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Neuroimaging research has substantially enhanced our understanding of the neurobiological mechanisms of typical and atypical learning in children. These developments can advance the design of novel approaches to diagnosis and intervention for learning disabilities. Despite the promise of educational neuroscience, there are still walls between neuroscience and special education researchers such that more collaboration and understanding are needed between these disciplines. This chapter attempts to break down the walls by discussing how neuroimaging techniques can be incorporated into special education research. We also present arguments as to why neuroscience is “the next big thing” in special education research and the obstacles that must be overcome in order for neuroscience to be incorporated into education research. To describe how neurobiology might impact special education, we focus primarily on reading disability. We believe that educational neuroscience can aid in the identification and intervention of other learning disorders as well.

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.

In this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.

The proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.

The model can be used by doctors to identify the exact location of the tumorous region.

The proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

The most commonly described components of customer experience include cognitive and affective aspects. However, the subjective self-reported methods traditionally applied in tourism research cannot fully represent the instant, dynamic, and affective nature of customer experience. Therefore, there is a need for moment-based approaches and longitudinal methods in tourism research. The chapter provides a selective review of measures that can be used to assess the affective aspects of customer experience. Taking into account the advantages and limitations of each method, the integration of self-reported scales, moment-based psychophysiological techniques, and longitudinal methods should be considered as the best approach to measuring affective components of customer experience in tourism. This holistic interdisciplinary approach will help researchers and tourism practitioners understand the relationship between affective and cognitive components of tourists' pre-visit, onsite, and post-visit experience, as well as evaluate the effectiveness of marketing campaigns, identify weak points of tourists' customer journey, and maximize total travel experience.

Without having a shared operationalization of what constitutes a direct-to-consumer advertising (DTCA) exposure, it is impossible to accurately generalize findings about their effects. First, it needs to be established how the variables involved in exposures impact outcomes. This will allow for more accurate operationalizations.

A sample of 216 participants were recruited from Amazon’s Mechanical Turk and randomly assigned into one of four conditions to take an online survey. A 2 × 2 experiment (active/passive attention × low/high exposure) was conducted to determine if the level of attention, otherwise known as attentiveness, and the number of exposures impacted preferences for a fictitious prescription sleep aid.

Results indicated a significant difference among active and passive conditions such that active exposures resulted in stronger positive preferences.

Studies using different operationalizations should not be aggregated for generalizations about the effects of DTCA of prescription drugs.

This paper urges researchers to clearly operationalize their definitions for “exposure” and to be hesitant about generalizing findings studies using different definitions.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Autism spectrum disorder (ASD) has been studied as a neurodevelopmental disorder since Leo Kanner's early observations of abnormal head circumference in autistic children. In the past few years, there has been much progress made in elucidating the anatomical and functional abnormalities in ASD. This paper aims to summarise the extant research.

This paper provides a summary of relevant research findings in the neuroimaging of autism for the past 12 month period. Papers were identified using the Medline search terms: autism; ASD (functional); magnetic resonance imaging (MRI); neuroimaging; diffusion tensor imaging (DTI); and endophenotype.

Relatively recent techniques such as functional MRI and DTI have furthered the initial work derived from early histological and structural imaging studies. Even newer techniques, such as DTI tractography and support vector machine analysis, and other computer‐based learning methods have allowed us to move beyond regional variations in grey and white matter volume and study ASD as a disorder of connectivity, and of regional cerebral function and neural circuitry. Brain regions and neural circuits that are implicated in the core symptoms of ASD (deficits in social reciprocity, language and communication, and restricted and stereotyped interests) have been repeatedly shown to be abnormal in those individuals.

This paper aims to provide a background for clinicians to the current research and focuses on developments in the field of neuroimaging of ASD from the past year, which have generated further insights into the neurobiology of ASD.

This chapter presents a sociological analysis of the work involved in producing neuroimaging scans used in clinical practice. Drawing on fieldwork in magnetic resonance imaging (MRI) units in hospitals and free-standing imaging centers; in-depth interviews with technologists, radiologists, and neurologists; and reviews of relevant medical literatures, this analysis demonstrates how assembly line techniques structure neuroimaging work. Neuroimages (after being ordered by the referring clinician) are created in an image production line where scans of brains, breasts, livers, and other body parts are all produced: although some facilities may focus on one area of the body, most create an array of scans. Following MRI scans as they are produced demonstrates how medical work emphasizes repetition, specialization, and efficiency – key features of mass production. On the medical assembly line, the organization of work aims to transform patients into objects – ones that multiply as scans are created and circulated. Neurologists, radiologists, and technologists are positioned as skilled workers who manage the flow of bodies and the production of knowledge with the aim of producing health or, at the very least, knowledge of illness. Patients are also actors who actively impact the imaging production process. Previous scholarship has shown that diagnostic work involves a distributed form of expertise; one that involves patients, other medical professionals, machines, and neurologists. This chapter demonstrates that the deployment and synchronization of this expertise is a form of labor, involving distinct professions, professional hierarchies, and reimbursement systems. Working conditions are central to the production of MRI scans as knowledge and contribute to the social shaping of neuroimaging techniques.

We report the findings from a study exploring the experiences of individuals undergoing MRI scanning for research. Semi‐structured interviews took place before and after scanning with 17 participants; 12 were healthy volunteers and five were patients with a diagnosis of remitted depression. Themes of apprehension and curiosity prior to scanning were common in both groups. Patients were often confused about the procedure. Negative feelings were an issue at the outset, characterised by shock related to the physical surroundings, after which positive feelings, for example relaxation, were often experienced, and in the case of patients, learning more about their brain. Written information about imaging was deemed satisfactory; however the ability to ‘experience’ aspects of scanning beforehand was suggested. Scanning may be viewed as a process beginning prior to the procedure itself and involving positive and negative emotions. Increased information, reassurance and a more interactive intervention to reduce anxiety may be beneficial and may improve individuals' experience of this widely used procedure.