Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI.

Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks. To address these limitations, we present a novel deep learning and spatial-wise attention based model called Spatial-Temporal Convolutional Attention (STCA) to accurately model dynamic FBNs. Specifically, we train STCA in a self-supervised manner by utilizing a Convolutional Autoencoder to guide the STCA module in assigning higher attention weights to regions of functional activity. To validate the reliability of the results, we evaluate our approach on the HCP-task motor behavior dataset, the experimental results demonstrate that the STCA derived FBNs have higher spatial similarity with the templates and that the spatial similarity between the templates and the FBNs derived by STCA fluctuates with the task design over time, suggesting that STCA can reflect the dynamic changes of brain function, providing a powerful tool to better understand human brain function. Code is available at https://github.com/SNNUBIAI/STCAE.

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices.

Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely nonneural, appearing stronger in neurovasculature than gray matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting-state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods.

Brain-based sex differences in schizophrenia: A systematic review of fMRI studies.

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex-based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex-based symptom differences. This study aimed to summarize sex-based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex-based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level-dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low-frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low-frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex-based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex-based differences in the clinical presentation and course of the disorder.

Neural basis of reward expectancy inducing proactive aggression.

Proactive aggression refers to deliberate and unprovoked behavior, typically motivated by personal gain or expected reward. Reward expectancy is generally recognized as a critical factor that may influence proactive aggression, but its neural mechanisms remain unknown. We conducted a task-based functional magnetic resonance imaging (fMRI) experiment to investigate the relationship between reward expectancy and proactive aggression. 37 participants (20 females, mean age = 20.8 ± 1.42, age range = 18-23 years) completed a reward-harm task. In the experiment, reward valence expectancy and reward possibility expectancy were manipulated respectively by varying amounts (low: 0.5-1.5 yuan; high: 10.5-11.5 yuan) and possibilities (low: 10%-30%; high: 70%-90%) of money that participants could obtain by choosing to aggress. Participants received fMRI scans throughout the experiment. Brain activation regions associated with reward expectancy mainly involve the middle frontal gyrus, lingual gyrus, inferior temporal gyrus, anterior cuneus, caudate nucleus, inferior frontal gyrus, cingulate gyrus, anterior central gyrus, and posterior central gyrus. Associations between brain activation and reward expectancy in the left insula, left middle frontal gyrus, left thalamus, and right middle frontal gyrus were found to be related to proactive aggression. Furthermore, the brain activation regions primarily involved in proactive aggression induced by reward expectancy were the insula, inferior frontal gyrus, inferior temporal gyrus, pallidum, and caudate nucleus. Under conditions of high reward expectancy, participants engage in more proactive aggressive behavior. Reward expectancy involves the activation of reward- and social-cognition-related brain regions, and these associations are instrumental in proactive aggressive decisions.

The utilisation of fMRI for pre-operative mapping in the paediatric population with central nervous system tumours: a systematic review.

BACKGROUND: Functional MRI (fMRI) is a well-established tool for pre-operative planning, providing neurosurgeons with a roadmap of critical functional areas to preserve during surgery. Despite its increasing use, there is a need to compare task-based (tb-fMRI) and resting-state fMRI (rs-fMRI) in the peadiatric population to comprehensively evaluate the existing literature on the use of fMRI for pre-operative mapping in pediatric patients, comparing tb-fMRI and rs-fMRI. METHODS: Two databases were searched for relevant studies published before July 2024 following the PRISMA guidelines. Eleven studies were selected and comprised 431 participants: 377 patients with different types and locations of brain tumours, and 54 healthy controls (HC). RESULTS: Results indicate that tb-fMRI could reliably locate the eloquent cortex with more than an 80% success rate. Furthermore, results were comparable with intraoperative mapping. Two studies reported that 68-81% of patients did not develop deficits in the postoperative period. Results also found that rs-fMRI can fill the gap in the situation of paediatric patients when other techniques do not apply to younger patients. CONCLUSION: This study suggests that tb-fMRI is more effective for pre-operative mapping in pediatric patients, offering precise localisation of critical brain functions and enhancing surgical planning. Although rs-fMRI is less demanding and compatible with light sedation, it lacks the specificity needed for accurate identification of language, sensory, and motor areas, which limits its clinical relevance. rs-fMRI can aid in function-preserving treatments for brain tumour patients and reduce the need for invasive procedures. Combining tb-fMRI with intraoperative mapping optimizes precision and safety in pediatric-neurosurgery.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition.

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Performance and reliability assessment of a lower dose, task-based scoliosis radiography protocol in pediatric patients.

BACKGROUND: Follow-up scoliosis radiographs are performed to assess the degree of spinal curvature and skeletal maturity, which can be done at lower radiation exposures than those in standard-dose radiography. OBJECTIVE: Describe and evaluate a protocol that reduced the radiation in follow-up frontal-view scoliosis radiographs. MATERIALS AND METHODS: We implemented a postero-anterior lower dose modified-technique for scoliosis radiography with task-based definition of adequate image quality and use of technique charts based on target exposure index and patient's height and weight. We subsequently retrospectively evaluated 40 consecutive patients who underwent a follow-up radiograph using the modified-technique after an initial standard-technique radiograph. We evaluated comparisons of proportions for subjective assessment with chi-squared tests, and agreements of reader's scores with intraclass correlation coefficients and Bland-Altman plots. We determined incident air kerma, exposure index, deviation index/standard deviation, dose-area product (DAP), and effective dose for each radiograph. We set statistical significance at P<0.05. RESULTS: Forty patients (65% female), aged 4-17 years. Median effective dose was reduced from 39 to 10 µSv (P<0.001), incident air kerma from 139 to 29 µSv (P<0.001), and DAP from 266 to 55 mGy*cm2 (P<0.001). All modified-technique parameters were rated with a mean score of acceptable or above. All modified-technique measurements obtained inter- and intra-observer correlation coefficient agreements of 0.86 ("Good") or greater. CONCLUSION: Substantial dose reduction on follow-up scoliosis imaging with existing radiography units is achievable through task-based definition of adequate image quality and tailoring of radiation to each patient's height and weight, while still allowing for reliable assessment and reproducible measurements.

Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data.

The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (ß = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (ß = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial.

BACKGROUND: Delivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially facilitating neural activation and improving motor function for stroke survivors. However, the intervention effect of this personalized HD-tDCS has not been explored on post-stroke motor recovery. In this study, we aim to evaluate whether targeting individual motor hotspot with HD-tDCS followed by EMG-driven robotic hand training could further facilitate the upper extremity motor function for chronic stroke survivors. METHODS: In this pilot randomized controlled trial, eighteen chronic stroke survivors were randomly allocated into two groups. The HDtDCS-group (n = 8) received personalized HD-tDCS using task-based fMRI to guide the stimulation on individual motor hotspot. The Sham-group (n = 10) received only sham stimulation. Both groups underwent 20 sessions of training, each session began with 20 min of HD-tDCS and was then followed by 60 min of robotic hand training. Clinical scales (Fugl-meyer Upper Extremity scale, FMAUE; Modified Ashworth Scale, MAS), and neuroimaging modalities (fMRI and EEG-EMG) were conducted before, after intervention, and at 6-month follow-up. Two-way repeated measures analysis of variance was used to compare the training effect between HDtDCS- and Sham-group. RESULTS: HDtDCS-group demonstrated significantly better motor improvement than the Sham-group in terms of greater changes of FMAUE scores (F = 6.5, P = 0.004) and MASf (F = 3.6, P = 0.038) immediately and 6 months after the 20-session intervention. The task-based fMRI activation significantly shifted to the ipsilesional motor area in the HDtDCS-group, and this activation pattern increasingly concentrated on the motor hotspot being stimulated 6 months after training within the HDtDCS-group, whereas the increased activation is not sustainable in the Sham-group. The neuroimaging results indicate that neural plastic changes of the HDtDCS-group were guided specifically and sustained as an add-on effect of the stimulation. CONCLUSIONS: Stimulating the individual motor hotspot before robotic hand training could further enhance brain activation in motor-related regions that promote better motor recovery for chronic stroke. TRIAL REGISTRATION: This study was retrospectively registered in ClinicalTrials.gov (ID NCT05638464).

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort.

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Investigation of Neurofunctional Changes Over the Course of Electroconvulsive Therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is an effective treatment for patients suffering from depression. Yet the exact neurobiological mechanisms underlying the efficacy of ECT and indicators of who might respond best to it remain to be elucidated. Identifying neural markers that can inform about an individual's response to ECT would enable more optimal treatment strategies and increase clinical efficacy. METHODS: Twenty-one acutely depressed inpatients completed an emotional working memory task during functional magnetic resonance imaging before and after receiving treatment with ECT. Neural activity was assessed in 5 key regions associated with the pathophysiology of depression: bilateral dorsolateral prefrontal cortex and pregenual, subgenual, and dorsal anterior cingulate cortex. Associations between brain activation and clinical improvement, as reflected by Montgomery-Åsberg Depression Rating Scale scores, were computed using linear regression models, t tests, and Pearson correlational analyses. RESULTS: Significant neurobiological prognostic markers or changes in neural activity from pre- to post ECT did not emerge. CONCLUSIONS: We could not confirm normalization effects and did not find significant neural markers related to treatment response. These results demonstrate that the search for reliable and clinically useful biomarkers for ECT treatment remains in its initial stages and still faces challenges.

Neural predictors of cognitive-behavior therapy outcome in anxiety-related disorders: a meta-analysis of task-based fMRI studies.

BACKGROUND: Cognitive-behavior therapy (CBT) is a well-established first-line intervention for anxiety-related disorders, including specific phobia, social anxiety disorder, panic disorder/agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, and posttraumatic stress disorder. Several neural predictors of CBT outcome for anxiety-related disorders have been proposed, but previous results are inconsistent. METHODS: We conducted a systematic review and meta-analysis of task-based functional magnetic resonance imaging (fMRI) studies investigating whole-brain predictors of CBT outcome in anxiety-related disorders (17 studies, n = 442). RESULTS: Across different tasks, we observed that brain response in a network of regions involved in salience and interoception processing, encompassing fronto-insular (the right inferior frontal gyrus-anterior insular cortex) and fronto-limbic (the dorsomedial prefrontal cortex-dorsal anterior cingulate cortex) cortices was strongly associated with a positive CBT outcome. CONCLUSIONS: Our results suggest that there are robust neural predictors of CBT outcome in anxiety-related disorders that may eventually lead (probably in combination with other data) to develop personalized approaches for the treatment of these mental disorders.

Functional connectivity correlates of attentional networks in insomnia disorder: A pilot study.

Insomnia disorder has been associated with poor executive functioning. Functional imaging studies of executive functioning in insomnia are scarce and inconclusive. Because the Attentional Network Test relies on well-defined cortical networks and sensitively distinguishes different aspects of executive function, it might reveal brain functional alterations in relatively small samples of patients. The current pilot study assessed functional connectivity during the Attentional Network Test performed using magnetic resonance imaging in 12 participants with insomnia and 13 self-defined good sleepers. ANCOVAs were used to evaluate group differences in performance and functional connectivity in the regions of interest representing the attentional networks (i.e. alerting, orienting and executive control) at p < 0.05, uncorrected. During the orienting part, participants with insomnia showed weaker connectivity of the precentral gyrus with the superior parietal lobe (false discovery rate-corrected), while they showed stronger connectivity between premotor and visual regions. Individual differences in connectivity between premotor and visual regions correlated inversely with reaction time. Reaction times suggested more efficient executive control in participants with insomnia compared with good sleepers. During the executive control part, participants with insomnia showed stronger connectivity of thalamic parts of the arousal circuit with the middle frontal and the occipital gyri. Conversely, connectivity between the inferior and superior frontal gyri was weaker. Participants with insomnia seem to recruit more cortical resources in visuo-motor regions to orient attention than good sleepers do, and seem to have enhanced executive control that relates to stronger connectivity of arousal-related thalamic areas. This latter result should be treated with caution and requires confirmation.

Sex differences in the neural underpinnings of unimanual and bimanual control in adults.

While many of the movements we make throughout our day involve just one upper limb, most daily movements require a certain degree of coordination between both upper limbs. Historically, sex differences in eye-hand coordination have been observed. As well, there are demonstrated sex-specific differences in hemisphere symmetry, interhemispheric connectivity, and motor cortex organization. While it has been suggested that these anatomical differences may underlie sex-related differences in performance, sex differences in the functional neural correlate underlying bimanual performance have not been explicitly investigated. In the current study we tested the hypothesis that the functional connectivity underlying bimanual movement control differed depending on the sex of an individual. Participants underwent MRI scanning to acquire anatomical and functional brain images. During the functional runs, participants performed unimanual and bimanual coordination tasks using two button boxes. The tasks included pressing the buttons in time to an auditory cue with either their left or their right hand individually (unimanual), or with both hands simultaneously (bimanual). The bimanual task was further divided into either an in-phase (mirror/symmetrical) or anti-phase (parallel/asymmetrical) condition. Participants were provided with extensive training to ensure task comprehension, and performance error rates were found to be equivalent between men and women. A generalized psychophysiological interaction (gPPI) analysis was implemented to examine how functional connectivity in each condition was modulated by sex. In support of our hypothesis, women and men demonstrated differences in the neural correlates underlying unimanual and bimanual movements. In line with previous literature, functional connectivity patterns showed sex-related differences for right- vs left-hand movements. Sex-specific functional connectivity during bimanual movements was not a sum of the functional connectivity underlying right- and left-hand unimanual movements. Further, women generally showed greater interhemispheric functional connectivity across all conditions compared to men and had greater connectivity between task-related cortical areas, while men had greater connectivity involving the cerebellum. Sex differences in brain connectivity were associated with both unimanual and bimanual movement control. Not only do these findings provide novel insight into the fundamentals of how the brain controls bimanual movements in both women and men, they also present potential clinical implications on how bimanual movement training used in rehabilitation can best be tailored to the needs of individuals.

Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis.

OBJECTIVE: Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE. METHODS: We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity. RESULTS: The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE. SIGNIFICANCE: Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.

Neuropsychological profile of executive functions in autism spectrum disorder and schizophrenia spectrum disorders: a comparative group study in adults.

As assessed by numerous neuropsychological tasks, individuals with autism spectrum disorder (ASD) and schizophrenia spectrum disorders (SSDs) have similar impairments related to executive functions (EFs). The neuropsychological profile of these two conditions was examined using the three-component EFs' framework of Miyake and Friedman (Cogn Psychol 41(1):49-100, 2000). This approach assesses Inhibition (suppression of unwanted and irrelevant information/responses), Updating (use and control of contents of working memory), and Shifting (disengagement between activities or mental tasks) using nine different tasks. In line with previous research, we expected greater performance deficits in ASD in all three components compared to SSD, as well as faster responses for the SSD group. A self-paced task format allowed us to examine whether unlimited time given for a task would lead to better performance. The sample was constituted by the control group (N = 25), ASD group (N = 24), and SSD group (N = 12). Groups did not differ on Inhibition performance. In Updating, individuals with SSD performed poorer than the other groups. As for Shifting, both groups demonstrated poorer performance compared to controls, with the SSD group presenting the greatest difficulties. In terms of reaction time (RT), SSD participants' RT were the slowest on Inhibition and Shifting tasks. There was a positive correlation between performance and time spent on Inhibition and Shifting only for the SSD group, which demonstrates that their performance improves when there are no time constraints. Our work provides a better understanding of spared and impaired EFs, which could be useful for designing strategies aimed at improving specific EFs in each group.

The Case for a True Task-Based Curriculum for Medical Spanish Courses.

ISSUE: For decades, professional healthcare schools have invested considerable time and resources into the development and implementation of medical Spanish courses. However, most (if not all) of these courses and programs were developed without significant input from experts in the fields of applied linguistics and second language acquisition (SLA). This resulted in programs and courses which differ in every conceivable way, most notably in course objectives, course content, and assessment measures. Despite multiple calls by applied linguists over the years, there has never been a systematic evaluation of medical Spanish programs through the lens of applied linguistics. The literature to date also demonstrates a near-complete absence of collaboration between medical school faculty, applied linguists, and language teachers, furthering the divide between what is now well-documented in the applied linguistics and SLA literature and the reported classroom practices in the medical Spanish context. EVIDENCE: This article begins by contextualizing the development of medical Spanish courses under a backdrop of a critical need for multilingual medical professionals to better address documented healthcare disparities for Low English Proficiency (LEP) Spanish speakers, a steadily growing population in the United States. Then, the article introduces Task-Based Language Teaching (TBLT) as an opportunity for unprecedented collaboration between healthcare practitioners, medical Spanish course instructors, and applied linguists to improve medical Spanish curricular offerings. It goes on to identify specific opportunities for collaboration between medical Spanish professionals (instructors, course designers, program administrators) and applied linguists by highlighting three major areas for further development in the medical Spanish context. This collaboration would result in a pedagogical framework strongly rooted in applied linguistics research findings with direct, measurable impacts on both L2 Spanish learners in medical Spanish courses and the patients they will serve. IMPLICATIONS: Based on identified opportunities for curricular and programmatic improvement in published medical Spanish course reports, this article provides a rationale for TBLT as well as an overview of the process of TBLT course development. This detailed overview of TBLT course development with specific reference to the medical Spanish context presents an argument for the adoption of TBLT as an evidence-based and pedagogically viable alternative to current course offerings in medical Spanish programs. This article aims to identify a clear path forward to enable medical school program administrators and course and curriculum designers to take concrete steps to align courses with current best practices in second language pedagogy in order to optimize Spanish learning outcomes for their programs' specific students.

Development of an interprofessional task-based learning program in the field of occupational health: a content validity study.

OBJECTIVE: One of the duties of the educational system is to provide situations in which students learn the tasks corresponding to their future careers in an interprofessional team. This study was designed to develop an interprofessional task-based training program. METHODS: This was a curriculum development study conducted by content validity methodology in two stages: 1) 'framework development' which resulted in the creation of the framework items; and 2) 'evaluation of the framework' (judgment and quantification). The first stage consisted of task identification, generation of sub-tasks, and assimilation of items into a usable format. The second stage consisted of the judgment -quantification of the content validity of items and the framework. After that, the framework of the tasks of the occupational health team was finalized in the expert panel. After explaining the tasks, a matrix for task-expected roles in the occupational health team and a matrix for task-required skills to perform each task were developed. The next step determined the appropriate teaching and assessment methods for each task. Finally, an expert panel reviewed and approved the components of the interprofessional task-based training program. RESULTS: Integrating the interprofessional education strategy with task-based learning was considered innovative in occupational health team training. In the development stage, 48 items were extracted, and then 35 tasks were generated in the step of identification of tasks. In the second step, 174 sub-tasks were developed. The tasks and sub-tasks were categorized into seven areas. After the stage of evaluation of the framework, 33 tasks were categorized into seven main areas, including "assessment and identification of workplace hazards" (n = 10), "control of occupational hazards" (n = 4), "determining the appropriate job position for each person" (n = 3), "occupational health examinations" (n = 6), "management of occupational/work-related diseases" (n = 5), "inter-organizational and inter-disciplinary relations, and legal judgment" (n = 3) and "education and scholarship in occupational health services" (n = 2). CONCLUSION: The results of the present study can be used in developing the use of the interprofessional strategy and task-based training as two appropriate strategies for the purposeful development of learners' abilities in the fields involved in providing occupational health services in their future careers.

Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI.

Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final images and functional connectivity networks. However, there is no sufficient information about the effects of the Gaussian kernel size on group-level results for different cases yet. This study investigates the influence of kernel size on functional connectivity networks and network parameters in whole-brain rs-fMRI and tb-fMRI analyses of healthy adults. The analysis includes {0, 2, 4, 6, 8, 10} mm kernels, commonly used in practical analyses, covering all major brain networks. Graph theoretical measures such as betweenness centrality, global/local efficiency, clustering coefficient, and average path length are examined for each kernel. Additionally, principal component analysis (PCA) and independent component analysis (ICA) parameters, namely kurtosis and skewness, are evaluated for the functional images. The findings demonstrate that kernel size directly affects node connections, resulting in modifications to functional network structures and PCA/ICA parameters. However, network metrics exhibit greater resilience to these changes.

Effectiveness of engagement initiatives across engagement platforms: A meta-analysis.

As part of their customer engagement (CE) marketing, firms use different platforms to interact with customers, in ways that go beyond purchases. Task-based CE strategies call for customers' participation in structured, often incentivized tasks; experiential CE initiatives instead aim to stimulate pleasurable experiences for customers. But the optimal uses of these two strategies, in terms of improving customer engagement to produce more positive marketing outcomes, are unclear. With a meta-analysis and data from 395 samples, pertaining to 434,233 customers, the present study develops and tests a unifying framework of how to optimize investments in both two engagement strategies across different engagement platforms. On average, task-based initiatives are more effective in driving customer engagement, but the effects depend on the platform. If platforms support continuous or lean interactions, task-based initiatives are more effective; on platforms that encourage spot interactions, experiential initiatives are preferable. Three customer engagement dimensions (cognitive, emotional, and behavioral) in turn lead to positive marketing outcomes, though in ways that depend on the platforms' interaction characteristics (intensity, richness, initiation) and differ across digital versus physical platforms. These results provide clear guidance for managers regarding how to plan their CE marketing activities to benefit both their firms and their customers. Supplementary information: The online version contains supplementary material available at 10.1007/s11747-023-00925-7.