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Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system's nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces. [ABSTRACT FROM AUTHOR]

The proper preservation of human brain tissue is an indispensable requirement for post-mortem investigations. Neuroanatomical teaching, neuropathological examination, neurosurgical training, basic and clinical neuroscientific research are some of the possible downstream applications of brain specimens and, although much apart from one another, proper tissue fixation and preservation is a common denominator to all of them. In this review, the most relevant procedures to fixate brain tissue are described. In situ and immersion fixation approaches have been so far the most widespread ways to deliver the fixatives inside the skull. Although most of them rely on the use of formalin, alternative fixative solutions containing lower amounts of this compound mixed with other preservative agents, have been attempted. The combination of fixation and freezing paved the way for fiber dissection, particularly relevant for the neurosurgical practice and clinical neuroscience. Moreover, special techniques have been developed in neuropathology to tackle extraordinary problems, such as the examination of highly infective specimens, as in the case of the Creutzfeldt-Jakob encephalopathy, or fetal brains. Fixation is a fundamental prerequisite for further staining of brain specimens. Although several staining techniques have been developed for the microscopical investigation of the central nervous system, numerous approaches are also available for staining macroscopic brain specimens. They are mostly relevant for neuroanatomical and neuropathological teaching and can be divided in white and gray matter staining techniques. Altogether, brain fixation and staining techniques are rooted in the origins of neuroscience and continue to arouse interest in both preclinical and clinical neuroscientists also nowadays. [ABSTRACT FROM AUTHOR]

Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior–posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience. Analysis of resting-state fMRI data reveals the presence of functional connectivity gradients of the human cingulate cortex, which updates our understanding of cingulate heterogeneity. [ABSTRACT FROM AUTHOR]

Research and practice in clinical neurosciences often involve cognitive assessment. However, this has traditionally used a nomothetic approach, comparing the performance of patients to normative samples. This method of defining abnormality places the average test performance of neurologically healthy individuals at its center. However, evidence suggests that neurological 'abnormalities' are very common, as is the diversity of cognitive abilities. The veneration of central tendency in cognitive assessment, i.e., equating typicality with healthy or ideal, is, I argue, misguided on neurodiversity, bio-evolutionary, and cognitive neuroscientific grounds. Furthermore, the use of average performance as an anchor point for normal performance is unreliable in practice and frequently leads to the mischaracterization of cognitive impairments. Examples are explored of how individuals who are already vulnerable for socioeconomic reasons can easily be over-pathologized. At a practical level, by valuing diversity rather than typicality, cognitive assessments can become more idiographic and focused on change at the level of the individual. The use of existing methods that approach cognitive assessment ideographically is briefly discussed, including premorbid estimation methods and informant reports. Moving the focus away from averageness to valuing diversity for both clinical cognitive assessments and inclusion of diverse groups in research is, I argue, a more just and effective way forward for clinical neurosciences. [ABSTRACT FROM AUTHOR]

A compelling way to disentangle the complexity of the brain is to measure the effects of spatially and temporally synchronized systematic perturbations. In humans, this can be non-invasively achieved by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Spatiotemporally complex and long-lasting TMS-EEG evoked potential (TEP) waveforms are believed to result from recurrent, re-entrant activity that propagates broadly across multiple cortical and subcortical regions, dispersing from and later re-converging on, the primary stimulation site. However, if we loosely understand the TEP of a TMS-stimulated region as the impulse response function of a noisy underdamped harmonic oscillator, then multiple later activity components (waveform peaks) should be expected even for an isolated network node in the complete absence of recurrent inputs. Thus emerges a critically important question for basic and clinical research on human brain dynamics: what parts of the TEP are due to purely local dynamics, what parts are due to reverberant, re-entrant network activity, and how can we distinguish between the two? To disentangle this, we used source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from 100 ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics. The novel discoveries and new software technologies introduced here should be of broad utility in basic and clinical neuroscience research. [ABSTRACT FROM AUTHOR]

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders. [ABSTRACT FROM AUTHOR]

The direct link between neuropathology and the symptoms that emerge from damage to the brain is often difficult to discern. In this perspective, we argue that a satisfying account of neurodegenerative symptoms most naturally emerges from the consideration of the brain from the systems-level. Specifically, we will highlight the role of the neuromodulatory arousal system, which is uniquely positioned to coordinate the brain's ability to flexibly integrate the otherwise segregated structures required to support higher cognitive functions. Importantly, the neuromodulatory arousal system is highly heterogeneous, encompassing structures that are common sites of neurodegeneration across Alzheimer's and Parkinson's disease. We will review studies that implicate the dysfunctional interactions amongst distributed brain regions as a side-effect of pathological involvement of the neuromodulatory arousal system in these neurodegenerative disorders. From this perspective, we will argue that future work in clinical neuroscience should attempt to consider the inherent complexity in the brain and employ analytic techniques that do not solely focus on regional functional impairments, but rather captures the brain as an inherently dynamic, distributed, multi-scale system. Through this lens, we hope that we will devise new and improved diagnostic markers and interventional approaches to aid in the treatment of neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Implantable brain–computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials' contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

On behalf of the BraYn Association Ets, we are pleased to present the Abstracts of the Fifth Brainstorming Research Assembly for Young Neuroscientists, which was held in Rome, Italy from 28–30 September 2022. We congratulate all the presenters on their research work and contribution. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study is to analyze the multiple sclerosis research in Science Citation Index-Expanded from 1992 to 2019 in order to determine the top features, trends, and topics. Methods: In this scientometric study, research population was all articles related to multiple sclerosis in the Web of Science from 1992 to 2019. These articles were retrieved from the Science Citation Index-Expanded and analyzed based on various indicators and characteristics. Results: There was a significant increase in the number of articles from 408 in 1992 to 2756 in 2019. A sum of 42,112 articles related to multiple sclerosis has been published in 3,032 journals, which were classified among the 131 Web of Science categories. The top two categories were clinical neurology and neurosciences. Multiple Sclerosis Journal published the most articles. Articles published in the Neurology Journal also had the highest citation per publication. Most of the articles were in English, while 23 other languages were used in the articles. Articles in Japanese and English with 7 and 6.9 authors have a higher average of authors than articles in other languages. English language articles received more citations than articles in other languages. The frequency of used keywords in title, abstract, author keywords, and keywords plus analysis showed that "therapy", "disability", "neurodegeneration", "demyelination" and "MRI" show an increasing trend in the multiple sclerosis articles. Conclusion: The result of this study can help the policy makers and researchers realize the panorama of multiple sclerosis research and design future research. [ABSTRACT FROM AUTHOR]

Stroke is the leading cause of disability globally in need of novel and effective methods of rehabilitation. Intermittent theta burst stimulation (iTBS) has been adopted as a Level B recommendation for lower limb spasticity in guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS). Nonetheless, the methodological differences and deficits of existing work bring about heterogenous results and therefore limit the universal clinical use of rTMS in lower extremity (LE) rehabilitation. The variation of stimulated targets across motor cortex contributes mainly to these heterogeneities. This narrative review includes studies of rTMS on LE motor function rehabilitation in patients after stroke until now. Some analyses of brain imaging and electromagnetic simulation and quantification through computational modeling were also performed. rTMS appears capable of fostering LE motor rehabilitation after stroke, but the actually stimulated targets are considerably bias making it difficult to confirm effectiveness. The main reason for this phenomenon is probably inaccurate targeting of motor cortical leg representation. An underlying updated method is proposed as Individual‐Target TMS (IT‐TMS) combined with brain imaging. rTMS is a promising validated method for LE function regaining. Future studies should systematically compare the effects of IT‐TMS with traditional rTMS using large samples in random clinical trials. This article is categorized under:Neuroscience > Clinical Neuroscience [ABSTRACT FROM AUTHOR]

Background Brain age is a popular brain-based biomarker that offers a powerful strategy for using neuroscience in clinical practice. We investigated the brain-predicted age difference (PAD) in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging data. The association between brain-PAD and clinical parameters was also assessed. Methods We developed brain age prediction models for the association between 77 average structural brain measures and age in a training sample of controls (HCs) using ridge regression, support vector regression, and relevance vector regression. The trained models in the controls were applied to the test samples of the controls and 3 patient groups to obtain brain-based age estimates. The correlations were tested between the brain PAD and clinical measures in the patient groups. Results Model performance indicated that, regardless of the type of regression metric, the best model was support vector regression and the worst model was relevance vector regression for the training HCs. Accelerated brain aging was identified in patients with SCZ, FE-SSDs, and TRS compared with the HCs. A significant difference in brain PAD was observed between FE-SSDs and TRS using the ridge regression algorithm. Symptom severity, the Social and Occupational Functioning Assessment Scale, chlorpromazine equivalents, and cognitive function were correlated with the brain PAD in the patient groups. Conclusions These findings suggest additional progressive neuronal changes in the brain after SCZ onset. Therefore, pharmacological or psychosocial interventions targeting brain health should be developed and provided during the early course of SCZ. [ABSTRACT FROM AUTHOR]

This article discusses the relationship between menopause and bipolar disorder (BD) and highlights the need for further research in this area. The current body of literature on the topic is limited and lacks consistent definitions and standardized measures. The article emphasizes the importance of studying the postmenopausal phase (PM) independently, as it is characterized by hormonal stability and has unique effects on BD. The authors call for clear definitions, longitudinal studies, and the development of specific guidelines to improve the management of BD during PM. They believe that understanding the impact of PM on BD can lead to more effective treatments and personalized medicine approaches. [Extracted from the article]

This letter, titled "The Role of Neurosciences in Clinical Interviewing," emphasizes the value of the clinical interview in psychiatric treatment. The authors propose integrating neuroscience evidence into the clinical encounter to improve personalized psychiatry. They also highlight the importance of the emotional exchange between doctor and patient, which is not captured by Electronic Health Records. The letter suggests that emotions are not just surface-level experiences but are influenced by epigenetics and neuroimmune activities. The authors argue that understanding and addressing these emotional aspects are crucial for effective psychiatric treatment. [Extracted from the article]

Purpose: To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC). Methods: Axisymmetric DKI is more robust against noise‐induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal‐to‐noise ratios (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI?; second, is estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in‐vivo data focusing on white matter. Results: RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment. Conclusion: Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs (≈15$$ \approx 15 $$) in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open‐source ACID toolbox for SPM. [ABSTRACT FROM AUTHOR]

• Open-source Python library for graph-based EEG modeling using connectivity measures. • Includes several connectivity measures, related to time and frequency domains. • Allows flexibility to define specific parameters related to the analysis of EEGs. • Output is provided in two formats: data structures and graph visual representation. • Promotes the use of EEGs as a clinical test for the study of connectivity patterns. Connectivity studies make it possible to identify alterations in brain connections and to associate these pathologies with different neurological disorders. However, a clinical test is necessary to obtain information about the state of the brain. Electroencephalograms (EEGs) provide this information in addition to being tests with other benefits for the patient (non-invasive, low-cost, high reproducibility). Graph theory can be used to represent both the anatomical and functional connections of the brain by means of connectivity measures. The procedure of transforming an EEG into a graph can be slightly tedious for researchers, especially when implementing different connectivity measures. The open-source Python library EEGraph automatically performs the modeling of an EEG through a graph, providing its matrix and visual representation. It recognizes various EEG input formats, identifying the number of electrodes and the location of each electrode in the brain. Moreover, it allows the user to choose from 12 connectivity measures to produce the graph from the EEG, with great flexibility to define specific parameters to adapt them to each study, including EEG time-windows segmentation and separation in frequency bands. The EEGraph library is developed as a tool, for researchers and clinical specialists in the field of neuroscience, that provides direct information on the connectivity of the brain from electroencephalography signals. Its documentation and source code are available at https://github.com/ufvceiec/EEGRAPH. It can be installed from the Python Package Index using pip install EEGRAPH. The EEGraph library was built aiming to facilitate the development of connectivity studies based on the modeling of electroencephalography tests through graphs. It includes a wide range of connectivity measures, which, together with the multiple output options, make EEGraph an easy to use and powerful tool with direct applications in both the clinical and neuroscience research fields. [ABSTRACT FROM AUTHOR]

Immuno-positron emission tomography (immunoPET) is a non-invasive in vivo imaging method based on tracking and quantifying radiolabeled monoclonal antibodies (mAbs) and other related molecules, such as antibody fragments, nanobodies, or affibodies. However, the success of immunoPET in neuroimaging is limited because intact antibodies cannot penetrate the blood–brain barrier (BBB). In neuro-oncology, immunoPET has been successfully applied to brain tumors because of the compromised BBB. Different strategies, such as changes in antibody properties, use of physiological mechanisms in the BBB, or induced changes to BBB permeability, have been developed to deliver antibodies to the brain. These approaches have recently started to be applied in preclinical central nervous system PET studies. Therefore, immunoPET could be a new approach for developing more specific PET probes directed to different brain targets. [ABSTRACT FROM AUTHOR]

Neuropsychology is the study of behavioural and cognitive deficits in patients with brain lesions, comparing task performance between healthy controls and those with brain damage to infer the link between brain anatomy and behaviour. The discipline's three most popular tools - association, dissociation, and double dissociation studies - have yielded a vast amount of valuable experimental and clinical insight for neuroscience's understanding of the brain-behaviour connection. Here, specific contributions from each type of study are discussed in relation to the diagnosis and treatment of three neuropsychological impairments - Bálint's syndrome, visual form agnosia, and aphasia. The inherent limitations of neuropsychology as a discipline are considered, including the problem of low generalizability, the debatable fractionation assumption, poor temporal resolution and its relative obsoleteness in comparison to contemporary brain imaging techniques. It is concluded that the combined approach of converging traditional neuropsychological techniques with modern neuroimaging methods, is most efficient at providing insight on brain function and localisation, leading to brain-behaviour conclusions that would be impossible to discover using solely patient studies. [ABSTRACT FROM AUTHOR]

Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on "big data" and new hypothesis-free experimental models. In this Special Issue, we set the focus on informatics-supported interdisciplinary neuroscience accomplishments symmetrically combining wet-lab and clinical routines. Video-tracking and automated mitosis detection in vitro, the macromolecular modeling of kinesin motion, and the unsupervised classification of the brain's macrophage activation status share a common denominator: they are energized by machine and deep learning. Essential clinical neuroscience questions such as the estimated risk of brain aneurysm rupture and the surgical outcome of facial nerve transplantation are addressed in this issue as well. Precise and rapid evaluation of complex clinical data by deep learning and data mining dives deep to reveal symmetrical and asymmetrical features beyond the abilities of human perception or the limits of linear algebraic modeling. This editorial opts to motivate researchers from the wet lab, computer science, and clinical environments to join forces in reshaping scientific platforms, share and converge high-quality data on public platforms, and use informatics to facilitate interdisciplinary information exchange. [ABSTRACT FROM AUTHOR]

Purpose: Research on the effects of acupuncture on autonomic function has been conducted for several decades, and a few notable studies have emerged in recent years. This study used bibliometric analysis to assess 100 top-cited articles to characterize the current status and research trends over the last three decades. Methods: The 100 top-cited publications were identified from the Web of Science Core Collection database. The bibliometrix package in R was used for quantitative and qualitative analyses of the publication patterns and the country/region, institution, and author contributions. VOSviewer was used to construct networks based on co-citation analysis of the journals and the keyword co-occurrence. Results: The 100 top-cited articles were identified with a total of 8,123 citations (range: 37-345). The majority of the articles came from the USA (n = 42), followed by Japan (n = 14) and mainland China (n = 13). Articles from the USA exhibited the largest number of citations (3,582 citations), followed by articles from Japan (1,189 citations), then articles from mainland China (755 citations). Neurosciences/Neurology was the most studied research area (n = 41). The Autonomic Neuroscience: Basic and Clinical published the largest number of papers (n = 14), while Brain Research received the largest number of citations (205 citations). Longhurst JC was the most productive author (10 publications), and Sato A was first among the cited authors (87 citations). The most frequently cited articles that focused on gastrointestinal, cardiovascular, or gynecologic responses to acupuncture regulation of the autonomic nervous system first appeared in the 1990s, peaked in the 2000s, then decreased after 2010. Publication of articles focused on the antiinflammatory effects of acupuncture associated with autonomic function demonstrated an increasing trend over the last three decades. Conclusion: From the initial studies focusing on the autonomic mechanism of visceral responses to acupuncture, researchers concentrated on exploring the autonomic mechanism of acupuncture in the control of systemic inflammation. Non-invasive electrical methods that activate somatoautonomic reflexes are current translational directions in clinical practice. Additional investigation of the underlying neuroanatomical basis of somatoautonomic reflexes also is needed. [ABSTRACT FROM AUTHOR]

The low‐frequency (<0.1 Hz) fluctuation in sustained attention attracts enormous interest in cognitive neuroscience and clinical research since it always leads to cognitive and behavioral lapses. What is the source of the spontaneous fluctuation in sustained attention in neural activity, and how does the neural fluctuation relate to behavioral fluctuation? Here, we address these questions by collecting and analyzing two independent fMRI and behavior datasets. We show that the neural (fMRI) fluctuation in a key brain network, the default‐mode network (DMN), mediate behavioral (reaction time) fluctuation during sustained attention. DMN shows the increased amplitude of fluctuation, which correlates with the behavioral fluctuation in a similar frequency range (0.01–0.1 Hz) but not in the lower (<0.01 Hz) or higher (>0.1 Hz) frequency range. This was observed during both auditory and visual sustained attention and was replicable across independent datasets. These results provide a novel insight into the neural source of attention‐fluctuation and extend the former concept that DMN was deactivated in cognitive tasks. More generally, our findings highlight the temporal dynamic of the brain–behavior relationship. [ABSTRACT FROM AUTHOR]

Several models have been proposed for the emergence and maintenance of obsessive-compulsive disorder (OCD). Although these models have provided important insights and inspired treatment development, no single model has yet sufficiently accounted for the complexed phenotype of the disorder. In the current paper, we propose a novel model that integrates elements from cognitive behavioral models of OCD with neurocognitive approaches to the disorder. This Reciprocal Interaction Model (RIM) for OCD is based on two assumptions: (a) similar observed symptoms can stem from different etiological processes; and (b) neuropsychological deficits (such as reduced response inhibition and overreliance on the habit formation system) and cognitive behavioral processes (such as temporary reduction in anxiety after engaging in compulsive behaviors) mutually affect each other such that abnormalities in one system influence the second system and vice-versa—creating a vicious cycle of pathological processes. Indeed, the bidirectional inhibitory connection between anxiety/obsessions and executive control is at the heart of the model. We begin by briefly reviewing the current models for OCD. We then move on to describe the RIM, the supporting evidence for the model, the model's predictions, and potential clinical implications. [ABSTRACT FROM AUTHOR]

Introduction: Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes. Methods: We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS. Results: No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation. Discussion: Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations. [ABSTRACT FROM AUTHOR]

In many areas of science, like neuroscience, genomics and text mining, several important and challenging research questions imply the study of (subject) heterogeneity present in three-way data. In clinical neuroscience, for example, disclosing differences or heterogeneity between subjects in resting state networks (RSNs) underlying multi-subject fMRI data (i.e., time by voxel by subject three-way data) may advance the subtyping of psychiatric and mental diseases. Recently, the Clusterwise Independent Component Analysis (C-ICA) method was proposed that enables the disclosure of heterogeneity between subjects in RSNs that is present in multi-subject rs-fMRI data [1]. Up to now, however, no publicly available software exists that allows to fit C-ICA to empirical data at hand. The goal of this paper, therefore, is to present the CICA R package, which contains the necessary functions to estimate the C-ICA parameters and to interpret and visualize the analysis output. Further, the package also includes functions to select suitable initial values for the C-ICA model parameters and to determine the optimal number of clusters and components for a given empirical data set (i.e., model selection). The use of the main functions of the package is discussed and demonstrated with simulated data. Herewith, the necessary analytical choices that have to be made by the user (e.g., starting values) are explained and showed step by step. The rich functionality of the package is further illustrated by applying C-ICA to empirical rs-fMRI data from a group of Alzheimer patients and elderly control subjects and to multi-country stock market data. Finally, extensions regarding the C-ICA algorithm and procedures for model selection that could be implemented in future releases of the package are discussed. [ABSTRACT FROM AUTHOR]

• A pioneering wireless optically pumped magnetometer (OPM) MEG prototype is developed and evaluated. • Wireless OPM-MEG is achieved with significantly reduced magnetization in electronics and an optimized communication protocol. • The performance of our wireless OPM-MEG is comparable to that of wired systems. • Wireless OPM-MEG simplifies measurement and enables flexible experimental designs for wearable neuroimaging. The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) is a noninvasive imaging technique used in neuroscience and clinical research. The source estimation of MEG involves solving a highly underdetermined inverse problem, which requires additional constraints to restrict the solution space. Traditional methods tend to obscure the extent of the sources. However, an accurate estimation of the source extent is important for studying brain activity or preoperatively estimating pathogenic regions. To improve the estimation accuracy of the extended source extent, the spatial constraint of sources is employed in the Bayesian framework. For example, the source is decomposed into a linear combination of validated spatial basis functions, which is proved to improve the source imaging accuracy. In this work, we further construct the spatial properties of the source using the diagonal covariance bases (DCB), which we summarize as the source imaging method SI-DCB. In this approach, specifically, the covariance matrix of the spatial coefficients is modeled as a weighted combination of diagonal covariance basis functions. The convex analysis is used to estimate noise and model parameters under the Bayesian framework. Extensive numerical simulations showed that SI-DCB outperformed five benchmark methods in accurately estimating the location and extent of patch sources. The effectiveness of SI-DCB was verified through somatosensory stimulation experiments performed on a 31-channel OPM-MEG system. The SI-DCB correctly identified the source area where each brain response occurred. The superior performance of SI-DCB suggests that it can provide a template approach for improving the accuracy of source extent estimations under a sparse Bayesian framework. • A MEG source imaging method called SI-DCB is used for estimating the source. • Model covariance matrix of spatial coefficients by neighborhood DCBs. • The SI-DCB algorithm outperformed in terms of source location and extent. • The SI-DCB algorithm correctly localized the OPM-MEG measured somatosensory activities. [ABSTRACT FROM AUTHOR]

This document is a response to a previous study on the optimal dose of vortioxetine for depression. The authors clarify that their study focused on exploring the dose-response relationship of vortioxetine and did not identify the optimal therapeutic dose or strongly recommend increasing the dose. Their dose-response curve showed that efficacy increased linearly up to 5 mg/day, slowed between 5 and 10 mg/day, and then continued to rise linearly from 10 to 20 mg/day. While higher doses may enhance therapeutic outcomes, further research is needed to determine the balance between efficacy and risk at higher doses. [Extracted from the article]

This document is a correction notice for an article titled "Influence of preoperative embolisation on resection of brain arteriovenous malformations: cohort study" published in Acta Neurochirurgica. The correction states that the affiliation of the last author, Drahoslav Sokol, was missing in the original paper. The correct affiliation for Drahoslav Sokol is the Department of Clinical Neurosciences, Royal Infirmary of Edinburgh, Edinburgh, UK. The original article has been corrected to include this information. The publisher expresses regret for the error and emphasizes that Springer Nature remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

Cognitive neuroscience comes in many facets, and a particularly large branch of research is conducted in individuals with mental health problems. This article outlines why it is important that cognitive neuroscientists re-shape their role in mental health research and re-define directions of research for the next decades. At present, cognitive neuroscience research in mental health is too firmly rooted in categorial diagnostic definitions of mental health conditions. It is discussed why this hampers a mechanistic understanding of brain functions underlying mental health problems and why this is a problem for replicability in research. A possible solution to these problems is presented. This solution affects the strategy of research questions to be asked, how current trends to increase replicability in research can or cannot be applied in the mental health field and how data are analyzed. Of note, these aspects are not only relevant for the scientific process, but affect the societal view on mental disorders and the position of affected individuals as members of society, as well as the debate on the inclusion of so-called WEIRD and non-WEIRD people in studies. Accordingly, societal and science political aspects of re-defining the role of cognitive neuroscientists in mental health research are elaborated that will be important to shape cognitive neuroscience in mental health for the next decades. [ABSTRACT FROM AUTHOR]

Experimental neuroscience typically uses "p-valued" statistical testing procedures (null hypothesis significance testing; NHST) in evaluating its results. The rote, often misguided, application of NHST (Gigerenzer, 2008) has led to errors and "questionable research practices." Although the problems could be avoided with better statistics training (Lakens, 2021), there have been calls to abandon NHST altogether. One suggestion is to replace NHST with "estimation statistics" (Cumming and Calin-Jageman, 2017; Calin-Jageman and Cumming, 2019). Estimation statistics emphasizes the uncertainty inherent in scientific investigations and uses metrics, e.g., confidence intervals (CIs), that draw attention to uncertainty. Besides procedural steps and methods, the Estimation Approach prefers expressing "quantitative," rather than "qualitative" conclusions and making generalizations, rather than testing scientific hypotheses. The Estimation Approach embodies a philosophy of science--its ultimate goals, experimental mindset, and specific aims--that diverges unhelpfully from what laboratory-based neuroscience needs. The Estimation Approach meshes naturally with, e.g., clinical neuroscience, drug development, human psychology, and social sciences. It fits less well with much of the neuroscience published in the Journal of Neuroscience, for example. In contrast, the philosophy behind NHST fits naturally with traditional, evaluative testing of scientific hypotheses. Finally, some Estimation Approach remedies, e.g., replication, ideally with "preregistration," are incompatible with much experimental neuroscience. This Dual Perspective essay argues that, while neuroscience can benefit from practical aspects of estimation statistics, entirely replacing conventional methods with the Estimation Approach would be a mistake. NHST testing should be retained and improved. [ABSTRACT FROM AUTHOR]

Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy's Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions. [ABSTRACT FROM AUTHOR]

Almost all functional processing in the cortex strongly depends on thalamic interactions. However, in terms of functional interactions with the cerebral cortex, the human thalamus nuclei still partly constitute a terra incognita. Hence, for a deeper understanding of thalamic-cortical cooperation, it is essential to know how the different thalamic nuclei are associated with cortical networks. The present work examines network-specific connectivity and task-related topical mapping of cortical areas with the thalamus. The study finds that the relay and higher-order thalamic nuclei show an intertwined functional association with different cortical networks. In addition, the study indicates that relay-specific thalamic nuclei are not only involved with relay-specific behavior but also in higher-order functions. The study enriches our understanding of interactions between large-scale cortical networks and the thalamus, which may interest a broader audience in neuroscience and clinical research. Functional connectivity and topic mapping of cortical networks reveal that an overlapping of functions exist within different thalamic nuclei, providing insight into the role of relay and higher-order nuclei with the cortex. [ABSTRACT FROM AUTHOR]

Research suggests that people tend to overweight arguments accompanied by neuroscientific terminology, which is dubbed as the seductive allure of neuroscience explanations (SANE) in the literature. Such an effect might be of particular significance when it comes to physicians and mental health professionals (MHP), given that it has the potential to cause significant bias in their understanding as well as their treatment approaches toward psychiatric symptoms. In this study, we aimed to test the SANE effect among Turkish medical students, and assess its uniqueness by comparing it with a discipline that still maintains an important role in contemporary psychiatric training in Turkey: psychoanalysis. 109 medical students with a basic level of knowledge of psychiatry and clinical neuroscience were asked to rate the credibility of explanations of differing quality (good vs. circular) for psychological phenomena, followed by three types of information: none, neuroscientific (SNI) or psychoanalytical (SPI). Our findings showed that SNI significantly increased the judged quality of explanations for both conditions with the effect being more prominent for circular explanations. On the other hand, SPI had no effect on good explanations but enhanced the judged quality of circular explanations in a level comparable to that of SNI. For the first time, the SANE effect was replicated among medical students and provided preliminary data in favor of a similar effect for psychoanalytically oriented information. [ABSTRACT FROM AUTHOR]

Humans are unique in their ability to think about themselves and carry a more or less clear notion of who they are in their mind. Here we review recent evidence suggesting that the birth, maintenance, and loss of the abstract concept of 'self' is deeply tied to interoception, the sense of internal physiological signals. Interoception influences multiple facets of the self-concept, cutting across its material, social, moral, and agentive components. Overall, we argue that interoception contributes to the stability of the self-concept over time, unifying its layers and constraining the degree to which it is susceptible to external influences. Hence, the core features of the self-concept are those that correlate more with inner bodily states. We discuss the implications that this may have for theories of embodied cognition as well as for the understanding of psychiatric disorders in which the concept of self appears fragmented or loose. Finally, we formulate some empirical predictions that could be tested in future studies to shed further light on this emerging field. [ABSTRACT FROM AUTHOR]

Spike sorting – the process of separating spikes from different neurons – is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets. • A novel algorithm for spike sorting based on a Contractive Auto-encoder. • Produce representations of spike waveforms that are robust to additive noise. • Reliably classify spikes for small and large datasets. • Outperform SOTA approaches in various online and offline spike-sorting applications. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) classification of mental states is of important significance in many neuroscience and clinical applications. Existing classification algorithms use all signal-collected brain regions as a whole, and brain sub-region contributions have not been well investigated. This paper proposes a functional region decomposition (FRD) method to incorporate brain sub-region contributions and enhance fNIRS classification of mental states. Specifically, the method iteratively decomposes the brain region into multiple sub-regions to maximize their contributions with respect to the validation accuracy and coverage of brain sub-regions. Then for the fNIRS data in brain sub-regions, features are extracted and classified to output the predictions. The final predictions are determined by fusing predictions from multiple brain sub-regions with stacking. Experiments on a publicly available fNIRS dataset showed that the proposed functional region decomposition method led to 9.01% and 10.58% increase of classification accuracy for the methods related to slope-based features and mean concentration change features, respectively. Therefore, the proposed method can decompose the brain region into sub-regions with respect to their functional contributions and fundamentally enhance the performance of mental state classification. [ABSTRACT FROM AUTHOR]

In recent years clinical and neuroscience studies have highlighted some weaknesses of the current psychopathological categories included in neurodevelopmental disorders, both from a diagnostic and therapeutic point of view. In this context, the present work analyzes the dimensional and categorical aspects of Autism Spectrum Disorders (ASD) within the latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). If on the one hand the DSM-5 introduced the dimensional concept of "spectrum", on the other, the presence of "specifiers" makes it possible to categorize the variegated group of ASDs into more homogeneous subgroups. In fact, the specifiers, in addition to describing the phenotypic expression of the disorder and being predictive indices of the clinical evolution of the autistic person in terms of autonomy and quality of life, will be able to help in the identification of patients who share a common neural substrate, potential target of future therapeutic interventions. It is therefore hoped to be able to expand the number of specifiers, in order to better characterize patients and reduce the heterogeneity that has so far prevented the development of both diagnostic and prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Objective: Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system. Methods: We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration. Results: The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG. Conclusion: Our study provides an effective method for the detection of multiple sources. Significance: With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research. [ABSTRACT FROM AUTHOR]

Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of themany neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services. [ABSTRACT FROM AUTHOR]

Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation. Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results. [ABSTRACT FROM AUTHOR]

This article reviews some of the ideological forces contributing to the systematic exclusion of Black, Indigenous, and People of Color (BIPOC) in clinical neuroscience. Limitations of functional near-infrared spectroscopy (fNIRS) and other methods systematically exclude individuals with coarse or curly hair and darker skin. Despite these well-known limitations, clinical neuroscience manuscripts frequently fail to report participant race or ethnicity or reasons for excluding participants. Grounding the discussion in Dis/ability Studies and Critical Race Theory (DisCrit), we review factors that exacerbate exclusion and contribute to the multiple marginalization of BIPOC, including (a) general methodological issues, (b) perceptions about race and disability, and (c) underreporting of methods. We also present solutions. Just as scientific practices changed in response to the replication crisis, we advocate for greater attention to the crisis of underrepresentation in clinical neuroscience and provide strategies that serve to make the field more inclusive. [ABSTRACT FROM AUTHOR]

This document is an editorial published in Frontiers in Human Neuroscience titled "Women in brain health and clinical neuroscience." The editorial highlights the contributions of women in the field of neuroscience and emphasizes the need for gender equality in scientific research. It introduces a research topic that includes six contributions, covering various aspects of brain health and clinical neuroscience. The editorial aims to challenge existing biases and inspire the next generation of women to pursue careers in STEM fields. [Extracted from the article]

The number of scientific papers published every year continues to increase, but scientific knowledge is not progressing at the same rate. Here we argue that a greater emphasis on falsification - the direct testing of strong hypotheses - would lead to faster progress by allowing well-specified hypotheses to be eliminated. We describe an example from neuroscience where there has been little work to directly test two prominent but incompatible hypotheses related to traumatic brain injury. Based on this example, we discuss how building strong hypotheses and then setting out to falsify them can bring greater precision to the clinical neurosciences, and argue that this approach could be beneficial to all areas of science. [ABSTRACT FROM AUTHOR]

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [https://github.com/Portiloop]. [ABSTRACT FROM AUTHOR]

Deep brain stimulation (DBS) is a technique commonly used both in clinical and fundamental neurosciences. Classically, brain stimulation requires an implanted and wired electrode system to deliver stimulation directly to the target area. Although techniques such as temporal interference (TI) can provide stimulation at depth without involving any implanted electrodes, these methods still rely on a wired apparatus which limits free movement. Herein organic photocapacitors as untethered light‐driven electrodes which convert deep‐red light into electric current are reported. Pairs of these ultrathin devices can be driven using lasers at two different frequencies to deliver stimulation at depth via temporally interfering fields. This concept of laser TI stimulation using numerical modeling, tests with phantom brain samples, and finally in vivo tests is validated. Wireless organic photocapacitors are placed on the cortex and elicit stimulation in the hippocampus, while not delivering off‐target stimulation in the cortex. This laser‐driven wireless TI evokes a neuronal response at depth that is comparable to control experiments induced with deep brain stimulation protocols using implanted electrodes. This work shows that a combination of these two techniques—temporal interference and organic electrolytic photocapacitors—provides a reliable way to target brain structures requiring neither deeply implanted electrodes nor tethered stimulator devices. The laser TI protocol demonstrated here addresses two of the most important drawbacks in the field of DBS and thus holds potential to solve many issues in freely moving animal experiments or for clinical chronic therapy application. [ABSTRACT FROM AUTHOR]