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Óscar F Gonçalves,1–3 Marcelo C Batistuzzo,4 João R Sato5 1Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal; 2Spaulding Neuromodulation Center, Department of Physical Medicine & Rehabilitation, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 3Social and Cognitive Neuroscience Laboratory, Center for Health and Biological Sciences, Mackenzie Presbyterian University, 4Department and Institute of Psychiatry, University of São Paulo Medical School (FMUSP), 5Mathematics, Computing, and Cognition Center, Universidade Federal do ABC – UFABC, São Paulo, Brazil Abstract: The current literature provides substantial evidence of brain alterations associated with obsessive-compulsive disorder (OCD) symptoms (eg, checking, cleaning/decontamination, counting compulsions; harm or sexual, symmetry/exactness obsessions), and emotional problems (eg, defensive/appetitive emotional imbalance, disgust, guilt, shame, and fear learning/extinction) and cognitive impairments associated with this disorder (eg, inhibitory control, working memory, cognitive flexibility). Building on this evidence, new clinical trials can now target specific brain regions/networks. Real-time functional magnetic resonance imaging (rtfMRI) was introduced as a new therapeutic tool for the self-regulation of brain–mind. In this review, we describe initial trials testing the use of rtfMRI to target brain regions associated with specific OCD symptoms (eg, contamination), and other mind–brain processes (eg, cognitive – working memory, inhibitory control, emotional – defensive, appetitive systems, fear reduction through counter-conditioning) found impaired in OCD patients. While this is a novel topic of research, initial evidence shows the promise of using rtfMRI in training the self-regulation of brain regions and mental processes associated with OCD. Additionally, studies with healthy populations have shown that individuals can regulate brain regions associated with cognitive and emotional processes found impaired in OCD. After the initial “proof-of-concept” stage, there is a need to follow up with controlled clinical trials that could test rtfMRI innovative treatments targeting brain regions and networks associated with different OCD symptoms and cognitive-emotional impairments. Keywords: real-time fMRI, obsessive-compulsive disorder, self-regulation, neurofeedback, neuromodulation

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes. The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant's gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker. The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a "ball of rotations"; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material. With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

In technology education, there has been a paradigmatic shift towards student-centered approaches such as learning by doing, constructionism, and experiential learning. Educational robotics allows students to experiment with building and interacting with their creations while also fostering collaborative work. However, understanding the student's response to these approaches is crucial to adapting them during the teaching-learning process. In this sense, neuroscientific tools such as Functional Near-Infrared Spectroscopy and Eye-tracker could be useful, allowing the investigation of relevant states experienced by students. Although they have already been used in educational research, their practical relevance in the teaching-learning process has not been extensively investigated. In this perspective article expressing our position, we bring four examples of learning experiences in a robotics class with children, in which we illustrate the usefulness of these tools., Competing Interests: Declaration of competing interest We declare no conflict of interest in this publication., (Copyright © 2024 Elsevier GmbH. All rights reserved.)

Spatial cognition plays a crucial role in our daily lives. The relationship between spatial abilities and mathematical performance is well-established, with visuospatial training offering significant benefits in academic STEM (Science, Technology, Engineering, and Mathematics) disciplines. Developing visuospatial training requires an objective evaluation of spatial cognition and consideration of various 3D displays. This study aims to compare the neural efficiency of STEM and non-STEM individuals during mental rotation tasks (MRT) in 3D and 2.5D conditions (pseudo 3D) using virtual reality (VR). For that, we propose a novel integrative assessment of spatial cognition by combining a cost-effective VR headset and functional near-infrared spectroscopy (fNIRS). Overall, the findings reveal that STEM individuals exhibit greater neural efficiency in the dorsolateral prefrontal cortex (PFC) while solving MRT in a VR environment compared to their non-STEM counterparts. Additionally, the study shows that there is no significant difference in performance between 3D and 2.5D stimuli, suggesting that both conditions are equally suitable for MRT in VR. One possible explanation is that immersive VR reduces the distinctions between 3D models and 2.5D images, considering MRT scores and PFC activity. This research underscores the practicality and relevance of using VR and fNIRS to evaluate visuospatial tasks and the potential to identify distinct student learning profiles and enhance spatial skills. Furthermore, it highlights the potential of 2.5D stimuli as a cost-effective alternative for learning applications in VR. Here, we demonstrated that modeling the same task in 3D and 2.5D conditions can compare how humans interact with visuospatial tests, providing insights into applying VR devices to develop spatial skills., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Non-invasive neuromodulation is a promising intervention for obsessive-compulsive disorder (OCD), although its neurobiological mechanisms of action are still poorly understood. Recent evidence suggests that abnormalities in the connectivity of the default mode network (DMN) and the supplementary motor area (SMA) with other brain regions and networks are involved in OCD pathophysiology. We examined if transcranial direct current stimulation (tDCS) alters these connectivity patterns and if they correlate with symptom improvement in treatment-resistant OCD., Methods: In 23 patients from a larger clinical trial (comparing active tDCS to sham) who underwent pre- and post-treatment MRI scans, we assessed resting-state functional MRI (rs-fMRI) data. The treatment involved 30-minute daily tDCS sessions for four weeks (weekdays only), with the cathode over the SMA and the anode over the left deltoid. We conducted whole-brain connectivity analysis comparing active tDCS-treated to sham-treated patients., Results: We found that active tDCS, but not sham, led to connectivity increasing between the DMN and the bilateral pre/postcentral gyri (p = 0.004, FDR corrected) and temporal-auditory areas plus the SMA (p = 0.028, FDR corrected). Also, symptom improvement was directly associated with connectivity increasing between the left lateral sensorimotor network and the left precuneus (r = 0.589, p = 0.034)., Limitations: Limited sample size (23 participants with resting-state neuroimaging), inability to analyze specific OCD symptom dimensions (e.g., harm, sexual/religious, symmetry/checking, cleaning/contamination)., Conclusions: These data offer novel information concerning functional connectivity changes associated with non-invasive neuromodulation interventions in OCD and can guide new brain stimulation interventions in the framework of personalized interventions., Competing Interests: Declaration of competing interest All authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Previous functional near-infrared spectroscopy (fNIRS) studies using Go/No-Go (GNG) tasks have focused on brain activation in relation to cognitive processes, particularly inhibitory control (IC). The results of these studies commonly describe right hemispheric engagement of the dorsolateral, ventromedial, or inferior frontal regions of the prefrontal cortex. Considering that typical healthy cognitive development is negatively correlated with higher cortisol levels (which may alter brain development), the overarching aim of the current study was to investigate how elevated stress (due to unforeseeable events such as the pandemic) impacts early cognitive development., Method: In this study, we examined fNIRS data collected from a sample of children (aged 2-4 years) during a GNG task relative to the response to stressors measured via hair cortisol concentrations. We acquired data in an ecological setting (Early Childhood Education and Care) during the coronavirus pandemic., Results: We found that children with higher stress levels and a less efficient IC recruited more neural terrain and our group-level analysis indicated activation in the left orbitofrontal area during IC performance., Conclusions: A contextual stressor may disrupt accuracy in the executive function of IC early in development. More research efforts are needed to understand better how an orbitofrontal network subserves goal-directed behavior., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Much research in psychology relies on data from observational studies that traditionally do not allow for causal interpretation. However, a range of approaches in statistics and computational sciences have been developed to infer causality from correlational data. Based on conceptual and theoretical considerations on the integration of interventional and time-restrainment notions of causality, we set out to design and empirically test a new approach to identify potential causal factors in longitudinal correlational data. A principled and representative set of simulations and an illustrative application to identify early-life determinants of cognitive development in a large cohort study are presented. The simulation results illustrate the potential but also the limitations for discovering causal factors in observational data. In the illustrative application, plausible candidates for early-life determinants of cognitive abilities in 5-year-old children were identified. Based on these results, we discuss the possibilities of using exploratory causal discovery in psychological research but also highlight its limits and potential misuses and misinterpretations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research., (© 2024. The Author(s).)

Objective: The aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks., Methods: Patients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model., Results: Patients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task., Conclusions: Cortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation., Significance: Simultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments., (Copyright © 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Spatial cognition plays a crucial role in academic achievement, particularly in science, technology, engineering, and mathematics (STEM) domains. Immersive virtual environments (VRs) have the growing potential to reduce cognitive load and improve spatial reasoning. However, traditional methods struggle to assess the mental effort required for visuospatial processes due to the difficulty in verbalizing actions and other limitations in self-reported evaluations. In this neuroergonomics study, we aimed to capture the neural activity associated with cognitive workload during visuospatial tasks and evaluate the impact of the visualization medium on visuospatial task performance. We utilized functional near-infrared spectroscopy (fNIRS) wearable neuroimaging to assess cognitive effort during spatial-reasoning-based problem-solving and compared a VR, a computer screen, and a physical real-world task presentation. Our results reveal a higher neural efficiency in the prefrontal cortex (PFC) during 3D geometry puzzles in VR settings compared to the settings in the physical world and on the computer screen. VR appears to reduce the visuospatial task load by facilitating spatial visualization and providing visual cues. This makes it a valuable tool for spatial cognition training, especially for beginners. Additionally, our multimodal approach allows for progressively increasing task complexity, maintaining a challenge throughout training. This study underscores the potential of VR in developing spatial skills and highlights the value of comparing brain data and human interaction across different training settings.

Background: Phantom pain limb (PLP) has gained more attention due to the large number of people with amputations around the world and growing knowledge of the pain process, although its mechanisms are not completely understood., Objectives: The aim of this study was to understand, in patients with amputations, the association between PLP and residual limb pain (RLP), and the brain metabolic response in cortical motor circuits, using functional near-infrared spectroscopy (fNIRS)., Methods: Sixty participants were recruited from the rehabilitation program in São Paulo, Brazil. Included patients were aged over 18 years, with traumatic unilateral lower-limb amputation, with PLP for at least 3 months after full recovery from amputation surgery. PLP and RLP levels were measured using visual analogue scales. fNIRS was performed during motor execution and motor mirror tasks for 20 s. In order to highlight possible variables related to variation in pain measures, univariate linear regression analyses were performed for both experimental conditions, resulting in four fNIRS variables (two hemispheres x two experimental conditions). Later, in order to test the topographic specificity of the models, eight multivariate regression analyses were performed (two pain scales x two experimental conditions x two hemispheres), including the primary motor cortex (PMC) related channel as an independent variable as well as five other channels related to the premotor area, supplementary area, and somatosensory cortex. All models were controlled for age, sex, ethnicity, and education., Results: We found that: i) there is an asymmetric metabolic activation during motor execution and mirror task between hemispheres (with a predominance that is ipsilateral to the amputated limb), ii) increased metabolic response in the PMC ipsilateral to the amputation is associated with increased PLP (during both experimental tasks), while increased metabolic response in the contralateral PMC is associated with increased RLP (during the mirror motor task only); ii) increased metabolic activity of the ipsilateral premotor region is associated with increased PLP during the motor mirror task; iii) RLP was only associated with higher metabolic activity in the contralateral PMC and lower metabolic activity in the ipsilateral inferior frontal region during motor mirror task, but PLP was associated with higher metabolic activity during both tasks., Conclusion: These results suggest there is both task and region specificity for the association between the brain metabolic response and the two different types of post-amputation pain. The metabolic predominance that is ipsilateral to the amputated limb during both tasks was associated with higher levels of PLP, suggesting a cortical motor network activity imbalance due to potential interhemispheric compensatory mechanisms. The present work contributes to the understanding of the underlying topographical patterns in the motor-related circuits associated with pain after amputations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023. Published by Elsevier Masson SAS.)

Introduction: Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context., Methods: In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together., Results: This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players., Discussion: The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 do Nascimento, Santos da Silva, Ara, Sato and Costa.)

Naturalistic paradigms are being increasingly applied to investigate human brain function. Compared with resting-state and task-based paradigms in neuroimaging, naturalistic stimuli and situations can be potentially more readily translated to daily-life applications. Among neuroimaging modalities, functional near-infrared spectroscopy (fNIRS) is particularly suitable for naturalistic investigations and applications. However, specific and tailored statistical analysis to interrogate brain function using naturalistic fNIRS is warranted. Here, we describe an exploratory graph-centrality-based approach to investigating participants' spatiotemporal similarities from the fNIRS signal. We illustrate the usefulness of our approach in a sample of typically developing children (10 males and 9 females; mean age of 5.2 years old; sd = 0.78) while they watch the Inscapes movie designed for neuroimaging acquisition. A node in the left dorsal prefrontal cortex presented similar responses across children, and those fNIRS responses were in line with scene transitions in the movie stimulus. Our results suggest the feasibility of applying centrality graph-based measures to investigate brain function in naturalistic fNIRS during development.

Objective: To explore differences in regional cortical morphometric structure between adolescents at risk for depression or with current depression., Methods: We analyzed cross-sectional structural neuroimaging data from a sample of 150 Brazilian adolescents classified as low-risk (n=50) or high-risk for depression (n=50) or with current depression (n=50) through a vertex-based approach with measurements of cortical volume, surface area and thickness. Differences between groups in subcortical volumes and in the organization of networks of structural covariance were also explored., Results: No significant differences in brain structure between groups were observed in whole-brain vertex-wise cortical volume, surface area or thickness. Also, no significant differences in subcortical volume were observed between risk groups. In relation to the structural covariance network, there was an indication of an increase in the hippocampus betweenness centrality index in the high-risk group network compared to the low-risk and current depression group networks. However, this result was only statistically significant when applying false discovery rate correction for nodes within the affective network., Conclusion: In an adolescent sample recruited using an empirically based composite risk score, no major differences in brain structure were detected according to the risk and presence of depression., Competing Interests: The authors report no conflicts of interest.

Recently, several studies have investigated the neurodevelopment of psychiatric disorders using brain data acquired via structural magnetic resonance imaging (sMRI). These analyses have shown the potential of sMRI data to provide a relatively precise characterization of brain structural biomarkers. Despite these advances, a relatively unexplored question is how reliable and consistent a model is when assessing subjects from other independent datasets. In this study, we investigate the performance and generalizability of the same model architecture trained from distinct datasets comprising youths in diverse stages of neurodevelopment and with different mental health conditions. We employed models with the same 3D convolutional neural network (CNN) architecture to assess autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), brain age, and a measure of dimensional psychopathology, the Child Behavior Checklist (CBCL) total score. The investigated datasets include the Autism Brain Imaging Data Exchange II (ABIDE-II, N = 580), Attention Deficit Hyperactivity Disorder (ADHD-200, N = 922), Brazilian High-Risk Cohort Study (BHRCS, N = 737), and Adolescent Brain Cognitive Development (ABCD, N = 11,031). Models' performance and interpretability were assessed within each dataset (for diagnosis tasks) and inter-datasets (for age estimation). Despite the demographic and phenotypic differences of the subjects, all models presented significant estimations for age (p value < 0.001) within and between datasets. In addition, most models showed a moderate to high correlation in age estimation. The results, including the models' brain regions of interest (ROI), were analyzed and discussed in light of the youth neurodevelopmental structural changes. Among other interesting discoveries, we found that less confounded training datasets produce models with higher generalization capacity., (© 2023. The Author(s).)

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation., (© 2023 The Authors. Genes, Brain and Behavior published by International Behavioural and Neural Genetics Society and John Wiley & Sons Ltd.)

Generative Adversarial Networks (GANs) are promising analytical tools in machine learning applications. Characterizing atypical neurodevelopmental processes might be useful in establishing diagnostic and prognostic biomarkers of psychiatric disorders. In this article, we investigate the potential of GANs models combined with functional connectivity (FC) measures to build a predictive neurotypicality score 3-years after scanning. We used a ROI-to-ROI analysis of resting-state functional magnetic resonance imaging (fMRI) data from a community-based cohort of children and adolescents (377 neurotypical and 126 atypical participants). Models were trained on data from neurotypical participants, capturing their sample variability of FC. The discriminator subnetwork of each GAN model discriminated between the learned neurotypical functional connectivity pattern and atypical or unrelated patterns. Discriminator models were combined in ensembles, improving discrimination performance. Explanations for the model's predictions are provided using the LIME (Local Interpretable Model-Agnostic) algorithm and local hubs are identified in light of these explanations. Our findings suggest this approach is a promising strategy to build potential biomarkers based on functional connectivity., Competing Interests: LAR has received grant or research support from, served as a consultant to, and served on the speakers’ bureau of Abbott, Aché, Bial, Medice, Novartis/Sandoz, Pfizer/Upjohn, and Shire/Takeda in the last three years. The ADHD and Juvenile Bipolar Disorder Outpatient Programs chaired by LAR have received unrestricted educational and research support from the following pharmaceutical companies in the last three years: Novartis/Sandoz and Shire/Takeda. LAR has received authorship royalties from Oxford Press and ArtMed. All of these are outside of the study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with the author JS., (Copyright © 2023 Dos Santos, Mendes, Biazoli, Gadelha, Salum, Miguel, Rohde and Sato.)

Teacher-student relationships have been found consistently important for student school effectiveness in mathematics in the last three decades. Although this observation is generally made from the teacher's perspective, neuroscience can provide new insights by establishing the neurobiological underpinning of social interactions. This paper further develops this line of research by utilizing graph theory to represent interactions between teachers and students at the neural level. Through hyperscanning with functional near-infrared spectroscopy (fNIRS), we collected data from the prefrontal cortex and the temporoparietal junction of 24 dyads composed of a teacher and a student. Each dyad used a board game to perform a programming logic class that consisted of three steps: independent activities (control), presentation of concepts, and interactive exercises. Graph theory provides results regarding the strength of teacher-student interaction and the main channels involved in these interactions. We combined graph modularity and bootstrap to measure pair coactivation, thus establishing the strength of teacher-student interaction. Also, graph centrality detects the main brain channels involved during this interaction. In general, the teacher's most relevant nodes rely on the regions related to language and number processing, spatial cognition, and attention. Also, the students' most relevant nodes rely on the regions related to task management., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Eye tracking is one of the techniques used to investigate cognitive mechanisms involved in the school context, such as joint attention and visual perception. Eye tracker has portability, straightforward application, cost-effectiveness, and infant-friendly neuroimaging measures of cognitive processes such as attention, engagement, and learning. Furthermore, the ongoing software enhancements coupled with the implementation of artificial intelligence algorithms have improved the precision of collecting eye movement data and simplified the calibration process. These characteristics make it plausible to consider eye-tracking technology a promising tool to assist the teaching-learning process in school routines. However, eye tracking needs to be explored more as an educational instrument for real-time classroom activities and teachers' feedback. This perspective article briefly presents the fundamentals of the eye-tracking technique and four illustrative examples of employing this method in everyday school life. The first application shows how eye tracker information may contribute to teacher assessment of students' computational thinking in coding classes. In the second and third illustrations, we discuss the additional information provided by the eye-tracker to the teacher assessing the student's strategies to solve fraction problems and chart interpretation. The last illustration demonstrates the potential of eye tracking to provide Real-time feedback on learning difficulties/disabilities. Thus, we highlight the potential of the eye tracker as a complementary tool to promote personalized education and discuss future perspectives. In conclusion, we suggest that an eye-tracking system could be helpful by providing real-time student gaze leading to immediate teacher interventions and metacognition strategies., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Oku, Barreto, Bruneri, Brockington, Fujita and Sato.)

Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the EEG beta and gamma bands modulated by the alpha frequency band (beta- m -alpha and gamma- m -alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into "connectivity summary scores" (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson's correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma- m -alpha CSS ( r = -0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Trambaiolli, Cassani, Biazoli, Cravo, Sato and Falk.)

Attention is a basic human function underlying every other cognitive process. It is demonstrated in the functional Magnetic Resonance Imaging literature that frontoparietal networks are involved with attentive performance while default mode networks are involved with inattentive performance. Yet, it is still not clear whether similar results would be found with functional Near-Infrared Spectroscopy. The goal of our study was to investigate differences in hemodynamic activity measured by functional Near-Infrared Spectroscopy between fast and slow responses on a simple sustained attention task both before and after stimulus onset. Thirty healthy adults took part in the study. Our results have shown differences between fast and slow responses only on channels over medial frontal cortex and inferior parietal cortex (p < 0,05). These differences were observed both before and after stimulus presentation. It is discussed that functional Near-Infrared Spectroscopy is a good tool to investigate the frontoparietal network and its relationship with performance in attention tasks; it could be used to further investigate other approaches on attention, such as the dual network model of cognitive control and brain states views based on complex systems analysis; and finally, it could be used to investigate attention in naturalistic settings., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions., (© 2022 The Authors.)

The neurobiological factors associated with the emergence of major depressive disorder (MDD) in adolescence are still unclear. Previous cross-sectional studies have documented aberrant connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) networks. However, whether these findings precede MDD onset has not been established. This scoping review mapped key methodological aspects and main findings of longitudinal rs-fMRI studies of MDD in adolescence. Three sets of neuroimaging methods to analyze rs-fMRI data were identified: seed-based analysis, independent component analysis, and network-based approaches. Main findings involved aberrant connectivity within and between the default mode network (DMN), the cognitive control network (CCN), and the salience network (SN). Accordingly, we utilized Menon's (2011) triple-network model for neuropsychiatric disorders to summarize key results. Adolescent MDD was associated with hyperconnectivity within the SN and between DMN and SN, as well as hypoconectivity within the CCN. These findings suggested that dysfunctional connectivity among the three main large-scale brain networks preceded MDD onset. However, there was high heterogeneity in neuroimaging methods and sampling procedures, which may limit comparisons between studies. Future studies should consider some level of harmonization for clinical instruments and neuroimaging methods., Competing Interests: RAB has received research grants from Janssen Cilag, Novartis, and Roche; has been a forum consultant for Janssen, Novartis, and Roche; and has participated in speaker bureaus for Ache, Janssen, Lundbeck, and Novartis. PMP has received payment or honoraria for lectures and presentations in educational events for Sandoz, Daiichi Sankyo, Eurofarma, Abbott, Libbs, Instituto Israelita de Pesquisa e Ensino Albert Einstein, and Instituto D'Or de Pesquisa e Ensino. The other authors report no conflicts of interest.

Objective: Research in adolescent depression has found aberrant intrinsic functional connectivity (iFC) among the ventral striatum (VS) and several brain regions implicated in reward processing. The present study probes this question by taking advantage of the availability of data from a large youth cohort, the IMAGEN Consortium., Methods: iFC data from 303 adolescents (48% of them female) were used to examine associations of VS connectivity at baseline (at age 14) with depressive disorders at baseline and at 2-year (N=250) and 4-year (N=219) follow-ups. Eleven regions of interest, key nodes of the reward system, were used to probe the reward network and calculate the connectivity strength of the VS within this network (VS connectivity rw ). The main analyses assessed associations of VS connectivity rw with depressive disorders, anhedonia, and low mood using logistic regression. Autoregressive models accounting for carryover effects over time were conducted to further evaluate these brain-behavior associations., Results: Higher right VS connectivity rw was associated with higher probability of depressive disorders at baseline (odds ratio=2.65, 95% CI=1.40, 5.05). This finding was confirmed in the autoregressive model, adjusting for carryover effects of the depressive disorders across the three time points. VS connectivity rw was not predictive of depressive disorders at follow-up assessments. Longitudinal associations between VS connectivity rw and anhedonia emerged in the structural equation model: left VS connectivity rw was associated with anhedonia at 2 years (odds ratio=2.20, 95% CI=1.54, 3.14), and right VS connectivity rw was linked to anhedonia at 4 years (odds ratio=1.87, 95% CI=1.09, 3.21). VS connectivity rw did not predict low mood at any time point in the structural equation model., Conclusions: The connectivity strength of the VS within the reward network showed distinct patterns of association with depressive disorders and anhedonia from mid to late adolescence, suggesting that the role of this circuitry in depression changes with age. This study replicates, in an independent sample, the association between the VS and depression previously reported in younger adolescents. The findings suggest a role of VS connectivity rw in anhedonia but not in low mood.

Background: Most early children's experiences will occur in a family context; therefore, the quality of this environment is critical for development outcomes. Not many studies have assessed the correlations between brain functional connectivity (FC) in important areas such as the default mode network (DMN) and the quality of parent-child relationships in school-age children and early adolescence. The quality of family relationships and maternal behavior have been suggested to modulate DMN FC once they act as external regulators of children's affect and behavior. Objective: We aimed to test the associations between the quality of family environment/maternal behavior and FC within the DMN of school-age children. Method: Resting-state, functional magnetic resonance imaging data, were collected from 615 children (6-12 age range) enrolled in the Brazilian High-Risk Cohort (HRC) study. We assessed DMN intra-connectivity between the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPL-bilateral) regions. The family functioning was assessed by levels of family cohesiveness and conflict and by maternal behavior styles such as maternal responsiveness, maternal stimulus to the child's autonomy, and maternal overprotection. The family environment was assessed with the Family Environment Scale (FES), and maternal behavior was assessed by the mother's self-report. Results: We found that the quality of the family environment was correlated with intra-DMN FC. The more conflicting the family environment was, the greater the FC between the mPFC-left IPL (lIPL), while a more cohesive family functioning was negatively correlated with FC between the PCC-lIPL. On the other hand, when moderated by a positive maternal behavior, cohesive family functioning was associated with increased FC in both regions of the DMN (mPFC-lIPL and PCC-lIPL). Conclusions: Our results highlight that the quality of the family environment might be associated with differences in the intrinsic DMN FC.

Background and Purpose: Acute Respiratory Distress Syndrome (ADRS) due to coronavirus disease 2019 (COVID-19) has been associated with muscle fatigue, corticospinal pathways dysfunction, and mortality. High-Definition transcranial Direct Current Stimulation (HD-tDCS) may be used to attenuate clinical impairment in these patients. The HD-RECOVERY randomized clinical trial was conducted to evaluate the efficacy and safety of HD-tDCS with respiratory rehabilitation in patients with moderate to severe ARDS due to COVID-19., Methods: Fifty-six critically ill patients were randomized 1:1 to active (n = 28) or sham (n = 28) HD-tDCS (twice a day, 30-min, 3-mA) plus respiratory rehabilitation for up to 10 days or until intensive care unit discharge. The primary outcome was ventilator-free days during the first 28 days, defined as the number of days free from mechanical ventilation. Furthermore, secondary outcomes such as delirium, organ failure, hospital length of stay and adverse effects were investigated., Results: Active HD-tDCS induced more ventilator-free days compared to sham HD-tDCS. Patients in the active group vs in the sham group experienced lower organ dysfunction, delirium, and length of stay rates over time. In addition, positive clinical response was higher in the active vs sham group. There was no significant difference in the prespecified secondary outcomes at 5 days. Adverse events were similar between groups., Conclusions: Among patients with COVID-19 and moderate to severe ARDS, use of active HD-tDCS compared with sham HD-tDCS plus respiratory rehabilitation resulted in a statistically significant increase in the number of ventilator-free days over 28 days. HD-tDCS combined with concurrent rehabilitation therapy is a safe, feasible, potentially add-on intervention, and further trials should examine HD-tDCS efficacy in a larger sample of patients with COVID-19 and severe hypoxemia., Competing Interests: Declaration of competing interest The City University of New York holds patents on brain stimulation with MB as inventor. MB has equity in Soterix Medical Inc. MB consults, received grants, assigned inventions, and/or serves on the SAB of SafeToddles, Boston Scientific, GlaxoSmithKline, Biophysics, Mecta, Lumenis, Halo Neuroscience, Google-X, i-Lumen, Humm, Allergan (Abbvie), Apple. AD is an employee and has equity in Soterix Medical Inc., (Copyright © 2022. Published by Elsevier Inc.)

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6-14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up. Results were replicated in two samples: IMAGEN (n = 1627) and the Healthy Brain Network (n = 843). NAS were derived by summing the product of each standardized brain feature by the effect size of the association of that brain feature with seven psychiatric disorders documented by previous meta-analyses. NAS were calculated for surface area, cortical thickness and subcortical volumes using T1-weighted scans. NAS reliability, temporal stability and psychopathology and cognition prediction were analyzed. NAS for surface area showed high internal consistency and 3-year stability and predicted general psychopathology and cognition with higher replicability than specific symptomatic domains for all samples. They also predicted general psychopathology with higher replicability than single structures alone, accounting for 1-3% of the variance, but without directionality. The NAS for cortical thickness and subcortical volumes showed lower internal consistency and less replicable associations with behavioural phenotypes. These findings indicate the NAS based on surface area might be replicable markers of general psychopathology, but these links are unlikely to be causal or clinically useful yet., (© 2020. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Resistance to antipsychotic treatment affects up to 30% of patients with schizophrenia. Although the time course of development of treatment-resistant schizophrenia (TRS) varies from patient to patient, the reasons for these variations remain unknown. Growing evidence suggests brain dysconnectivity as a significant feature of schizophrenia. In this study, we compared fractional anisotropy (FA) of brain white matter between TRS and non-treatment-resistant schizophrenia (non-TRS) patients. Our central hypothesis was that TRS is associated with reduced FA values., Methods: TRS was defined as the persistence of moderate to severe symptoms after adequate treatment with at least two antipsychotics from different classes. Diffusion-tensor brain MRI obtained images from 34 TRS participants and 51 non-TRS. Whole-brain analysis of FA and axial, radial, and mean diffusivity were performed using Tract-Based Spatial Statistics (TBSS) and FMRIB's Software Library (FSL), yielding a contrast between TRS and non-TRS patients, corrected for multiple comparisons using family-wise error (FWE) < 0.05., Results: We found a significant reduction in FA in the splenium of corpus callosum (CC) in TRS when compared to non-TRS. The antipsychotic dose did not relate to the splenium CC., Conclusion: Our results suggest that the focal abnormality of CC may be a potential biomarker of TRS.

Congenital Zika Syndrome (CZS) is characterized by changes in cranial morphology associated with heterogeneous neurological manifestations and cognitive and behavioral impairments. In this syndrome, longitudinal neuroimaging could help clinicians to predict developmental trajectories of children and tailor treatment plans accordingly. However, regularly acquiring magnetic resonance imaging (MRI) has several shortcomings besides cost, particularly those associated with childrens' clinical presentation as sensitivity to environmental stimuli. The indirect monitoring of local neural activity by non-invasive functional near-infrared spectroscopy (fNIRS) technique can be a useful alternative for longitudinally accessing the brain function in children with CZS. In order to provide a common framework for advancing longitudinal neuroimaging assessment, we propose a principled guideline for fNIRS acquisition and analyses in children with neurodevelopmental disorders. Based on our experience on collecting fNIRS data in children with CZS we emphasize the methodological challenges, such as clinical characteristics of the sample, desensitization, movement artifacts and environment control, as well as suggestions for tackling such challenges. Finally, metrics based on fNIRS can be associated with established clinical metrics, thereby opening possibilities for exploring this tool as a long-term predictor when assessing the effectiveness of treatments aimed at children with severe neurodevelopmental disorders., (© 2021. The Author(s).)

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.

The procedures to be performed after sudden engine failure of a single-engine helicopter impose high workload on pilots. The maneuver to regain aircraft control and safe landing is called autorotation. The safety limits to conduct this maneuver are based on the aircraft height versus speed diagram, which is also known as "Dead Man's Curve". Flight-test pilots often use subjective methods to assess the difficulty to conduct maneuvers in the vicinity of this curve. We carried out an extensive flight test campaign to verify the feasibility of establishing quantitative physiological parameters to better assess the workload endured by pilots undergoing those piloting conditions. Eleven pilots were fully instrumented with sensors and had their physiological reactions collected during autorotation maneuvers. Our analyses suggested that physiological measurements (heart rate and electrodermal activity) can be successfully recorded and useful to capture the most effort-demanding effects during the maneuvers. Additionally, the helicopter's flight controls displacements were also recorded, as well as the pilots' subjective responses evaluated by the Handling Qualities Rate scale. Our results revealed that the degree of cognitive workload was associated with the helicopter's flight profile concerning the Height-Speed diagram and that the strain intensity showed a correlation with measurable physiological responses. Recording flight controls displacement and quantifying the pilot's subjective responses show themselves as natural effective candidates to evaluate the intensity of cognitive workload in such maneuvers., (© 2021. The Author(s).)

There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles., (© 2021 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Average slow potentials (SPs) can be computed from any voluntary task, minimally involving attention to anticipated stimuli. Their topography when recorded by large electrode arrays even during simple tasks is complex, multifocal, and its generators appear to be equally multifocal and highly variable across subjects. Various sources of noise of course contaminate such averages and must contribute to the topographic complexity. Here, we report a study in which the non-averaged SP band (0 to 1 Hz) was analyzed by independent components (ICA), from 256 channel recordings of 18 subjects, during four task conditions (resting, visual attention, CPT, and Stroop). We intended to verify whether the replicable SP generators (between two separate day sessions) modeled as current density reconstruction on structural MRI sets were individual-specific, and if putative task-related differences were systematic across subjects. Typically, 3 ICA components (out of 10) explained SPs in each task and subject, and their combined generators were highly variable across subjects: although some occipito-temporal and medial temporal areas contained generators in most subjects; the overall patterns were obviously variable, with no single area common to all 18 subjects. Linear regression modeling to compare combined generators (from all ICA components) between tasks and sessions showed significantly higher correlations between the four tasks than between sessions for each task. Moreover, it was clear that no common task-specific areas could be seen across subjects. Those results represent one more instance in which individual case analyses favor the hypothesis of individual-specific patterns of cortical activity, regardless of task conditions. We discuss this hypothesis with respect to results from the beta band, from individual-case fMRI studies, and its corroboration by functional neurosurgery and the neuropsychology of focal lesions., (© 2021. Fondazione Società Italiana di Neurologia.)

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer's disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community., (© 2021. The Author(s).)

Background: Functional imaging studies have associated dystonia with abnormal activation in motor and sensory brain regions. Commonly used techniques such as functional magnetic resonance imaging impose physical constraints, limiting the experimental paradigms. Functional near-infrared spectroscopy (fNIRS) offers a new noninvasive possibility for investigating cortical areas and the neural correlates of complex motor behaviors in unconstrained settings., Methods: We compared the cortical brain activation of patients with focal upper-limb dystonia and controls during the writing task under naturalistic conditions using fNIRS. The primary motor cortex (M1), the primary somatosensory cortex (S1), and the supplementary motor area were chosen as regions of interest (ROIs) to assess differences in changes in both oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) between groups., Results: Group average activation maps revealed an expected pattern of contralateral recruitment of motor and somatosensory cortices in the control group and a more bilateral pattern of activation in the dystonia group. Between-group comparisons focused on specific ROIs revealed an increased activation of the contralateral M1 and S1 cortices and also of the ipsilateral M1 cortex in patients., Conclusions: Overactivity of contralateral M1 and S1 in dystonia suggest a reduced specificity of the task-related cortical areas, whereas ipsilateral activation possibly indicates a primary disorder of the motor cortex or an endophenotypic pattern. To our knowledge, this is the first study using fNIRS to assess cortical activity in dystonia during the writing task under natural settings, outlining the potential of this technique for monitoring sensory and motor retraining in dystonia rehabilitation.