Different emotional states engage distinct descending pathways from the prefrontal cortex.

Emotion regulation, essential for adaptive behavior, depends on the brain's capacity to process a range of emotions. Current research has largely focused on individual emotional circuits without fully exploring how their interaction influences physiological responses or understanding the neural mechanisms that differentiate emotional valence. Using in vivo calcium imaging, electrophysiology, and optogenetics, we examined neural circuit dynamics in the medial prefrontal cortex (mPFC), targeting two key areas: the basal lateral amygdala (BLA) and nucleus accumbens (NAc). Our results demonstrate distinct activation patterns in the mPFC→BLA and mPFC→NAc pathways in response to social stimuli, indicating a mechanism for discriminating emotions: increased mPFC→BLA activity signals anxiety, while heightened mPFC→NAc responses are linked to exploration. Additionally, chronic emotional states amplify activity in these pathways-positivity enhances mPFC→NAc, while negativity boosts mPFC→BLA. This study sheds light on the nuanced neural circuitry involved in emotion regulation, revealing the pivotal roles of mPFC projections in emotional processing. Identifying these specific circuits engaged by varied emotional states advances our understanding of emotional regulation's biological underpinnings and highlights potential targets for addressing emotional dysregulation in psychiatric conditions. Significance statement: While existing circuitry studies have underscored the significance of emotional circuits, the majority of research has concentrated on individual circuits. The assessment of whether and how the balance among multiple circuits influences overall physiological outcomes is often overlooked. This study delves into the neural underpinnings of emotion regulation, focusing on how positive and negative valences are discriminated and managed. By examining the specific pathways from the medial prefrontal cortex (mPFC) to key emotional centers-the basal lateral amygdala (BLA) for negative valence and the nucleus accumbens (NAc) for positive one-we uncovered a novel dual-balanced neural circuit mechanism that enables this essential aspect of human cognition.

Pattern decorrelation in the mouse medial prefrontal cortex enables social preference and requires MeCP2.

Sociability is crucial for survival, whereas social avoidance is a feature of disorders such as Rett syndrome, which is caused by loss-of-function mutations in MECP2. To understand how a preference for social interactions is encoded, we used in vivo calcium imaging to compare medial prefrontal cortex (mPFC) activity in female wild-type and Mecp2-heterozygous mice during three-chamber tests. We found that mPFC pyramidal neurons in Mecp2-deficient mice are hypo-responsive to both social and nonsocial stimuli. Hypothesizing that this limited dynamic range restricts the circuit's ability to disambiguate coactivity patterns for different stimuli, we suppressed the mPFC in wild-type mice and found that this eliminated both pattern decorrelation and social preference. Conversely, stimulating the mPFC in MeCP2-deficient mice restored social preference, but only if it was sufficient to restore pattern decorrelation. A loss of social preference could thus indicate impaired pattern decorrelation rather than true social avoidance.

Motor training improves coordination and anxiety in symptomatic Mecp2-null mice despite impaired functional connectivity within the motor circuit.

Rett syndrome (RTT) is a severe neurodevelopmental disorder caused by loss of function of the X-linked methyl-CpG­binding protein 2 (MECP2). Several case studies report that gross motor function can be improved in children with RTT through treadmill walking, but whether the MeCP2-deficient motor circuit can support actual motor learning remains unclear. We used two-photon calcium imaging to simultaneously observe layer (L) 2/3 and L5a excitatory neuronal activity in the motor cortex (M1) while mice adapted to changing speeds on a computerized running wheel. Despite circuit hypoactivity and weakened functional connectivity across L2/3 and L5a, the Mecp2-null circuit's firing pattern evolved with improved performance over 2 weeks. Moreover, trained mice became less anxious and lived 20% longer than untrained mice. Because motor deficits and anxiety are core symptoms of RTT, which is not diagnosed until well after symptom onset, these results underscore the benefit of motor learning.

Erratum: Boolean Models of Biological Processes Explain Cascade-Like Behavior.

This corrects the article DOI: 10.1038/srep20067.

Boolean Models of Biological Processes Explain Cascade-Like Behavior.

Biological networks play a key role in determining biological function and therefore, an understanding of their structure and dynamics is of central interest in systems biology. In Boolean models of such networks, the status of each molecule is either "on" or "off" and along with the molecules interact with each other, their individual status changes from "on" to "off" or vice-versa and the system of molecules in the network collectively go through a sequence of changes in state. This sequence of changes is termed a biological process. In this paper, we examine the common perception that events in biomolecular networks occur sequentially, in a cascade-like manner, and ask whether this is likely to be an inherent property. In further investigations of the budding and fission yeast cell-cycle, we identify two generic dynamical rules. A Boolean system that complies with these rules will automatically have a certain robustness. By considering the biological requirements in robustness and designability, we show that those Boolean dynamical systems, compared to an arbitrary dynamical system, statistically present the characteristics of cascadeness and sequentiality, as observed in the budding and fission yeast cell- cycle. These results suggest that cascade-like behavior might be an intrinsic property of biological processes.

Network class superposition analyses.

Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., ≈ 10(30) for the yeast cell cycle process), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix T, which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for T derived from boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying T to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with T. We show how to generate Derrida plots based on T. We show that T-based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on T. We motivate all of these results in terms of a popular molecular biology boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for T, for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.

Process-driven inference of biological network structure: feasibility, minimality, and multiplicity.

A common problem in molecular biology is to use experimental data, such as microarray data, to infer knowledge about the structure of interactions between important molecules in subsystems of the cell. By approximating the state of each molecule as "on" or "off", it becomes possible to simplify the problem, and exploit the tools of boolean analysis for such inference. Amongst boolean techniques, the process-driven approach has shown promise in being able to identify putative network structures, as well as stability and modularity properties. This paper examines the process-driven approach more formally, and makes four contributions about the computational complexity of the inference problem, under the "dominant inhibition" assumption of molecular interactions. The first is a proof that the feasibility problem (does there exist a network that explains the data?) can be solved in polynomial-time. Second, the minimality problem (what is the smallest network that explains the data?) is shown to be NP-hard, and therefore unlikely to result in a polynomial-time algorithm. Third, a simple polynomial-time heuristic is shown to produce near-minimal solutions, as demonstrated by simulation. Fourth, the theoretical framework explains how multiplicity (the number of network solutions to realize a given biological process), which can take exponential-time to compute, can instead be accurately estimated by a fast, polynomial-time heuristic.

Process-based network decomposition reveals backbone motif structure.

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated).

Hydrogen peroxide induced oxidation of peroxisomal malate synthase and catalase.

Peroxisomes contain oxidases that produce H(2)O(2), which can result in protein oxidation. To test the vulnerability of peroxisomal proteins to oxidation in vivo the organelles were isolated from castor bean endosperm incubated with H(2)O(2). When peroxisomes were exposed to H(2)O(2)in vivo, the peroxisomal proteins exhibited an increase in carbonylation as detected in avidin blots of biotin hydrazide derivatized samples. Biotin-tagged peptides from trypsin digests of the proteins were analyzed by mass spectroscopy and compared to the masses of peptides from the same protein that had not been biotin-tagged and from proteins not exposed to excess H(2)O(2). H(2)O(2) exposure was found to increase the activity of catalase (CAT), and to increase the number of oxidized peptides found in CAT and malate synthase (MS). CAT had 10 peptides that were affected by in vivo exposure to H(2)O(2) and MS had 8. These sites of oxidation have definable locations within the proteins' structures.

Pathway switching explains the sharp response characteristic of hypoxia response network.

Hypoxia induces the expression of genes that alter metabolism through the hypoxia-inducible factor (HIF). A theoretical model based on differential equations of the hypoxia response network has been previously proposed in which a sharp response to changes in oxygen concentration was observed but not quantitatively explained. That model consisted of reactions involving 23 molecular species among which the concentrations of HIF and oxygen were linked through a complex set of reactions. In this paper, we analyze this previous model using a combination of mathematical tools to draw out the key components of the network and explain quantitatively how they contribute to the sharp oxygen response. We find that the switch-like behavior is due to pathway-switching wherein HIF degrades rapidly under normoxia in one pathway, while the other pathway accumulates HIF to trigger downstream genes under hypoxia. The analytic technique is potentially useful in studying larger biomedical networks.