Neuroscientific Investigations Research Articles

Saccade size predicts onset time of object processing during visual search of an open world virtual environment

ObjectiveTo date the vast majority of research in the visual neurosciences have been forced to adopt a highly constrained perspective of the vision system in which stimuli are processed in an open-loop reactive fashion (i.e., abrupt stimulus presentation followed by an evoked neural response). While such constraints enable high construct validity for neuroscientific investigation, the primary outcomes have been a reductionistic approach to isolate the component processes of visual perception. In electrophysiology, of the many neural processes studied under this rubric, the most well-known is, arguably, the P300 evoked response. There is, however, relatively little known about the real-world corollary of this component in free-viewing paradigms where visual stimuli are connected to neural function in a closed-loop. While growing evidence suggests that neural activity analogous to the P300 does occur in such paradigms, it is an open question when this response occurs and what behavioral or environmental factors could be used to isolate this component. ApproachThe current work uses convolutional networks to decode neural signals during a free-viewing visual search task in a closed-loop paradigm within an open-world virtual environment. From the decoded activity we construct fixation-locked response profiles that enable estimations of the variable latency of any P300 analogue around the moment of fixation. We then use these estimates to investigate which factors best reduce variable latency and, thus, predict the onset time of the response. We consider measurable, search-related factors encompassing top-down (i.e., goal driven) and bottom-up (i.e., stimulus driven) processes, such as fixation duration and salience. We also consider saccade size as an intermediate factor reflecting the integration of these two systems. Main resultsThe results show that of these factors only saccade size reliably determines the onset time of P300 analogous activity for this task. Specifically, we find that for large saccades the variability in response onset is small enough to enable analysis using traditional ensemble averaging methods. SignificanceThe results show that P300 analogous activity does occur during closed-loop, free-viewing visual search while highlighting distinct differences between the open-loop version of this response and its real-world analogue. The results also further establish saccades, and saccade size, as a key factor in real-world visual processing.

Decoding dynamic visual scenes across the brain hierarchy.

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

Undifferentiated versus retinoic acid - differentiated SH-SY5Y cells in investigation of markers of neural function in toxicological research

The SH-SY5Y human neuroblastoma cell line is a standard in vitro experimental model of neuronal-like cells used in neuroscience and toxicological research. These cells can be differentiated into mature neurons, most commonly using retinoic acid (RA). Despite differences in characteristics, both undifferentiated and differentiated SH-SY5Y cells are used in research. However, due to uncertainties regarding the expression of specific markers of neural function in each culture, there is no definite conclusion on which culture is better suited for (neuro)toxicological and/or neuroscience investigations. To address this dilemma, we investigated the basal expression/activity of the key elements of acetylcholine, dopamine, serotonin and GABA neurotransmitter pathways, along with the elements involved in exocytosis of neurotransmitters, and neuron electrophysiological activity in undifferentiated and in RA–differentiated SH-SY5Y cells using a 6-day differentiation protocol. Our findings revealed that both SH-SY5Y cell types are functionally active. While undifferentiated SH-SY5Y cells exhibited greater multipotency in the expression of tested markers, most of those markers expressed in both cell types showed higher expression levels in RA-differentiated SH-SY5Y cells. Our results suggest that the 6-day differentiation protocol with RA induces maturation, but not differentiation of the cells into specific neuron phenotype. The greater multipotency of undifferentiated cells in neural markers expression, together with their higher sensitivity to xenobiotic exposure and more simple cultivation protocols, make them a better candidate for high throughput toxicological screenings. Differentiated neurons are better suited for neuroscience researches that require higher expression of more specific neural markers and the specific types of neural cells.

Graphene oxide electrodes enable electrical stimulation of distinct calcium signalling in brain astrocytes

Astrocytes are responsible for maintaining homoeostasis and cognitive functions through calcium signalling, a process that is altered in brain diseases. Current bioelectronic tools are designed to study neurons and are not suitable for controlling calcium signals in astrocytes. Here, we show that electrical stimulation of astrocytes using electrodes coated with graphene oxide and reduced graphene oxide induces respectively a slow response to calcium, mediated by external calcium influx, and a sharp one, exclusively due to calcium release from intracellular stores. Our results suggest that the different conductivities of the substrate influence the electric field at the cell–electrolyte or cell–material interfaces, favouring different signalling events in vitro and ex vivo. Patch-clamp, voltage-sensitive dye and calcium imaging data support the proposed model. In summary, we provide evidence of a simple tool to selectively control distinct calcium signals in brain astrocytes for straightforward investigations in neuroscience and bioelectronic medicine.

The influence of app-based mindfulness training on the dorsolateral prefrontal cortex during an attentional control task: A fNIRS driven pilot study

Mindfulness-based interventions (MBIs) have been shown to be effective in treating psychological disturbances such as depression and anxiety. However the neural mechanisms underpinning MBIs, remain unclear. While studies employing functional magnetic resonance imaging (fMRI) and electroencephalography have advanced our understanding of neural correlates influenced by mindfulness, these modalities have notable limitations that restrict their applicability across various demographics. This study explored the feasibility of functional near-infrared spectroscopy (fNIRS) as a viable alternative for assessing neurocognitive changes induced by MBIs. To do this, fNIRS data was collected from 19 university students during a modified flanker task, before and after a week-long, app-based mindfulness intervention. Post intervention, participants improved their ability to inhibit pre-potent responses to stimuli after a rule change. While no significant pre-post changes in cortical activation of the DLPFC were observed, participants who performed better (i.e., better response accuracy) required less modulation from the DLPFC. These results continue to establish the use of fNIRS as an effective tool for understanding neurocognitive underpinnings of mindfulness. As a less invasive, cost-efficient modality, fNIRS shows promise in expanding neuroscientific investigation into understudied samples that may not be well suited for traditional methods such as fMRI. Ultimately, this could lead to more effective mindfulness interventions that have already shown promise in treating a range of physical and psychological disorders.

Toward a Unified Account of Advanced Concentrative Absorption Meditation: A Systematic Definition and Classification of Jhāna

ObjectivesThe jhānas are series of advanced concentrative absorption meditative (ACAM) states brought about by meditation. While previously cultivated mostly in monastic settings, a series of modern meditation manuals both openly discuss the jhānas and describe how they may be attained by laypeople. Simultaneously, the phenomenological and neuroscientific investigation of the jhānas is advancing. Although the descriptions of the jhānas in contemporary jhāna manuals to some degree overlap with one another, there are also significant disagreements. Here our objectives are to identify common features of jhāna in meditation manuals, to identify and discuss disagreements, and to offer an account of jhāna that is both comprehensive and consistent.MethodsA literature search and review was conducted. The search resulted in eight meditation manuals that provide phenomenologically rich accounts of the full range of the jhānas. Common characteristics and discrepancies in the different accounts of the jhānas have been identified by comparing terms and descriptions.ResultsWe offer a general definition of jhāna, distinguish three types of jhāna, and investigate phenomenological dimensions that may be necessary to address in order to comprehensively articulate the experience of ACAM.ConclusionsTo ensure scientific rigor, the study of jhāna needs to be informed by both commonalities and differences in the existing accounts of jhāna. While differences exist, these may also be systematically unified in a way that yields a comprehensive account of ACAM.

Therapeutic doses of ketamine acutely attenuate the aversive effect of losses during decision-making.

The discovery of rapid-acting antidepressant, ketamine has opened a pathway to a new generation of treatments for depression, and inspired neuroscientific investigation based on a new perspective that non-adaptive changes in the intrinsic excitatory and inhibitory circuitry might underlie the pathophysiology of depression. Nevertheless, it still remains largely unknown how the hypothesized molecular and synaptic levels of changes in the circuitry might mediate behavioral and neuropsychological changes underlying depression, and how ketamine might restore adaptive behavior. Here, we used computational models to analyze behavioral changes induced by therapeutic doses of ketamine, while rhesus macaques were iteratively making decisions based on gains and losses of tokens. When administered intramuscularly or intranasally, ketamine reduced the aversiveness of undesirable outcomes such as losses of tokens without significantly affecting the evaluation of gains, behavioral perseveration, motivation, and other cognitive aspects of learning such as temporal credit assignment and time scales of choice and outcome memory. Ketamine's potentially antidepressant effect was separable from other side effects such as fixation errors, which unlike outcome evaluation, was readily countered with strong motivation to avoid errors. We discuss how the acute effect of ketamine to reduce the initial impact of negative events could potentially mediate longer-term antidepressant effects through mitigating the cumulative effect of those events produced by slowly decaying memory, and how the disruption-resistant affective memory might pose challenges in treating depression. Our study also invites future investigations on ketamine's antidepressant action over diverse mood states and with affective events exerting their impacts at diverse time scales.

Therapeutic doses of ketamine acutely attenuate the aversive effect of losses during decision-making

The discovery of rapid-acting antidepressant, ketamine has opened a pathway to a new generation of treatments for depression, and inspired neuroscientific investigation based on a new perspective that non-adaptive changes in the intrinsic excitatory and inhibitory circuitry might underlie the pathophysiology of depression. Nevertheless, it still remains largely unknown how the hypothesized molecular and synaptic levels of changes in the circuitry might mediate behavioral and neuropsychological changes underlying depression, and how ketamine might restore adaptive behavior. Here, we used computational models to analyze behavioral changes induced by therapeutic doses of ketamine, while rhesus macaques were iteratively making decisions based on gains and losses of tokens. When administered intramuscularly or intranasally, ketamine reduced the aversiveness of undesirable outcomes such as losses of tokens without significantly affecting the evaluation of gains, behavioral perseveration, motivation, and other cognitive aspects of learning such as temporal credit assignment and time scales of choice and outcome memory. Ketamine’s potentially antidepressant effect was separable from other side effects such as fixation errors, which unlike outcome evaluation, was readily countered with strong motivation to avoid errors. We discuss how the acute effect of ketamine to reduce the initial impact of negative events could potentially mediate longer-term antidepressant effects through mitigating the cumulative effect of those events produced by slowly decaying memory, and how the disruption-resistant affective memory might pose challenges in treating depression. Our study also invites future investigations on ketamine’s antidepressant action over diverse mood states and with affective events exerting their impacts at diverse time scales.

Unlocking near-whole-brain, layer-specific functional connectivity with 3D VAPER fMRI

Abstract Neuroscientific investigations at the cortical layer level not only enrich our knowledge of cortical micro-circuitry in vivo, but also help bridge the gap between macroscopic (e.g., conventional fMRI, behavior) and microscopic (e.g., extracellular recordings) measures of brain function. While laminar fMRI studies have extensively explored the evoked cortical response in multiple subsystems, the investigation of the laminar component of functional networks throughout the entire brain has been hindered due to constraints in high-resolution layer-fMRI imaging methodologies. Our study addresses this gap by introducing an innovative layer-specific 3D VAPER (integrated VASO and Perfusion contrast) technique in humans at 7 T, for achieving fMRI at high resolution (800 µm isotropic), high specificity (not biased toward unspecific vein signals as BOLD), high sensitivity (robust measurement at submillimeter resolution), high spatial accuracy (analysis in native fMRI space), near-whole-brain coverage (cerebellum not included), and eventually extending layer fMRI to more flexible connectivity-based experiment designs. To demonstrate its effectiveness, we collected 0.8-mm isotropic fMRI data during both resting-state and movie-watching scenarios, established a layer-specific functional connectivity analysis pipeline from individual to group levels, and explored the role of different cortical layers in maintaining functional networks. Our results revealed distinct layer-specific connectivity patterns within the default mode, somatomotor, and visual networks, as well as at the global hubness level. The cutting-edge technique and insights derived from our exploration into near-whole-brain layer-specific connectivity provide unparalleled understanding of the organization principles and underlying mechanisms governing communication between different brain regions.

Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.

Structural-functional brain network coupling predicts human cognitive ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Objective. Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain–machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Approach. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior. Main results. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson–Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior. Significance. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

Current and Future Approaches to Pediatric Anxiety Disorder Treatment.

This overview critically appraises the literature on the treatment of pediatric anxiety disorders. The two established treatments for these conditions comprise cognitive-behavioral therapy (CBT) and antidepressant medications. Many youths receiving these treatments fail to achieve remission, which creates a need for new treatments. After summarizing the literature on CBT and currently available medications, the authors describe research that lays a foundation for improvements in the treatment of pediatric anxiety disorders. This foundation leverages neuroscientific investigations, also described in the overview, which provide insights on mechanisms of successful treatment.

Placebo stimulates neuroplasticity in depression: implications for clinical practice and research.

Neither psychological nor neuroscientific investigations have been able to fully explain the paradox that placebo is designed to be inert in randomized controlled trials (RCTs), yet appears to be effective in evaluations of clinical interventions in all fields of medicine and alternative medicine. This article develops the Neuroplasticity Placebo Theory, which posits that neuroplasticity in fronto-limbic areas is the unifying factor in placebo response (seen in RCTs) and placebo effect (seen in clinical interventions) where it is not intended to be inert. Depression is the disorder that has the highest placebo response of any medical condition and has the greatest potential for understanding how placebos work: recent developments in understanding of the pathophysiology of depression suggest that fronto-limbic areas are sensitized in depression which is associated with a particularly strong placebo phenomenon. An innovative linkage is made between diverse areas of the psychology and the translational psychiatry literature to provide supportive evidence for the Neuroplasticity Placebo Theory. This is underpinned by neuro-radiological evidence of fronto-limbic change in the placebo arm of antidepressant trials. If placebo stimulates neuroplasticity in fronto-limbic areas in conditions other than depression - and results in a partially active treatment in other areas of medicine - there are far reaching consequences for the day-to-day use of placebo in clinical practice, the future design of RCTs in all clinical conditions, and existing unwarranted assertions about the efficacy of antidepressant medications. If fronto-limbic neuroplasticity is the common denominator in designating placebo as a partially active treatment, the terms placebo effect and placebo response should be replaced by the single term "placebo treatment."

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

The Pigeon as a Model of Complex Visual Processing and Category Learning.

Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon's category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon's visual pathway. Given the pigeon's availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.

Optimistic Stratagems of Machine Based Deep Learning Employed with Automation in Psychiatry and Early Neurodevelopmental Disorders

Conversely, the contents uplift the promise of machine learning at varied levels in neuroscience investigation to discover the working of neural network and brain functioning.

Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.

Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold. We show that the model, which we named 'DFINE' (for 'dynamical flexible inference for nonlinear embeddings') achieves flexible inference in simulations of nonlinear dynamics and across neural datasets representing a diversity of brain regions and behaviours. Compared with earlier neural-network models, DFINE enables flexible inference, better predicts neural activity and behaviour, and better captures the latent neural manifold structure. DFINE may advance the development of neurotechnology and investigations in neuroscience.

The Representation of Valence by Visual Category in the Orbitofrontal Cortex and Amygdala

Emotion is critical for survival in all animals. Even in modern humans, processing emotion holds significant adaptive value. In neuroscientific investigations of emotion, both the amygdala, part of the evolutionarily primitive limbic system, and the orbitofrontal cortex, part of the evolutionarily newer cortical system, have been highly implicated in the representation of valence, a major emotional component. Although the strict divide between the limbic system and the cortical system in brain function has been rejected, the underlying notions of theories of brain evolution lead to the hypothesis that the amygdala is advantageous in processing evolutionarily older adaptive value whereas the orbitofrontal cortex is advantageous in processing evolutionarily newer adaptive value. In this study, this hypothesis is tested using a functional magnetic resonance imaging dataset in which human participants made valence judgments on natural items and scenes (i.e., evolutionarily old with greater immediate adaptive value) and man-made items and scenes (i.e., evolutionarily new with lesser immediate adaptive value). The results show that the amygdala represents valence of natural but not man-made scenes, consistent with the hypothesis. The orbitofrontal cortex, in contrast, represents valence of both natural and man-made scenes, partially consistent with the hypothesis. The findings illustrate how visual categories defined by adaptive value shape the neural representation of valence in the contrasting limbic and cortical systems.

Advances in Ultrafast Fiber Lasers for Multiphoton Microscopy in Neuroscience

Multiphoton microscopy (MPM) has emerged as a vital tool in neuroscience, enabling deeper imaging with a broader field of view, as well as faster and sub-cellular resolution. Recent innovations in ultrafast fiber laser technology have revolutionized MPM applications in living brains, offering advantages like cost-effectiveness and user-friendliness. In this review, we explore the progress in ultrafast fiber laser technology, focusing on its integration into MPM for neuroscience research. We also examine the utility of femtosecond fiber lasers in fluorescence and label-free two- and three-photon microscopy applications within the field. Furthermore, we delve into future possibilities, including next-generation fiber laser designs, novel laser characteristics, and their potential for achieving high spatial and temporal resolution imaging. We also discuss the integration of fiber lasers with implanted microscopes, opening doors for clinical and fundamental neuroscience investigations.