Frontiers in Behavioral Neuroscience最新文献

The reproducibility crisis and translational gap in preclinical research underscore the need for more accurate and reliable methods of health monitoring in animal models. Manual testing is labor-intensive, low-throughput, prone to human bias, and often stressful for animals. Although many smart cages have been introduced, they have seen limited adoption due to either low throughput (being limited to single animals), low data density (a few metrics only), high costs, a need for new space or infrastructure in the vivarium, high complexity use, or a combination of the above. Although technologies for video-based single-animal tracking have matured, no existing technology enables robust and accurate multi-animal tracking in standard home cages. To solve these problems, we built a new type of assay device: the Smart Lid. Smart Lids mount to existing racks, above standard home cages and stream video and audio data, turning regular racks into high-throughput monitoring platforms. To solve the multi-animal tracking problem, we developed a new computer vision pipeline (MOT - Multi-Organism Tracker) along with a new ear tag purpose-designed for computer vision tracking. MOT achieves over 97% accuracy in multi-animal tracking while maintaining an affordable runtime cost (less than $100 per month). The pipeline returns 21 health-related metrics, covering activity, feeding, drinking, rearing, climbing, fighting, cage positioning, social interactions and sleeping, with additional metrics under development.

Cognitive decline is a major non-motor symptom in patients with Parkinson's disease (PD) that can be present as early as the prodromal stage. As a multisystem neurodegenerative syndrome, PD is associated with disturbances in various neurotransmitters, including dopamine, acetylcholine, serotonin, noradrenaline, glutamate, and gamma-aminobutyric acid (GABA). While the roles of dopaminergic and cholinergic deficiencies in cognitive impairment in PD are well documented, the contribution of the GABAergic system is less clear. We investigated spatial and recognition memory, along with changes in hippocampal GABAergic parvalbumin-positive (PV+) neurons, in distinct rat models of PD neuropathology. PD cholinopathy was induced by bilateral pedunculopontine tegmental nucleus (PPT) lesion, hemiparkinsonism was induced by unilateral substantia nigra pars compacta (SNpc) lesion, and hemiparkinsonism with PD cholinopathy was induced by unilateral SNpc and bilateral PPT lesions. Behavioral tests were conducted 14 and 42 days after lesions and included assessments of spatial memory (spatial habituation test), recognition memory (novel object recognition test), and measurements of motor activity (open field test). Motor function was preserved in all PD models. We observed delayed impairments in spatial and recognition memory in PD cholinopathy, and persistent impairment in spatial memory in hemiparkinsonism, although hippocampal PV expression remained unchanged over time. In hemiparkinsonism with PD cholinopathy, persistent spatial memory impairment was followed by delayed recognition memory deficits, along with hippocampal PV suppression, which was functionally linked to recognition memory impairment. Our results show that different PD neuropathologies underlie different memory impairments in rats. While dopaminergic denervation plays an important role in impairing spatial memory from the prodromal stage of PD, cholinergic denervation impairs recognition memory in a delayed manner. However, only their synergistic dysfunction alters hippocampal GABAergic PV+ neuron-mediated inhibitory transmission during PD progression, which was correlated with memory impairment.

Stress exposure can disrupt fear extinction, which is a hallmark of some stress-related disorders. The underlying molecular mechanisms of impaired extinction, especially in females, remain poorly understood. In this study, we investigated proteomics changes in the infralimbic cortex, a region critical for fear suppression, in female rats exposed to single prolonged stress (SPS). One week after SPS exposure, adult female rats underwent auditory fear conditioning and extinction training and were classified as susceptible or resilient based on their extinction performance. Quantitative proteomics using tandem mass tag labeling combined with bioinformatics analysis identified distinct proteins and pathways differentiating the groups. Susceptible rats displayed unique proteomic profiles in the infralimbic cortex. Several of the 53 differentially expressed proteins are associated with synaptic plasticity and memory, including neurogranin and microtubule-associated protein tau (MAPT). Pathway enrichment analysis identified alterations in synaptogenesis, clathrin-mediated endocytosis, calcium signaling, and chaperone-mediated autophagy. Functional validation using AAV-shRNA knockdown of neurogranin or MAPT in CAMKIIα-expressing neurons of the infralimbic cortex improved extinction memory in SPS-exposed animals. Our findings suggest that dysregulated protein expression in the infralimbic cortex contributes to impaired extinction memory and traumatic stress susceptibility in female rats, offering insight into the neurobiological mechanisms underlying vulnerability to stress-related disorders.

Background: Sleep, a core circadian rhythm, maintains physiological homeostasis. Its dysfunction links to neuropsychiatric disorders. Clinically, poor sleep impairs positive emotions and enhances negative emotion susceptibility, but the mechanism remains unclear, potentially involving circadian clock genes and neuroinflammatory pathways.

Methods: Divide the male C57BL/6J mice into the following five groups: Non-sleep deprivation (SD) control (CON), sleep recovery 14-day after SD 7-day (SD7R14), sleep recovery 21-day after SD 7-day (SD7R21), sleep recovery 14-day after SD 14-day (SD14R14), and sleep recovery 21-day after SD 14-day (SD14R21). Behavioral tests evaluated anxiety-like behaviors, fear and andanhedonia. Histological staining observed neuronal morphology in the medial prefrontal cortex (mPFC), and RT-qPCR was employed to measure mRNA levels of circadian clock genes, Silent information regulator 6 (Sirt6), High mobility group box-1 (Hmgb1), and inflammatory factors.

Results: SD induces time-dependent anxiety-like behaviors (reduced exploratory activity in elevated mazes), anhedonia (decreased sucrose preference), and fear behaviors (prolonged immobility in forced swim and tail suspension tests). Histological analysis reveals reversible neuronal damage in the mPFC, with complete recovery observed after 21 days of sleep restitution. Molecular analyses show dysregulation of the muscle aryl-hydrocarbon receptor nuclear translocator-like 1 (Bmal1) and circadian locomotor output cycles kaput (Clock) circadian pathway and activation of the Sirt6/Hmgb1 inflammatory axis, leading to proinflammatory cytokine release (TNFα, IL1β, COX-2, IL6), with partial recovery after sleep restoration.

Conclusion: SD for 7-day or 14-day may impair emotional behaviors by disrupting the RNA expression of clock genes and the Sirt6/Hmgb1 inflammatory axis, while sleep recovery for 14-day or 21-day can partially reverse this impairment.

Objective: Severe traumatic brain injury (sTBI) causes permanent disability in adults worldwide. While enriched environments (EE) have been shown to improve recovery in the early post-TBI period, their efficacy during the chronic phase of sTBI remains unclear. This study evaluated neurological function recovery in mice with chronic sTBI housed in either traditional EE or supportive EE.

Methods: Adult male C57BL mice were subjected to sTBI by controlled cortical impact and maintained in standard environments (SE) for 7 months. sTBI mice were then randomized into SE (TBI-SE), traditional EE (TBI-EE-1), or supportive EE (TBI-EE-2, co-housed with sham mice). Sham controls were housed in SE (Sham-SE) or supportive EE (Sham-EE-2). EE consisted of a large stainless-steel cage with toys replaced three times weekly. Mice remained in these conditions for 10 weeks, and neurobehavioral testing was performed beginning in week 6.

Results: In the RotaRod test, TBI-SE mice displayed persistent motor coordination and learning deficits, whereas TBI-EE-2 mice showed robust motor coordination recovery and improved motor learning. Of all TBI mice, only the TBI-EE-2 mice demonstrated improved motor learning. In the Morris water maze test, both TBI-EE-1 and TBI-EE-2 groups showed enhanced spatial learning and memory compared with TBI-SE. Y-maze testing revealed impaired short-term memory in TBI-EE-1 mice but significant improvement in TBI-EE-2 mice. Anxiety-like behavior, assessed by open field and light-dark box tests, was reduced only in the TBI-EE-2 mice.

Conclusion: Supportive EE more effectively reduced anxiety and improved motor and cognitive function in chronic sTBI compared with conventional EE. These findings highlight the potential value of incorporating social integration with healthy individuals into rehabilitation programs to optimize recovery in chronic severe TBI.

Objective: Gliomas, the most aggressive type of brain tumor, are infamous for their low survival rates. Tumor grading and isocitrate dehydrogenase (IDH) status are key prognostic biomarkers for gliomas. However, obtaining these markers typically requires invasive methods such as biopsy. As an effective, noninvasive alternative, multimodal MRI can reveal tumor spatial information and the microenvironment. Low-grade and IDH-mutant gliomas often exhibit T2-FLAIR mismatch signals. Medical image foundational models can explore complex representations in medical images, and fine-tuning them may further enhance glioma diagnosis.

Methods: We propose a multi-task network, MTSAM, for simultaneous glioma IDH genotyping and grading. MTSAM first uses dilated convolutions to simulate large-field convolutions and then reviews the T2 and FLAIR images. Then, we employ convolutions to perform a detailed exploration of the T2 and FLAIR images, and we subtract the weighted T2 and FLAIR images to obtain T2-FLAIR mismatch features. T2-FLAIR mismatch features are concatenated with multimodal MRIs and input into the customized SAM-Med3D. The customized SAM-Med3D is fine-tuned by leveraging complementary information across multi-view modalities, including MRIs, handcrafted radiomics (HCR), and clinical features. Then it extracts deep features for accurate IDH genotyping and grading.

Results: MTSAM achieves AUCs of 92.38 and 94.31% for glioma IDH typing and grading on the UCSF-PDGM dataset, respectively, and AUCs of 91.56 and 93.37% on the BraTS2020 dataset, outperforming other methods. Additionally, we use Grad-CAM to visualize the attention maps of MTSAM, demonstrating its potential for non-invasive glioma diagnosis.

Conclusion: The proposed method demonstrates that we can effectively fuse multi-view, non-invasive information and fully explore the knowledge learned by medical image foundational models from large-scale medical datasets to facilitate glioma diagnosis, thereby advancing glioma research.

Introduction: Flow is characterized by complete immersion and optimal engagement in a task, striking a balance between challenge and skill. Recent neuroimaging studies suggest that flow involves dynamic interactions among large-scale brain networks, particularly the default mode network (DMN) and the executive control network (ECN). This review aims to synthesize current findings on how flow-related DMN-ECN connectivity supports creativity and emotional regulation (ER).

Methodology: Following PRISMA guidelines, we searched PubMed, PsycINFO, and Google Scholar for peer-reviewed neuroimaging studies that experimentally induced or measured flow states. Inclusion criteria encompassed task-based and resting-state fMRI, PET, or EEG designs focusing on DMN, ECN, or related networks (e.g., salience, reward), and studies explicitly reporting on creativity or ER outcomes. We extracted data on sample characteristics, flow induction methods, neuroimaging modalities, and main findings regarding DMN/ECN activation and connectivity. Risk of bias was assessed in the domains of selection, performance, detection, attrition, and reporting.

Results: Nine studies met the inclusion criteria. Across diverse tasks-ranging from video games to jazz improvisation-flow was consistently associated with (1) down-regulation of core DMN regions (e.g., medial prefrontal cortex, posterior cingulate cortex) linked to diminished self-referential thought, (2) increased activity in lateral prefrontal and parietal areas underpinning attentional control, and (3) functional connectivity between networks often considered anti-correlated (e.g., DMN and ECN). This integrated network state appears to facilitate simultaneous idea generation (DMN) and goal-directed processing (ECN), supporting creativity. Additionally, reduced amygdala activity and insula-reward network coupling during flow suggest potential benefits for emotional regulation, allowing high focus and low anxiety.

Conclusion: Flow emerges as a unique neurocognitive phenomenon marked by selective DMN suppression and enhanced ECN engagement. Such network reconfiguration fosters creativity through DMN-ECN synergy while providing emotional stability via reduced self-monitoring and negative affect. Although these findings are promising, further research should employ larger, more diverse samples, incorporate causal and longitudinal designs, and explicitly measure ER outcomes. Elucidating the neurochemical underpinnings of flow (e.g., dopamine release) and individual differences in "flow-proneness" remains an important future direction.

Machine learning is revolutionizing behavioral neuroscience by enabling the study of animal behavior with greater ecological validity while maintaining experimental rigor. Traditional manual observation methods in ethology are constrained by subjectivity, costs, and low throughput, whereas modern machine learning algorithms now provide quantitative tools to investigate natural behavior with unprecedented precision. This mini review surveys recent advances in machine learning for behavioral neuroscience, focusing on markerless pose estimation and unsupervised behavioral clustering, and discusses their roles along the typical research pipeline, from tracking and detection to classification and integration of behavioral and neural data. Open-source platforms using deep learning-based image processing have turned video cameras into high-resolution measurement devices, while unsupervised methods extend inference across large-scale behavioral recordings. In laboratory settings, machine learning enables fine-scale analysis of animal kinematics and their relationship to neural activity, while in field studies it enhances longitudinal data collection through drone and satellite imaging. These approaches expand ethological research by quantifying movement, segmenting behavior into meaningful units, detecting transient events often missed by human observers, and bridging behavior with brain activity via joint latent spaces and closed-loop paradigms. Although challenges remain in handling high-dimensional datasets, machine learning offers powerful opportunities for more comprehensive neuroscientific insights. By bridging the controlled precision of the laboratory with the complexity of real-world environments, these methods advance our understanding of animal behavior and its neural underpinnings, providing experimentalists with practical tools to design, implement, and interpret more naturalistic studies in the field of ethological neuroscience.

Background: Effective feedback in the cognitive domain is essential for surgical education but often limited by resource constraints and traditional assessment formats. Artificial Intelligence (AI) has emerged as a catalyst for innovation, enabling automated feedback, real-time cognitive diagnostics, and scalable item generation, thereby transforming how future surgeons learn and are assessed.

Methods: An item bank of 150 multiple-choice questions was developed using AI-assisted item generation and difficulty estimation. A formative Computerized Adaptive Testing (CAT), balanced across three cognitive domains (memory, analysis, and decision) and surgical topics, was delivered via QuizOne^® 3-5 days before the summative Progress Test. A total of 147 students participated, of whom 116 completed the formative CAT. Performance correlations, group comparisons, analysis of covariance (ANCOVA), and regression analyses were conducted.

Results: Students who voluntarily completed CAT showed higher Progress Test scores, though causality cannot be established due to self-selection bias (p = 0.021), with the effect persisting after adjusting for prior academic performance (ANCOVA p = 0.041). Memory skills were the strongest predictors of summative outcomes (R ² = 0.180, β = 0.425), followed by analysis (R ² = 0.080, β = 0.283); decision was not significant (R ² = 0.029, β = 0.170).

Conclusion: AI-enhanced CAT-Cognitive Diagnostic Modeling (CDM) represents a promising formative approach in undergraduate surgical education, being associated with higher summative performance and providing individualized diagnostic feedback. Refining feedback presentation and enhancing decision-making assessment could further optimize its educational impact.

Introduction: Social isolation, reduced social interaction, and social anhedonia are associated with a range of neuropsychiatric conditions. While the search for novel pharmacological agents to treat social symptoms persists, more precise social behavior measures in pre-clinical animal models are needed to make the most accurate predictions of therapeutic outcomes.

Methods: In the current study, we propose two novel behavioral tasks to measure social motivation in mouse models. We define social motivation as the willingness to exert effort to access a social partner. The first social motivation test, the weighted door task, requires a mouse to push open a one-way, weighted door that increases in weight across successive trials to access a social partner behind the door. The second social motivation test, the ladder task, requires a mouse to climb a ladder that increases in steepness across trials to access a social partner on a platform at the top of the ladder. To validate these tasks, we compared behavioral outcomes across three common inbred strains, C57BL/6J, DBA/2J, and BTBR T + Itpr3 tf /J. Social motivation outcomes were then compared to outcomes in two standard social behavior tests: the three-chamber task and the free dyadic social interaction task.

Results: Following behavioral testing, we found that each strain displayed distinct behavioral responses in social motivation tasks with BTBR mice demonstrating low social motivation, DBA mice demonstrating high social motivation, and C57 mice demonstrating conditionally high social motivation during low effort trials.

Discussion: When combined with standard social behavior testing, our measures provide more detailed social behavior phenotypes unique to each strain. In addition to allowing the creation of more complete social behavior ethograms, these tasks offer advantages as compared to existing conditioning-based behavior tasks measuring social motivation and reward such as the social conditioned place preference task and operant conditioning for social reward. The weighted door and ladder tasks leverage innate exploration behaviors that do not require prior learning which allows for more models, including those with memory, attention, and learning deficits, to be used. These pre-clinical measures of social motivation may prove useful in improving predictions of social behavior outcomes of proposed pharmacological interventions for clinical populations.