Measured Brain Activity Research Articles

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [18F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-related brain networks. Building on this principle, we introduce an approach called behavioral task-associated PET (beta-PET) consisting of two scans: the first after a mouse is familiarized with a conditioning chamber, and the second upon recall of contextual threat. Associative threat conditioning occurs between scans. Beta-PET focuses on brain regions encoding threat memory (e.g., amygdala, prefrontal cortex) and contextual aspects (e.g., hippocampus, subiculum, entorhinal cortex). Our results show that beta-PET identifies a biologically defined network encoding contextual threat memory and its uncoupling in a mouse model of long sepsis. Moreover, machine learning algorithms (linear logistic regression) and ordinal trends analysis demonstrate that beta-PET robustly predicts the behavioral defense response and its breakdown during long sepsis.

Attentional Refreshing in Working Memory and Its Interplay with Long-term Memory: A Behavioral and EEG Study.

Despite the growing interest in the study of attentional refreshing, the functioning of this working memory maintenance mechanism, including its cerebral underpinnings, is still debated. In particular, it remains unclear whether refreshing promotes long-term memory and whether it, in return, depends on long-term memory content to operate. Here, we used direct maintenance instructions and measured brain activity to investigate working memory maintenance with two objectives: (1) test if different behavioral and oscillatory patterns could be observed when participants were instructed to use attentional refreshing versus verbal rehearsal, and (2) observe whether and how refreshing is modulated when maintaining novel (pseudowords) versus familiar (words) memoranda. We conducted an EEG experiment using a modified Brown-Peterson task, in which we manipulated the type of maintenance engaged through explicit instructions (verbal rehearsal vs. refreshing), the type of memoranda (words vs. pseudowords), and the memory load (2 vs. 6). Using scalp EEG, we measured both neural oscillations during working memory maintenance and ERPs during the concurrent parity judgment task. For words, we showed that verbal rehearsal benefited more short-term recall whereas refreshing benefited more delayed recall. In keeping with these behavioral differences between maintenance instructions, frontal-midline theta power increased with memory load only when using verbal rehearsal, whereas occipito-parietal alpha desynchronization was larger with refreshing than verbal rehearsal. When maintaining pseudowords, verbal rehearsal also benefitted short-term recall more than refreshing. However, no long-term memory benefit of refreshing was observed for pseudowords, and oscillatory activity was not different under the two maintenance instructions. Our results provide new evidence supporting the independence between attentional refreshing and verbal rehearsal, and bring new insight into refreshing functioning. We discuss the possible interpretations of these results and the implications for the attentional refreshing literature.

A Study on the Physiological Diversity of Hand Bathing under Different Conditions

Achieving the best outcomes and ensuring patients are as comfortable as possible are crucial goals for healthcare professionals. Hospital stress has been linked to worsening patient outcomes and so it is important to implement methods for keeping patients calm. Targeting the autonomic nervous system through hand bathing could provide answers. Associate Professor Hajime Nakano and his collaborators at the Faculty of Nursing, Josai International University, Japan, used precise tools to measure the heart rates of patients bathing their hands in waterbaths maintained at carefully controlled temperatures. The researchers used a probe to non-invasively measure brain activation and found that hand bathing decreased sympathetic nervous system activity, increased parasympathetic nervous system activity and produced feelings of relaxation. Nakano and the team conducted another study in which they activated the sympathetic nervous system in volunteers with mental arithmetic and found that hand bathing significantly suppressed the activation. This led them to posit that thermal stimulation has the potential to impact the stress responses in patients and their motivation in a bidirectional manner, which indicated significant possibilities for clinical applications. The researchers are keen to explore how altering the temperature of the hand bath can positively impact patients and an optimum temperature be selected. The team wants to ensure that interventions are tailored to the unique needs of patients and, as such, observed responses under different conditions and collected vast data that has enabled them to consider a research design that accommodated individual differences.

Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data

Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5–32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for “bad” segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker.

Neurophysiological Correlates of Expert Knowledge: An Event-Related Potential (ERP) Study about Law-Relevant Versus Law-Irrelevant Terms.

The evaluation of evidence, which frequently takes the form of scientific evidence, necessitates the input of experts in relevant fields. The results are presented as expert opinions or expert evaluations, which are generally accepted as a reliable representation of the facts. A further issue that remains unresolved though is the process of evaluating the expertise and knowledge of an expert in the first instance. In general, earned certificates, grades and other objective criteria are typically regarded as representative documentation to substantiate an expert status. However, there is a possibility that these may not always be sufficiently representative. The goal of the present study was to provide evidence that the neural processing of law-relevant and law-irrelevant terms varies significantly between participants who have received training in the field of law (experts) and those who have not (novices). To this end, changes in brain activity were recorded via electroencephalography (EEG) during visual presentations of terms belonging to five different categories (fake right, democracy, filler word, basic right and rule of law). Event-related potentials (ERPs) were subsequently averaged for each category and subjected to statistical analysis. The results clearly demonstrate that participants trained in law processed fake rights and filler words in a similar manner. Furthermore, both of these conditions elicited different levels of brain activity compared to all law-relevant terms. This was not the case in participants who had not received legal training. The brains of untrained participants processed all five term categories in a strikingly similar manner. In light of prior knowledge regarding language processing, the primary focus was on two distinct electrode locations: one in the left posterior region, and the other in the left frontal region. In both locations, the most prominent differences in brain activity elicited by the aforementioned term categories in law-trained participants occurred approximately 450 milliseconds after stimulus onset. The results were further corroborated by a repeated-measures ANOVA and subsequent t-tests, which also demonstrated the absence of this effect in law-untrained participants. The findings of this study provide empirical evidence that brain activity measurements, in particular ERPs, can be used to distinguish between experts trained in a specific field of expertise and novices in that field. Such findings have the potential to facilitate objective assessments of expertise, enabling comparisons between experts and novices that extend beyond traditional criteria such as qualifications and experience. Instead, individuals can be evaluated based on their cognitive processes, as observed through brain activity.

How Can Near‐Infrared Spectroscopy (NIRS) Help Understanding of the Psychotherapy Process? A Scoping Review of Therapeutic Interactions

AbstractIn recent years, near‐infrared spectroscopy (NIRS) has garnered significant attention for its potential clinical applications as a non‐invasive and straightforward method for measuring brain functions. While NIRS has become useful in psychiatric contexts by aiding in the differential diagnosis of depression, its application in clinical psychology is still developing. Due to its ecological validity, which allows for measurements in natural settings, NIRS shows promise in revealing the dynamic elements of the psychotherapy process. In this study, we conducted a scoping literature review of the use of NIRS in psychotherapeutic interactions, exploring its potential to unveil the complexities of psychotherapy settings involving interpersonal interactions. Employing a scoping review approach, we systematically searched the PubMed, PsycINFO, and Web of Science databases for articles published through October 19, 2023 about studies using NIRS as a measurement instrument: This yielded various therapeutic interactions. Of the 155 retrieved results that remained after the initial screening, seven met the inclusion criteria. Of these, six studies examined the synchronization of brain functions through the simultaneous brain activity measurements (hyperscanning) of two participants. The review revealed that, despite the anticipated potential of NIRS, it has become evident there is a dearth of research focusing on interactions during psychotherapy. Furthermore, the few available studies have not explored changes in line with the progression of psychotherapeutic interactions. Consequently, future studies should address whether NIRS can effectively assess alterations in brain function resulting from micro‐level events, such as the therapeutic interaction between therapists and clients during psychotherapy.

Ecological functional near-infrared spectroscopy in mobile children: using short separation channels to correct for systemic contamination during naturalistic neuroimaging.

The advances and miniaturization in functional near-infrared spectroscopy (fNIRS) instrumentation offer the potential to move the classical laboratory-based cognitive neuroscience investigations into more naturalistic settings. Wearable and mobile fNIRS devices also provide a novel child-friendly means to image functional brain activity in freely moving toddlers and preschoolers. Measuring brain activity in more ecologically valid settings with fNIRS presents additional challenges, such as the increased impact of physiological interferences. One of the most popular methods for minimizing such interferences is to regress out short separation channels from the long separation channels [i.e., superficial signal regression (SSR)]. Although this has been extensively investigated in adults, little is known about the impact of systemic changes on the fNIRS signals recorded in children in either classical or novel naturalistic experiments. We aim to investigate if extracerebral physiological changes occur in toddlers and preschoolers and whether SSR can help minimize these interferences. We collected fNIRS data from 3- to 7-year-olds during a conventional computerized static task and in a dynamic naturalistic task in an immersive virtual reality (VR) cave automatic virtual environment. Our results show that superficial signal contamination data are present in young children as in adults. Importantly, we find that SSR helps in improving the localization of functional brain activity, both in the computerized task and, to a larger extent, in the dynamic VR task. Following these results, we formulate suggestions to advance the field of developmental neuroimaging with fNIRS, particularly in ecological settings.

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.

Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses

Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.

Influence of feedback transparency on motor imagery neurofeedback performance: the contribution of agency

Objective.Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) of a participant's brain activity that they must learn to self-regulate. A classical visual FB delivered in a NF task is a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked-from the subject's perspective-to the task performed (e.g., motor imagery (MI)). This may decrease the sense of agency, that is, the participants' reported control over FB. Here, we assessed the influence of FB transparency on NF performance and the role of agency in this relationship.Approach.Participants performed a NF task using MI to regulate brain activity measured using electroencephalography. In separate blocks, participants experienced three different conditions designed to vary transparency: FB was presented as either (1) a swinging pendulum, (2) a clenching virtual hand, (3) a clenching virtual hand combined with a motor illusion induced by tendon vibration. We measured self-reported agency and user experience after each NF block.Main results. We found that FB transparency influences NF performance. Transparent visual FB provided by the virtual hand resulted in significantly better NF performance than the abstract FB of the pendulum. Surprisingly, adding a motor illusion to the virtual hand significantly decreased performance relative to the virtual hand alone. When introduced in incremental linear mixed effect models, self-reported agency was significantly associated with NF performance and it captured the variance related to the effect of FB transparency on NF performance.Significance. Our results highlight the relevance of transparent FB in relation to the sense of agency. This is likely an important consideration in designing FB to improve NF performance and learning outcomes.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Investigation on Factors Affecting Cognitive Skills in Detection of Driving Fatigue

Road safety is a worldwide concern where a high number of fatalities and injuries are caused by traffic accidents. One major issue contributing to these incidents is driving while fatigued, which has been recognized as a contributing factor to accidents in many countries. Being tired while driving is a state that encompasses both physical and mental exhaustion and can greatly impair a person's ability to drive safely. Fatigue negatively affects a driver's cognitive abilities, resulting in decreased performance behind the wheel. The aim of this study is to analyse the effect of driving duration, body mass index (BMI), types of roads and gender factor in causing fatigue while driving. Cognitive skills of drivers while driving was measured through the electroencephalogram (EEG) signal gathered from Emotiv EPOC device to measure brain activity. Relationship between cognitive skills and fatigue signalling is then analysed using regression analysis in Design-Expert software. Results indicated that as mental fatigue increases, there is a noticeable decrease in the relative power of beta rhythm, supporting previous research findings. Results on correlation show that the longer the drivers drove, the wearier they become. Obese people experience more fatigue when driving than those with a normal or overweight BMI. Driving on straight roads resulted in greater fatigue compared to driving on winding ones, with female participants indicating higher levels of tiredness than male participants. These insights are crucial for road safety authorities to tailor their supervision methods based on individual characteristics and to design effective driver fatigue warnings.

Neuroimaging model of visceral manipulation in awake rat.

It is unclear to what extent measurements of brain activity are affected by background, and experimentally unrelated, interoceptive processes. To advance our understanding of ongoing visceral activity's influence on brain states, here we provide a proof of concept, anesthesia-free animal model of visceral manipulation during simultaneous BOLD fMRI. We successfully demonstrated BOLD activation during gastric distension of the unanesthetized rat in both classically reported (cerebellum, hippocampus) and novel (inferior colliculus) regions. This paradigm establishes an important foundation for further interrogation of viscera-brain interactions.

NinjaNIRS: an open hardware solution for wearable whole-head high-density functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) technology has been steadily advancing since the first measurements of human brain activity over 30 years ago. Initially, efforts were focused on increasing the channel count of fNIRS systems and then to moving from sparse to high density arrays of sources and detectors, enhancing spatial resolution through overlapping measurements. Over the last ten years, there have been rapid developments in wearable fNIRS systems that place the light sources and detectors on the head as opposed to the original approach of using fiber optics to deliver the light between the hardware and the head. The miniaturization of the electronics and increased computational power continues to permit impressive advances in wearable fNIRS systems. Here we detail our design for a wearable fNIRS system that covers the whole head of an adult human with a high-density array of 56 sources and up to 192 detectors. We provide characterization of the system showing that its performance is among the best in published systems. Additionally, we provide demonstrative images of brain activation during a ball squeezing task. We have released the hardware design to the public, with the hope that the community will build upon our foundational work and drive further advancements.

TMS-induced plasticity improving cognitive control in OCD II: Task-based neural predictors of treatment response

ObjectiveRepetitive transcranial magnetic stimulation (rTMS) has the potential to increase the clinical effect of exposure with response prevention (ERP) psychotherapy for obsessive-compulsive disorder (OCD). We investigated the use of task-based functional MRI (tb-fMRI) for predicting clinical outcomes to different rTMS protocols combined with ERP in OCD. Method61 adults with OCD underwent rTMS and ERP and were randomized to different high frequency rTMS conditions: left dorsolateral prefrontal cortex (DLPFC; n=19), left pre-supplementary motor area (preSMA; n=23), and control stimulation at the vertex at low intensity (n=19). The Tower of London task and Stop-Signal Task were used to assess pretreatment activation during planning and inhibitory control, respectively. We adopted a Bayesian region-based approach to test whether clinical improvement can be predicted by tb-fMRI-derived measures of task-related brain activation or functional connectivity between task-relevant regions and the bilateral amygdala. ResultsFor the vertex group, but not the DLPFC/preSMA rTMS conditions, higher activation in several task-relevant regions during planning and response inhibition, and lower error-related activation, corresponded with better short-term clinical improvement. Lower precuneus activation with increased planning taskload correlated with symptom reduction in the DLPFC group. In the preSMA group, higher error-related activation and lower inhibition-related insular-amygdalar connectivity was associated with symptom reduction. ConclusionsPretreatment tb-fMRI-derived measures of activation and connectivity during planning and inhibition-related processes are associated with clinical response for specific rTMS conditions in OCD. Future placebo-controlled trials with larger sample sizes should combine clinical information and neural correlates to improve prediction of clinical outcome.

Homogenization of word relationships in schizophrenia: Topological analysis of cortical semantic representations.

Patients with schizophrenia typically exhibit symptoms of disorganized thought and display concreteness and over-inclusion in verbal reports, depending on the level of abstraction. While concreteness and over-inclusion may appear contradictory, the underlying psychopathology that explains these symptoms remains unclear. In the current study, we used functional magnetic resonance imaging with an encoding modeling approach to examine how concepts of various words, represented as brain activity, are anomalously connected at different levels of abstraction in patients with schizophrenia. Fourteen individuals diagnosed with schizophrenia and 17 healthy controls underwent functional magnetic resonance imaging to measure brain activity representing concepts of various words. We used a persistent homology (PH) method to analyze the topological structures of word representations in schizophrenia patients, healthy controls, and random data, across different levels of abstraction by varying dissimilarity scales in the representation space. The results revealed that patients with schizophrenia exhibited more homogeneous word relationships across different levels of abstraction compared with healthy controls. Additionally, topological structures exhibited a shift toward a random network structure in patients with schizophrenia compared with controls. The PH method successfully distinguished semantic representations of patients with schizophrenia from those of controls. The current results provide an explanation for the mechanisms underlying the deficits in abstraction ability observed in schizophrenia. The isotopic connection of individual concepts reflects both the reduction of contextual connections at a semantically fine-grained scale and the absence of clear boundaries between related concepts at a coarse scale, which lead to concreteness and over-inclusion, respectively.

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Electroencephalography (EEG) is commonly used to measure brain activity in clinical research. However, the abundance of channels in EEG data poses several challenges, including increased computational complexity, noise interference, and decreased efficiency in data analysis. A new graph-based method called G-EEGCS has been developed to address these issues for EEG channel selection. The graph-based optimum EEG channel selection (G-EEGCS) method constructs a directed network by establishing channel connections based on their pairwise correlations. Statistical features are used to determine similarity, and an adjacency matrix is created to represent the connectivity between the EEG channels. The influence of each channel on information flow within the network is assessed using the centrality measures. By calculating the shortest paths between all channel pairs, the algorithm quantifies the probability of each channel serving as a bridge between different parts of the graph. Channels with high centrality scores, indicating their significance in the information flow, are given priority during the selection process. An adaptive threshold is applied to optimize channel selection, ensuring that only channels that exceed the threshold, exhibit specific characteristics, and align with the overall network structure are retained. This adaptive thresholding mechanism enhances the robustness and flexibility of the G-EEGCS method, enabling personalized channel selection tailored to individual EEG datasets. The G-EEGCS approach offers a promising solution for EEG channel selection, improving the interpretability and efficiency of EEG-based studies and applications for researchers. It achieved an average accuracy of 0.9125, outperforming state-of-the-art methods. This method provides a valuable means of addressing the challenges posed by the multitude of channels in EEG data, ultimately contributing to more effective and insightful analysis in clinical research.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.MethodsWe used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.ResultsWe found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.LimitationsOptical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.ConclusionsThis study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Whole-brain mapping in adult zebrafish and identification of a novel tank test functional connectome.

Identifying general principles of brain function requires the study of structure-function relationships in a variety of species. Zebrafish have recently gained prominence as a model organism in neuroscience, yielding important insights into vertebrate brain function. Although methods have been developed for mapping neural activity in larval animals, we lack similar techniques for adult zebrafish that have the advantage of a fully developed neuroanatomy and larger behavioral repertoire. Here, we describe a pipeline built around open-source tools for whole-brain activity mapping in freely swimming adult zebrafish. Our pipeline combines recent advances in histology, microscopy, and machine learning to capture cfos activity across the entirety of the adult brain. Images captured using light-sheet microscopy are registered to the recently created adult zebrafish brain atlas (AZBA) for automated segmentation using advanced normalization tools (ANTs). We used our pipeline to measure brain activity after zebrafish were subject to the novel tank test. We found that cfos levels peaked 15 minutes following behavior and that several regions containing serotoninergic, dopaminergic, noradrenergic, and cholinergic neurons were active during exploration. Finally, we generated a novel tank test functional connectome. Functional network analysis revealed that several regions of the medial ventral telencephalon form a cohesive sub-network during exploration. We also found that the anterior portion of the parvocellular preoptic nucleus (PPa) serves as a key connection between the ventral telencephalon and many other parts of the brain. Taken together, our work enables whole-brain activity mapping in adult zebrafish for the first time while providing insight into neural basis for the novel tank test.

Immediate and Transient Perturbances in EEG Within Seconds Following Controlled Soccer Head Impact.

Athletes in contact and collision sports can sustain frequent subconcussive head impacts. Although most impacts exhibit low kinematics around or below 10g of head linear acceleration, there is growing concern regarding the cumulative effects of repetitive sports head impacts. Even mild impacts can lead to brain deformations as shown through neuroimaging and finite element modeling, and thus may result in mild and transient effects on the brain, prompting further investigations of the biomechanical dose-brain response relationship. Here we report findings from a novel laboratory study with continuous monitoring of brain activity through electroencephalography (EEG) during controlled soccer head impacts. Eight healthy participants performed simulated soccer headers at 2 mild levels (6g, 4rad/s and 10g, 8rad/s) and three directions (frontal, oblique left, oblique right). Participants were instrumented with an inertial measurement unit (IMU) bite bar and EEG electrodes for synchronized head kinematics and brain activity measurements throughout the experiment. After an impact, EEG exhibited statistically significant elevation of relative and absolute delta power that recovered within two seconds from the impact moment. These changes were statistically significantly higher for 10g impacts compared with 6g impacts in some topographical regions, and oblique impacts resulted in contralateral delta power increases. Post-session resting state measurements did not indicate any cumulative effects. Our findings suggest that even mild soccer head impacts could lead to immediate, transient neurophysiological changes. This study paves the way for further dose-response studies to investigate the cumulative effects of mild sports head impacts, with implications for long-term athlete brain health.