Frontiers in Systems Neuroscience最新文献

The mesolimbic dopamine system is a set of subcortical brain circuits that plays a key role in reward processing, reinforcement, associative learning, and behavioral responses to salient environmental events. In our previous studies of the dopaminergic response to salient visual stimuli, we observed that dopamine release in the lateral nucleus accumbens (LNAc) of mice encoded information about the rate and magnitude of rapid environmental luminance changes from darkness. Light-evoked dopamine responses were rate-dependent, robust to the time of testing or stimulus novelty, and required phototransduction by rod and cone opsins. However, it is unknown if these dopaminergic responses also involve non-visual opsins, such as melanopsin, the primary photopigment expressed by intrinsically photosensitive retinal ganglion cells (ipRGCs). In the current study, we evaluated the role of melanopsin in the dopaminergic response to light in the LNAc using the genetically encoded dopamine sensor dLight1 and fiber photometry. By measuring light-evoked dopamine responses across a broad irradiance and wavelength range in constitutive melanopsin (Opn4) knockout mice, we were able to provide new insights into the ability of non-visual opsins to regulate the mesolimbic dopamine response to visual stimuli.

Anesthetics such as ketamine and thiopental, commonly used for inducing unconsciousness, have distinct effects on neuronal activity, metabolism, and cardiovascular and respiratory systems. Ketamine increases heart rate and blood pressure while preserving respiratory function, whereas thiopental decreases both and can cause respiratory depression. This study investigates the impact of ketamine (100 mg/kg) and thiopental (45 mg/kg) on ultraweak photon emission (UPE), oxidative-nitrosative stress, and antioxidant capacity in isolated rat brains. To our knowledge, no previous study has investigated and compared UPE in the presence and absence of anesthesia. Here, we compare the effects of ketamine and thiopental anesthetics with each other and with a non-anesthetized control group. Ketamine increased UPE, lipid peroxidation, and antioxidant enzyme activity while reducing thiol levels. Conversely, thiopental decreased UPE, oxidative markers, and antioxidant enzyme activity, while increasing thiol levels. UPE was negatively correlated with thiol levels and positively correlated with oxidative stress markers. These findings suggest that the contrasting effects of ketamine and thiopental on UPE are linked to their differing impacts on brain oxidative stress and antioxidant capacity. This research suggests a potential method to monitor brain oxidative stress via UPE during anesthesia, and opens up new ways for understanding and managing anesthetic effects.

Environmental enrichment, an enhancement in the breeding environment of laboratory animals, enhance development of the cortical circuit and suppresses brain dysfunction. We quantitatively investigated the influences of enriched environment (EE) exposure, on responses in layers 2/3 (L2/3) of the primary visual area (V1) of mice. EE modifies visual cortex plasticity by inducing immediate early genes. To detect this, we performed immunostaining for the immediate early gene product c-Fos. EE exposure significantly increased the number of neurons with high c-Fos fluorescence intensity compared with those of mice under standard housing (SH). In contrast, there was no significant difference in the number of neurons exhibiting low c-Fos intensity between the SH and EE exposure groups. To further investigate the mechanism of modulation by EE exposure, we developed a microcircuit model with a biologically plausible L2/3 of V1 that combined excitatory pyramidal (Pyr) neurons and three inhibitory interneuron subclasses. In the model, synaptic strengths between Pyr neurons were determined according to a log-normal distribution. Model simulations with various inputs mimicking physiological conditions for SH and EE exposure quantitatively reproduced the experimentally observed activity modulation induced by EE exposure. These results suggested that synaptic connections among Pyr neurons obeying a log-normal distribution underlie the characteristic EE-exposure-induced modulation of L2/3 in V1.

Complex behavior and task execution require fast changes of local activity and functional connectivity in cortical networks at multiple scales. The roles that changes of power and connectivity play during these processes are still not well understood. Here, we study how fluctuations of functional cortical coupling across different brain areas determine performance in an audiovisual, lateralized detection task in the ferret. We hypothesized that dynamic variations in the network's state determine the animals' performance. We evaluated these by quantifying changes of local power and of phase coupling across visual, auditory and parietal regions. While power for hit and miss trials showed significant differences only during stimulus and response onset, phase coupling already differed before stimulus onset. An analysis of principal components in coupling at the single-trial level during this period allowed us to reveal the subnetworks that most strongly determined performance. Whereas higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component revealed a reduction in coupling between subnetworks of different sensory modalities, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, our results show that fluctuations in the network state, as reflected in large-scale coupling, are key determinants of the animals' behavior.

This study examines the impact of positive and negative feedback on recall of past decisions, focusing on behavioral performance and electrophysiological (EEG) responses. Participants completed a decision-making task involving 10 real-life scenarios, each followed by immediate positive or negative feedback. In a recall phase, participants' accuracy (ACC), errors (ERRs), and response times (RTs) were recorded alongside EEG data to analyze brain activity patterns related to recall. Results indicate that accurately recalled decisions with positive feedback had slower RTs, suggesting an attentional bias toward positive information that could increase cognitive load during memory retrieval. A lack of difference in recall accuracy implies that social stimuli and situational goals may influence the positivity bias. EEG data showed distinct patterns: lower alpha band activity in frontal regions (AF7, AF8) for both correct and incorrect decisions recall, reflecting focused attention and cognitive control. Correctly recalled decisions with negative feedback showed higher delta activity, often linked to aversive processing, while incorrect recalls with negative feedback showed higher beta and gamma activity. A theta band feedback-dependent modulation in electrode activity showed higher values for decisions with negative feedback, suggesting memory suppression. These findings suggest that recalling decisions linked to self-threatening feedback may require greater cognitive effort, as seen in increased beta and gamma activity, which may indicate motivational processing and selective memory suppression. This study provides insights into the neural mechanisms of feedback-based memory recall, showing how feedback valence affects not only behavioral outcomes but also the cognitive and emotional processes involved in decision recall.

Introduction: Evidence increasingly shows that facial emotion recognition (FER) is impaired in refractory mesial temporal lobe epilepsy (rMTLE), especially in patients with a right focus. This study explores FER in both mild (mMTLE) and refractory forms, examining the influence of epileptic focus lateralization on FER.

Methods: 50 MTLE patients, categorized by epilepsy severity and focus lateralization, were compared with healthy controls. FER was assessed using the Ekman Faces Test (EFT), which evaluates recognition of six basic emotions, alongside a battery of cognitive and mood tests.

Results: mMTLE patients showed selective deficits in recognizing fear and anger, while rMTLE patients displayed broader deficits, affecting all emotions except surprise. Patients with a right focus underperformed across all negative emotions, whereas those with a left focus showed deficits mainly in fear and anger. Analysis indicated that early epilepsy onset was associated with poorer FER in right-focused patients; febrile seizures and mesial temporal sclerosis significantly impacted FER in left-focused patients.

Conclusion: MTLE affects circuits of FER even in mild subjects, although to a lesser extent than in refractory ones. Earlier onset of MTLE could disrupt the development of FER, possibly interfering during a critical phase of maturation of its circuits, when the focus is right. Conversely, left MTLE may cause less damage to FER circuits, requiring additional factors such as a history of febrile seizures and/or mesial temporal sclerosis for significant impact. Clinically, refractory and right-sided MTLE might be viewed as risk factors of FER deficits.

Dysfunction in fear and stress responses is intrinsically linked to various neurological diseases, including anxiety disorders, depression, and Post-Traumatic Stress Disorder. Previous studies using in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable insights into these mechanisms and aided the development of new therapeutic approaches. However, assessing these dysfunctions in animal subjects using IED and SEFL protocols can cause significant pain and suffering. To advance the understanding of fear and stress, this study presents a biologically and behaviorally plausible computational architecture that integrates several subregions of key brain structures, such as the amygdala, hippocampus, and medial prefrontal cortex. Additionally, the model incorporates stress hormone curves and employs spiking neural networks with conductance-based integrate-and-fire neurons. The proposed approach was validated using the well-established Contextual Fear Conditioning paradigm and subsequently tested with IED and SEFL protocols. The results confirmed that higher intensity aversive stimuli result in more robust and persistent fear memories, making extinction more challenging. They also underscore the importance of the timing of extinction and the significant influence of stress. To our knowledge, this is the first instance of computational modeling being applied to IED and SEFL protocols. This study validates our computational model's complexity and biological realism in analyzing responses to fear and stress through fear conditioning, IED, and SEFL protocols. Rather than providing new biological insights, the primary contribution of this work lies in its methodological innovation, demonstrating that complex, biologically plausible neural architectures can effectively replicate established findings in fear and stress research. By simulating protocols typically conducted in vivo-often involving significant pain and suffering-in an insilico environment, our model offers a promising tool for studying fear-related mechanisms. These findings support the potential of computational models to reduce the reliance on animal testing while setting the stage for new therapeutic approaches.

Brain responses to transcranial magnetic stimulation (TMS) can be recorded with electroencephalography (EEG) and comprise TMS-evoked potentials and TMS-induced oscillations. Repetitive TMS may entrain endogenous brain oscillations. In turn, ongoing brain oscillations prior to the TMS pulse can influence the effects of the TMS pulse. These intricate TMS-EEG and EEG-TMS interactions are increasingly attracting the interest of researchers and clinicians. This review surveys the literature of TMS and its interactions with brain oscillations as measured by EEG in health and disease.

In many real-life situations, decisions involve temporal delays between actions and their outcomes. During these intervals, waiting is an active process that requires maintaining motivation and anticipating future rewards. This study aimed to explore the role of the midbrain reticular formation (MRF) in delay-based decision-making. We recorded neural activity in the MRF while rats performed delay discounting and reward discrimination tasks, choosing between a smaller, sooner reward and a larger, later reward. Our findings reveal that MRF neurons are integral to maintaining motivation during waiting periods by encoding both the anticipated size and the discounted value of delayed rewards. Furthermore, the inactivation of the MRF led to a significant reduction in the rats' willingness to wait for delayed rewards. These results demonstrate the MRF's function in balancing the trade-offs between reward magnitude and timing, providing insight into the neural mechanisms that support sustained motivation and decision-making over time.