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We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

The purpose of this paper is to examine the changes of brain functional network after electroconvulsive therapy (ECT) treatment in major depressive disorder (MDD).

In this study, resting electroencephalography (EEG) is used to explore the changes in spectral power density, functional connectivity and network topology elicited by an acute open-label course of ECT in a group of 19 MDD subjects. The brain functional network based on Pearson correlation is constructed in a continuous threshold space (0.38–0.59). Complex network theory is used to analyze the network characteristic such as the length of the characteristic path, clustering coefficient, degree, betweenness centrality, global efficiency and small-world architecture.

The results show that ECT increased the spectral power density of Delta, Theta and Alpha1 bands and the full frequency. ECT increases the functional connectivity in Delta and full frequency and reduces the functional connectivity in Alpha2 band. In the selected threshold space, the clustering coefficient, global efficiency and small-world attributes of the network are changed significantly after ECT.

The findings indicate that resting EEG could effectively characterize the changes of brain functional networks following ECT in MDD. The results provide a theoretical basis to explore the neurophysiological mechanism of ECT in the field of MDD treatment.

An estimate is made of the connectivity of a mammalian neuron, i.e., the number of other neurons to or from which an average neuron directly connects. The value derived is about 10. Some functional implications of the value of the connectivity are considered, particularly mental illness, epilepsy, and intelligence. The “length” and “width”, in terms of neurons, of a functional neural channel are discussed.

How does the human brain absorb information and turn it into skills of its own in psychotherapy? In an attempt to answer this question, the authors will review the intricacies of processing channels in psychotherapy and propose the term transprocessing (as in transduction and processing combined) for the underlying mechanisms. Through transprocessing the brain processes multimodal memories and creates reparative solutions in the course of psychotherapy. Transprocessing is proposed as a stage-sequenced mechanism of deconstruction of engrained patterns of response. Through psychotherapy, emotional-cognitive reintegration and its consolidation is accomplished. This process is mediated by cellular and neural plasticity changes.

This study is the first to examine consumer’s neural reaction to different merchandising communication strategies at the point-of-sale (PoS) by applying functional near-infrared spectroscopy (fNIRS). By doing so, the purpose of this study is to extend consumer neuroscience to retail and shopper research.

Two experiments were conducted in which 36 shoppers were exposed to a realistic grocery shopping scenario while their brain haemodynamics were measured using mobile fNIRS.

Results revealed that mobile fNIRS appears a valid method to study neural activation of the prefrontal cortex (PFC) in the field of “shopper neuroscience”. More precisely, results demonstrated that the orbitofrontal cortex (OFC) might be crucial for processing and predicting merchandising communication strategy effectiveness.

This research gives evidence that certain regions of the PFC, in particular the OFC and the dorsolateral prefrontal cortex (dlPFC), are crucial to process and evaluate merchandising communication strategies.

The current work opens a promising new avenue for studying and understanding shopper’s behaviour. Mobile fNIRS enables marketing management to collect neural data from shoppers and analyse neural activity associated with real-life settings. Furthermore, based on a better understanding of shoppers’ perceptual processes of communication strategies, marketers can design more effective merchandising communication strategies.

The study is the first to implement the innovative, mobile neuroimaging method of fNIRS to a PoS setting. It, therefore, opens up the promising field of “shopper neuroscience”.

The paper aims to further understanding of connectivity from the perspective of blockchain technology (BT) in the supply chain (SC). It presents the weaknesses (inhibitors) of connectivity during technology implementation, focusing on supply chain interaction and resilience.

Restricting the focus to digital connectivity, interaction and supply chain resilience, this paper uses a systematic literature review (SLR) to examine how the literature has addressed, related or flagged connectivity weaknesses affecting supply chain interaction and resilience.

This study highlights the influence of connectivity for blockchain-technology projects. Technical and organisational influencers that affect the adoption of technology in the SC are presented. These influencers support the factors proposed in this study regarding the weaknesses that negatively affect the interaction between the agents involved and the SC's resilience. The research suggests that the weaknesses are related to technical needs and the relationships between companies arising from functionalities.

This paper is restricted to a review of the theory and the researched material. Although the author was careful to choose the best search terms related to the research objective, some potentially relevant articles may have been excluded.

The study summarises research on blockchain connectivity influencers in the SC, helping managers to anticipate and mitigate some of doubts and concerns in projects of this nature.

This is one of the first articles in the area of operations and SCs that addresses the topic of connectivity, focusing on its restrictive factors (connectivity inhibitors), in the context of blockchain implementation in the SC.

“Internet of things” is a broad term used to describe network connectivity to physical objects. Called connectable or smart objects, they are embedded in electronic circuits and software that make them capable of detecting, collecting and transmitting data and information. This paper aims to examine the influence of the attributes of “internet of things” products in the functional and emotional experience of purchase intention.

The study used a model adapted from Yaping et al. (2014), with adjustment for the constructs “emotional experience”, “functional experience” and “purchase intention”. The survey consisted of a sample of 747 valid questionnaires regarding users of “internet of things” products, through a structured questionnaire with 36 assertions, which were answered based on the Likert scale. The quantitative research approach followed an exploratory descriptive phase followed by the application of structural equation modeling.

Results validated most of the relationships of the model, with high levels of significance. In addition, there was a greater influence of emotional experience than functional on purchase intention for the selected sample, which mainly consisted of young people.

In short, the study confirmed the statistical significance of the structural paths, indicating that the proposed model is consistent, and with an appropriate adjustment can be applied in future research.

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 article examines aspects of information communication technology (ICT) connectivity among the understudied population of low-income older adults living in rural and peri-urban subsidized housing. We aim to investigate if variations exist in access and connectivity when economic and housing conditions are constant and use data from northern New England.

The multidisciplinary, mixed-methods approach involved administering structured surveys using iPads with senior residents (n = 91) from five housing sites, qualitative observations by field researchers and an ecological assessment of ICT resources at housing, community and state levels.

All subsidized housing sites were broadband accessible and nearby libraries. Fewer sites had Wi-Fi freely available to residents, and individual residents disparately accessed the Internet. Age and education demonstrably influenced ICT use of social media and email. Technology in the form of iPads used for surveys posed functional challenges for some older adults, but these technology-mediated interactions were also perceived as important sites of sociability.

Older adults disparately access and use ICT relative to socioeconomic status even as housing conditions remain constant, and access and use influences frequency of social connections with friends and family. The findings reveal factors that contribute to the existing digital divide facing older adults and broader lack of digital equity.

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.