a delicious dessert being presented to us may elicit strong feelings of happiness and excitement, the same treat falling slowly away can lead to sadness and disappointment. Our emotional response to the item depends on its visual motion direction. Despite this importance, it remains unclear whether (and how) cortical areas devoted to decoding motion direction represents or integrates emotion with perceived motion direction. Motion-selective visual area V5/MT+ sits, both functionally and anatomically, at the nexus of dorsal and ventral visual streams. These pathways, however, differ in how they are modulated by emotional cues. The current study was designed to disentangle how emotion and motion perception interact, as well as use emotion-dependent modulation of visual cortices to understand the relation of V5/MT+ to canonical processing streams. During functional magnetic resonance imaging (fMRI), approaching, receding, or static motion after-effects (MAEs) were induced on stationary positive, negative, and neutral stimuli. An independent localizer scan was conducted to identify the visual-motion area V5/MT+. Through univariate and multivariate analyses, we demonstrated that emotion representations in V5/MT+ share a more similar response profile to that observed in ventral visual than dorsal, visual structures. Specifically, V5/MT+ and ventral structures were sensitive to the emotional content of visual stimuli, whereas dorsal visual structures were not. Overall, this work highlights the critical role of V5/MT+ in the representation and processing of visually acquired emotional content. It further suggests a role for this region in utilizing affectively salient visual information to augment motion perception of biologically relevant stimuli.

Gender Impacts the Relationship between Mood Disorder Symptoms and Effortful Avoidance Performance.

We must often decide how much effort to exert or withhold to avoid undesirable outcomes or obtain rewards. In depression and anxiety, levels of avoidance can be excessive and reward-seeking may be reduced. Yet outstanding questions remain about the links between motivated action/inhibition and anxiety and depression levels, and whether they differ between men and women. Here, we examined the relationship between anxiety and depression scores, and performance on effortful active and inhibitory avoidance (Study 1) and reward seeking (Study 2) in humans. Undergraduates and paid online workers ([Formula: see text] = 545, [Formula: see text] = 310; [Formula: see text] = 368, [Formula: see text] = 450, [Formula: see text] = 22.58, [Formula: see text] = 17-62) were assessed on the Beck Depression Inventory II (BDI) and the Beck Anxiety Inventory (BAI) and performed an instructed online avoidance or reward-seeking task. Participants had to make multiple presses on active trials and withhold presses on inhibitory trials to avoid an unpleasant sound (Study 1) or obtain points toward a monetary reward (Study 2). Overall, men deployed more effort than women in both avoidance and reward-seeking, and anxiety scores were negatively associated with active reward-seeking performance based on sensitivity scores. Gender interacted with anxiety scores and inhibitory avoidance performance, such that women with higher anxiety showed worse avoidance performance. Our results illuminate effects of gender in the relationship between anxiety and depression levels and the motivation to actively and effortfully respond to obtain positive and avoid negative outcomes.

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.

Isoflurane stress induces glucocorticoid production in mouse lymphoid organs.

Glucocorticoids (GCs) are secreted by the adrenal glands and locally produced by lymphoid organs. Adrenal GC secretion at baseline and in response to stressors is greatly reduced during the stress hyporesponsive period (SHRP) in neonatal mice (postnatal day (PND) 2-12). It is unknown whether lymphoid GC production increases in response to stressors during the SHRP. Here, we administered an acute stressor (isoflurane anesthesia) to mice before, during, and after the SHRP and measured systemic and local GCs via mass spectrometry. We administered isoflurane, vehicle control (oxygen), or neither (baseline) at PND 1, 5, 9, or 13 and measured progesterone and six GCs in blood, bone marrow, thymus, and spleen. At PND1, blood and lymphoid GC levels were high and did not respond to stress. At PND5, blood GC levels were very low and increased slightly after stress, while lymphoid GC levels were also low but increased greatly after stress. At PND9, blood and lymphoid GC levels were similar at baseline and increased similarly after stress. At PND13, blood GC levels were higher than lymphoid GC levels at baseline, and blood GC levels showed a greater response to stress. These data demonstrate the remarkable plasticity of GC physiology during the postnatal period, show that local steroid levels do not reflect systemic steroid levels, provide insight into the SHRP, and identify a potential mechanism by which early-life stressors can alter immunity in adulthood.

Steroid profiling of glucocorticoids in microdissected mouse brain across development.

Corticosterone is produced by the adrenal glands and also produced locally by other organs, such as the brain. Local levels of corticosterone in specific brain regions during development are not known. Here, we microdissected brain tissue and developed a novel liquid chromatography tandem mass spectrometry method (LC-MS/MS) to measure a panel of seven steroids (including 11-deoxycorticosterone (DOC), corticosterone, and 11-dehydrocorticosterone (DHC) in the blood, hippocampus (HPC), cerebral cortex (CC), and hypothalamus (HYP) of mice at postnatal day (PND) 5, 21, and 90. In a second cohort of mice, we measured the expression of three genes that code for steroidogenic enzymes that regulate corticosterone levels (Cyp11b1, Hsd11b1, and Hsd11b2) in the HPC, CC, and HYP. There were region-specific patterns of steroid levels across development, including higher corticosterone levels in the HPC and HYP than in the blood at PND5. In contrast, corticosterone levels were higher in the blood than in all brain regions at PND21 and PND90. Brain corticosterone levels were not positively correlated with blood corticosterone levels, and correlations across brain regions increased with age. Local corticosterone levels were best predicted by local DOC levels at PND5, but by local DHC levels at PND21 and PND90. Transcripts for the three enzymes were detectable in all samples (with highest expression of Hsd11b1) and showed region-specific changes with age. These data demonstrate that individual brain regions fine-tune local levels of corticosterone during early development and that coupling of glucocorticoid levels across regions increases with age.

Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.

Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut-a robust movement-tracking deep neural network framework-which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered "closed loop" brain-machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.