Study Of Brain Function Research Articles

A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.

Examining functional Near-Infrared Spectroscopy as a tool to study brain function in bilinguals

There is increasing evidence that using more than one languages has significant effects on brain function. These effects have been observed in the developing, adult and aging brain, and have been suggested to have implications for cognitive and brain decline in bilinguals and multilinguals. Aside from extensive investigations with behavioral methods, such effects are now commonly investigated with functional neuroimaging methods, mostly functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG). A relatively underused method in the field is functional Near-Infrared Spectroscopy (fNIRS), which carries several advantages over more established methods, including being appropriate for use with infants and children, but also with older and diseased samples. This paper provides an overview of the method and an account of how it has been used in the field of bilingual cognition. It concludes with suggestions of how the method can be best utilized in future research, highlighting it as a method with a strong potential for updating existing theories on the effects of bilingualism on brain function.

The Agoutis: A Future Model for Ecologically Relevant Neuroscience and Physiology In Natura

ABSTRACTThe overarching goal of neurobiology is to understand how complex behaviors are generated by the nervous system. The behavior of each species, and the brain that controls it, is shaped by the historical and current state of the environment that they inhabit. This fact is juxtaposed with the reductionist approach of neuroscience that isolates animals from their natural environment. Understanding how brains evolved to orchestrate the myriads of natural behaviors an animal performs in response to its environment requires an integrative approach to neuroscience that considers ecology, ethology, and evolution. Current technological developments are leading us to an inflection point at which studying brain functions in the wild is now possible. Ecological studies on how the environment affects behavior of animals (i.e., hibernation, foraging, food hoarding, and nest building) have framed a plurality of questions to be answered mechanistically, and yet, only few studies have addressed the relationship between the environment and the brain's anatomy and physiology. Neuroscience needs new animal models that allow us to tackle such questions in the wild. Here, we propose a new animal model for wild neuroscience, the agouti (Dasyprocta spp.), a large wild rodent playing a critical seasonal role in the maintenance of the central and south American rainforest ecosystems. We focus on how a rodent model, like the agouti, will allow for the investigation of large‐scale brain dynamics during seasonal behaviors of ecological importance: scatter‐hoarding and retrieval. We describe agouti evolution, ecology, and physiology as well as neuro‐anatomical and neurophysiological studies, which have set the foundation for future neuroscience in natura. We suggest agoutis have the potential to be a groundbreaking model for wild neuroscience.

Automated quality control of small animal MR neuroimaging data

Abstract Magnetic resonance imaging (MRI) is a valuable tool for studying brain structure and function in animal and clinical studies. With the growth of public MRI repositories, access to data has finally become easier. However, filtering large datasets for potential poor-quality outliers can be a challenge. We present AIDAqc, a machine-learning-assisted automated Python-based command-line tool for small animal MRI quality assessment. Quality control features include signal-to-noise ratio (SNR), temporal SNR, and motion. All features are automatically calculated and no regions of interest are needed. Automated outlier detection for a given dataset combines the interquartile range and the machine-learning methods one-class support vector machine, isolation forest, local outlier factor, and elliptic envelope. To evaluate the reliability of individual quality control metrics, a simulation of noise (Gaussian, salt and pepper, speckle) and motion was performed. In outlier detection, single scans with induced artifacts were successfully identified by AIDAqc. AIDAqc was challenged in a large heterogeneous dataset collected from 19 international laboratories, including data from mice, rats, rabbits, hamsters, and gerbils, obtained with different hardware and at different field strengths. The results show that the manual inter-rater agreement (mean Fleiss Kappa score 0.17) is low when identifying poor-quality data. A direct comparison of AIDAqc results, therefore, showed only low-to-moderate concordance. In a manual post hoc validation of AIDAqc output, precision was high (>70%). The outlier data can have a significant impact on further postprocessing, as shown in representative functional and structural connectivity analysis. In summary, this pipeline optimized for small animal MRI provides researchers with a valuable tool to efficiently and effectively assess the quality of their MRI data, which is essential for improved reliability and reproducibility.

Magnetogenetic stimulation inside MRI induces spontaneous and evoked changes in neural circuits activity in rats.

The ability to modulate specific neural circuits and simultaneously visualize and measure brain activity with MRI would greatly impact our understanding of brain function in health and disease. The combination of neurostimulation methods and functional MRI in animal models have already shown promise in elucidating fundamental mechanisms associated with brain activity. We developed an innovative magnetogenetics neurostimulation technology that can trigger neural activity through magnetic fields. Similar to other genetic-based neuromodulation methods, magnetogenetics offers cell-, area-, and temporal-specific control of neural activity. The magnetogenetic protein-Electromagnetic Perceptive Gene (EPG)-is activated by non-invasive magnetic fields, providing a unique way to target neural circuits by the MRI static and gradient fields while simultaneously measuring their effect on brain activity. EPG was expressed in rat's visual cortex and the amplitude of low-frequency fluctuation, resting-state functional connectivity (FC), and sensory activation was measured using a 7T MRI. The results demonstrate that EPG-expressing rats had significantly higher signal fluctuations in the visual areas and stronger FC in sensory areas consistent with known anatomical visuosensory and visuomotor connections. This new technology complements the existing neurostimulation toolbox and provides a means to study brain function in a minimally-invasive way which was not possible previously.

Replicability and generalizability in population psychiatric neuroimaging.

Studies linking mental health with brain function in cross-sectional population-based association studies have historically relied on small, underpowered samples. Given the small effect sizes typical of such brain-wide associations, studies require samples into the thousands to achieve the statistical power necessary for replicability. Here, we detail how small sample sizes have hampered replicability and provide sample size targets given established association strength benchmarks. Critically, while replicability will improve with larger samples, it is not guaranteed that observed effects will meaningfully apply to target populations of interest (i.e., be generalizable). We discuss important considerations related to generalizability in psychiatric neuroimaging and provide an example of generalizability failure due to "shortcut learning" in brain-based predictions of mental health phenotypes. Shortcut learning is a phenomenon whereby machine learning models learn an association between the brain and an unmeasured construct (the shortcut), rather than the intended target of mental health. Given the complex nature of brain-behavior interactions, the future of epidemiological approaches to brain-based studies of mental health will require large, diverse samples with comprehensive assessment.

From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI.

Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses-from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.

Machine learning of brain-specific biomarkers from EEG

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. All authors have been working for F. Hoffmann-La Roche Ltd.

Sex and sobriety: Human brain structure and function in AUD abstinence

Women are drinking alcohol as much as men for the first time in history. Women experience more health-related consequences from alcohol use disorder (AUD), like increased prevalence of alcohol-related cancers, faster progression of alcohol-related liver disease, and greater risk for relapse compared to men. Thus, sex differences in chronic alcohol use pose a substantial public health problem. Despite these evident sex differences, our understanding of how these differences present during alcohol abstinence is limited. Investigations of brain structure and function are therefore critical for disentangling factors that lead to sex differences in AUD abstinence. This review will discuss current human neuroimaging data on sex differences in alcohol abstinence, focusing on structural and functional brain measures. Current structural imaging literature reveals that abstinent men have smaller gray and white matter volume and weaker structural connectivity compared to control men. Interestingly, abstinent women do not show differences in brain structure when compared to controls; instead, abstinent women show a relation between alcohol use and decreased measures of brain structure. Current functional brain studies reveal that abstinent men exhibit greater brain activation and stronger task-based functional connectivity to aversive stimuli than control men, while abstinent women exhibit lesser brain activation and weaker task-based functional connectivity than control women. Together, the current literature suggests that sex differences persist well into alcohol abstinence and impact brain structure and function differently. Understanding how men and women differ during alcohol abstinence can improve our understanding of sex-specific effects of alcohol, which will be critical to augment treatment methods to better serve women.

Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing

Electrical stimulation is a key tool in neuroscience, both in brain mapping studies and in many therapeutic applications such as cochlear, vestibular, and retinal neural implants. Due to safety considerations, stimulation is restricted to short biphasic pulses. Despite decades of research and development, neural implants lead to varying restoration of function in patients. In this study, we use computational modeling to provide an explanation for how pulsatile stimulation affects axonal channels and therefore leads to variability in restoration of neural responses. The phenomenological explanation is transformed into equations that predict induced firing rate as a function of pulse rate, pulse amplitude, and spontaneous firing rate. We show that these equations predict simulated responses to pulsatile stimulation with a variety of parameters as well as several features of experimentally recorded primate vestibular afferent responses to pulsatile stimulation. We then discuss the implications of these effects for improving clinical stimulation paradigms and electrical stimulation-based experiments.

Newborn's neural representation of instrumental and vocal music as revealed by fMRI: A dynamic effective brain connectivity study.

Music is ubiquitous, both in its instrumental and vocal forms. While speech perception at birth has been at the core of an extensive corpus of research, the origins of the ability to discriminate instrumental or vocal melodies is still not well investigated. In previous studies comparing vocal and musical perception, the vocal stimuli were mainly related to speaking, including language, and not to the non-language singing voice. In the present study, to better compare a melodic instrumental line with the voice, we used singing as a comparison stimulus, to reduce the dissimilarities between the two stimuli as much as possible, separating language perception from vocal musical perception. In the present study, 45 newborns were scanned, 10 full-term born infants and 35 preterm infants at term-equivalent age (mean gestational age at test = 40.17 weeks, SD = 0.44) using functional magnetic resonance imaging while listening to five melodies played by a musical instrument (flute) or sung by a female voice. To examine the dynamic task-based effective connectivity, we employed a psychophysiological interaction of co-activation patterns (PPI-CAPs) analysis, using the auditory cortices as seed region, to investigate moment-to-moment changes in task-driven modulation of cortical activity during an fMRI task. Our findings reveal condition-specific, dynamically occurring patterns of co-activation (PPI-CAPs). During the vocal condition, the auditory cortex co-activates with the sensorimotor and salience networks, while during the instrumental condition, it co-activates with the visual cortex and the superior frontal cortex. Our results show that the vocal stimulus elicits sensorimotor aspects of the auditory perception and is processed as a more salient stimulus while the instrumental condition activated higher-order cognitive and visuo-spatial networks. Common neural signatures for both auditory stimuli were found in the precuneus and posterior cingulate gyrus. Finally, this study adds knowledge on the dynamic brain connectivity underlying the newborns capability of early and specialized auditory processing, highlighting the relevance of dynamic approaches to study brain function in newborn populations.

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition.

Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact. We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale. Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models. Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power. The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.e., a smooth gradient among ICA connectivity values). Several methods are presented to summarize dynamic FNC gradients (dFNGs) over time, starting with static FNC gradients (sFNGs), then exploring the reordering properties as well as the dynamics of the gradients themselves. We then apply this approach to a dataset of schizophrenia (SZ) patients and healthy controls (HC). Functional dysconnectivity between different brain regions has been reported in schizophrenia, yet the neural mechanisms behind it remain elusive. Using resting state fMRI and ICA on a dataset consisting of 151 schizophrenia patients and 160 age and gender-matched healthy controls, we extracted 53 intrinsic connectivity networks (ICNs) for each subject using a fully automated spatially constrained ICA approach. We develop several summaries of our functional network connectivity gradient analysis, both in a static sense, computed as the Pearson correlation coefficient between full time series, and a dynamic sense, computed using a sliding window approach followed by reordering based on the computed gradient, and evaluate group differences. Static connectivity analysis revealed significantly stronger connectivity between subcortical (SC), auditory (AUD) and visual (VIS) networks in patients, as well as hypoconnectivity in sensorimotor (SM) network relative to controls. sFNG analysis highlighted distinctive clustering patterns in patients and HCs along cognitive control (CC)/ default mode network (DMN), as well as SC/ AUD/ SM/ cerebellar (CB), and VIS gradients. Furthermore, we observed significant differences in the sFNGs between groups in SC and CB domains. dFNG analysis suggested that SZ patients spend significantly more time in a SC/ CB state based on the first gradient, while HCs favor the SM/DMN state. For the second gradient, however, patients exhibited significantly higher activity in CB domains, contrasting with HCs' DMN engagement. The gradient synchrony analysis conveyed more shifts between SM/ SC networks and transmodal CC/ DMN networks in patients. In addition, the dFNG coupling revealed distinct connectivity patterns between SC, SM and CB domains in SZ patients compared to HCs. To recap, our results advance our understanding of brain network modulation by examining smooth connectivity trajectories. This provides a more complete spatiotemporal summary of the data, contributing to the growing body of current literature regarding the functional dysconnectivity in schizophrenia patients. By employing dFNG, we highlight a new perspective to capture large scale fluctuations across the brain while maintaining the convenience of brain networks and low dimensional summary measures.

PET-MRI Applications and Future Prospects in Psychiatry.

This article reviews the synergistic application of positron emission tomography-magnetic resonance imaging (PET-MRI) in neuroscience with relevance for psychiatry, particularly examining neurotransmission, epigenetics, and dynamic imaging methodologies. We begin by discussing the complementary insights that PET and MRI modalities provide into neuroreceptor systems, with a focus on dopamine, opioids, and serotonin receptors, and their implications for understanding and treating psychiatric disorders. We further highlight recent PET-MRI studies using a radioligand that enables the quantification of epigenetic enzymes, specifically histone deacetylases, in the brain in vivo. Imaging epigenetics is used to exemplify the impact the quantification of novel molecular targets may have, including new treatment approaches for psychiatric disorders. Finally, we discuss innovative designs involving functional PET using [18F]FDG (fPET-FDG), which provides detailed information regarding dynamic changes in glucose metabolism. Concurrent acquisitions of fPET-FDG and functional MRI provide a time-resolved approach to studying brain function, yielding simultaneous metabolic and hemodynamic information and thereby opening new avenues for psychiatric research. Collectively, the review underscores the potential of a multimodal PET-MRI approach to advance our understanding of brain structure and function in health and disease, which could improve clinical care based on objective neurobiological features and treatment response monitoring. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Quantifying tissue temperature changes induced by infrared neural stimulation: numerical simulation and MR thermometry.

Infrared neural stimulation (INS) delivered via short pulse trains is an innovative tool that has potential for us use for studying brain function and circuitry, brain machine interface, and clinical use. The prevailing mechanism for INS involves the conversion of light energy into thermal transients, leading to neuronal membrane depolarization. Due to the potential risks of thermal damage, it is crucial to ensure that the resulting local temperature increases are within non-damaging limits for brain tissues. Previous studies have estimated damage thresholds using histological methods and have modeled thermal effects based on peripheral nerves. However, additional quantitative measurements and modeling studies are needed for the central nervous system. Here, we performed 7 T MRI thermometry on ex vivo rat brains following the delivery of infrared pulse trains at five different intensities from 0.1-1.0 J/cm2 (each pulse train 1,875 nm, 25 us/pulse, 200 Hz, 0.5 s duration, delivered through 200 µm fiber). Additionally, we utilized the General BioHeat Transfer Model (GBHTM) to simulate local temperature changes in perfused brain tissues while delivering these laser energies to tissue (with optical parameters of human skin) via three different sizes of optical fibers at five energy intensities. The simulation results clearly demonstrate that a 0.5 second INS pulse train induces an increase followed by an immediate drop in temperature at stimulation offset. The delivery of multiple pulse trains with 2.5 s interstimulus interval (ISI) leads to rising temperatures that plateau. Both thermometry and modeling results show that, using parameters that are commonly used in biological applications (200 µm diameter fiber, 0.1-1.0 J/cm2), the final temperature increase at the end of the 60 sec stimuli duration does not exceed 1°C with stimulation values of 0.1-0.5 J/cm2 and does not exceed 2°C with stimulation values of up to 1.0 J/cm2. Thus, the maximum temperature rise is consistent with the thermal damage threshold reported in previous studies. This study provides a quantitative evaluation of the temperature changes induced by INS, suggesting that existing practices pose minimal major safety concerns for biological tissues.

Explaining deep learning-based representations of resting state functional connectivity data: focusing on interpreting nonlinear patterns in autism spectrum disorder.

Resting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used extensively to study brain function in psychiatric disorders, yielding insights into brain organization. However, the high dimensionality of the rs-fMRI data presents significant challenges for data analysis. Variational autoencoders (VAEs), a type of neural network, have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity (rsFC) patterns, thereby addressing the complex nonlinear structure of rs-fMRI data. Despite these advances, interpreting these latent representations remains a challenge. This paper aims to address this gap by developing explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). One-thousand one hundred and fifty participants (601 healthy controls [HC] and 549 patients with ASD) were included in the analysis. RsFC correlation matrices were extracted from the preprocessed rs-fMRI data using the Power atlas, which includes 264 regions of interest (ROIs). Then VAEs were trained in an unsupervised manner. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. We quantified the latent contribution scores for both the ASD and HC groups at the network level. We found that both ASD and HC groups share the top network connectivitives contributing to all estimated latent components. For example, latent 0 was driven by rsFC within ventral attention network (VAN) in both the ASD and HC. However, we found significant differences in the latent contribution scores between the ASD and HC groups within the VAN for latent 0 and the sensory/somatomotor network for latent 2. This study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC feature as the estimated latent representation changes, enabling an explainable deep learning model that better understands the underlying neural mechanisms of ASD.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

A large-scale c-Fos brain mapping study on extinction of cocaine-primed reinstatement.

Individuals with cocaine addiction can experience many craving episodes and subsequent relapses, which represents the main obstacle to recovery. Craving is often favored when abstinent individuals ingest a small dose of cocaine, encounter cues associated with drug use or are exposed to stressors. Using a cocaine-primed reinstatement model in rat, we recently showed that cocaine-conditioned interoceptive cues can be extinguished with repeated cocaine priming in the absence of drug reinforcement, a phenomenon we called extinction of cocaine priming. Here, we applied a large-scale c-Fos brain mapping approach following extinction of cocaine priming in male rats to identify brain regions implicated in processing the conditioned interoceptive stimuli of cocaine priming. We found that cocaine-primed reinstatement is associated with increased c-Fos expression in key brain regions (e.g., dorsal and ventral striatum, several prefrontal areas and insular cortex), while its extinction mostly disengages them. Moreover, while reinstatement behavior was correlated with insular and accumbal activation, extinction of cocaine priming implicated parts of the ventral pallidum, the mediodorsal thalamus and the median raphe. These brain patterns of activation and inhibition suggest that after repeated priming, interoceptive signals lose their conditioned discriminative properties and that action-outcome associations systems are mobilized in search for new contingencies, a brain state that may predispose to rapid relapse.

IVR-fNIRS: studying brain functions in a fully immersive virtual environment.

Immersive virtual reality (iVR) employs head-mounted displays or cave-like environments to create a sensory-rich virtual experience that simulates the physical presence of a user in a digital space. The technology holds immense promise in neuroscience research and therapy. In particular, virtual reality (VR) technologies facilitate the development of diverse tasks and scenarios closely mirroring real-life situations to stimulate the brain within a controlled and secure setting. It also offers a cost-effective solution in providing a similar sense of interaction to users when conventional stimulation methods are limited or unfeasible. Although combining iVR with traditional brain imaging techniques may be difficult due to signal interference or instrumental issues, recent work has proposed the use of functional near infrared spectroscopy (fNIRS) in conjunction with iVR for versatile brain stimulation paradigms and flexible examination of brain responses. We present a comprehensive review of current research studies employing an iVR-fNIRS setup, covering device types, stimulation approaches, data analysis methods, and major scientific findings. The literature demonstrates a high potential for iVR-fNIRS to explore various types of cognitive, behavioral, and motor functions in a fully immersive VR (iVR) environment. Such studies should set a foundation for adaptive iVR programs for both training (e.g., in novel environments) and clinical therapeutics (e.g., pain, motor and sensory disorders and other psychiatric conditions).

CSF1R inhibitor PLX3397 depletes microglia in Mongolian gerbil Meriones unguiculatus, but not in syrian hamster Mesocricetus auratus

Microglia are the residential immune cells in the central nervous system. Their roles as innate immune cells and regulators of synaptic remodeling are critical to the development and the maintenance of the brain. Numerous studies have depleted microglia to elucidate their involvement in healthy and pathological conditions. PLX3397, a blocker of colony stimulating factor 1 receptor (CSF1R), is widely used to deplete mouse microglia due to its non-invasiveness and convenience. Recently, other small rodents, including Syrian hamsters (Mesocricetus auratus) and Mongolian gerbils (Meriones unguiculatus), have been recognized as valuable animal models for studying brain functions and diseases. However, whether microglia depletion via PLX3397 is feasible in these species remains unclear. Here, we administered PLX3397 orally via food pellets to hamsters and gerbils. PLX3397 successfully depleted gerbil microglia but had no effect on microglial density in hamsters. Comparative analysis of the CSF1R amino acid sequence in different species hints that amino acid substitutions in the juxtamembrane domain may potentially contribute to the inefficacy of PLX3397 in hamsters.