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Based on prior findings of content-specific beta synchronization in working memory and decision making, we hypothesized that beta oscillations support the (re-)activation of cortical representations by mediating neural ensemble fo...
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Based on prior findings of content-specific beta synchronization in working memory and decision making, we hypothesized that beta oscillations support the (re-)activation of cortical representations by mediating neural ensemble formation. We found that beta activity in monkey dorsolateral prefrontal cortex (dlPFC) and pre-supplementary motor area (preSMA) reflects the content of a stimulus in relation to the task context, regardless of its objective properties. In duration- and distance-categorization tasks, we changed the boundary between categories from one block of trials to the next. We found that two distinct beta-band frequencies were consistently associated with the two relative categories, with activity in these bands predicting the animals’ responses. We characterized beta at these frequencies as transient bursts, and showed that dlPFC and preSMA are connected via these distinct frequency channels. These results support the role of beta in forming neural ensembles, and further show that such ensembles synchronize at different beta frequencies. How the brain achieves context-dependent, flexible categorization remains poorly understood. By looking at neural ensemble formation, this study finds that distinct beta rhythms signal categorical decisions, and category-selective neurons synchronize at those frequencies.
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Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific...
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Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks—collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach—which leverages methods from the field of network neuroscience and graph theory—can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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The present paper discusses the application of organizational neuroscience in management research in Africa. In so doing, the paper draws from the field of neuroscience, organizational neuroscience, and cultural neuroscience to ex...
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The present paper discusses the application of organizational neuroscience in management research in Africa. In so doing, the paper draws from the field of neuroscience, organizational neuroscience, and cultural neuroscience to explore the extent to which topics, such as corruption, tribal identity, and nepotism could be analyzed through the lens of organizational neuroscience. The paper's implications for further research and management practice are discussed.
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In this article, we give an overview of the growing field of consumer neuroscience and discuss when and how it is useful to integrate neurophysiological data into research conducted in business fields. We first discuss the foundat...
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In this article, we give an overview of the growing field of consumer neuroscience and discuss when and how it is useful to integrate neurophysiological data into research conducted in business fields. We first discuss the foundational elements of consumer neuroscience and showcase a range of studies that highlight the ways that neuroscientific research and theory can add to existing lines of research in marketing. Next, we discuss the new domains and questions that brain data allow us to address, such as an emerging ability to predict market-level behavior in a range of decision types. We conclude by providing insights about the emerging frontiers in the field that we think will have an important impact on our understanding of marketing behavior, as well as organizational behavior.
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Abtract While research in social and affective neuroscience has a long history, it is only in the last few decades that it has been truly established as an independent field of investigation. In the Australian region, despite havi...
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Abtract While research in social and affective neuroscience has a long history, it is only in the last few decades that it has been truly established as an independent field of investigation. In the Australian region, despite having an even shorter history, this field of research is experiencing a dramatic rise. In this review, we present recent findings from a survey conducted on behalf of the Australasian Society for Social and Affective Neuroscience (AS4SAN) and from an analysis of the field to highlight contributions and strengths from our region (with a focus on Australia). Our results demonstrate that researchers in this field draw on a broad range of techniques, with the most common being behavioural experiments and neuropsychological assessment, as well as structural and functional magnetic resonance imaging. The Australian region has a particular strength in clinically driven research, evidenced by the types of populations under investigation, top cited papers from the region, and funding sources. We propose that the Australian region has potential to contribute to cross-cultural research and facilitating data sharing, and that improved links with international leaders will continue to strengthen this burgeoning field.
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This paper explores the question of “LatinX” through debates in affective and critical neuroscience regarding the “neurologization of self” that many theorists claim we are experiencing today. This exploration takes Oliver Sac
This paper explores the question of “LatinX” through debates in affective and critical neuroscience regarding the “neurologization of self” that many theorists claim we are experiencing today. This exploration takes Oliver Sacks’ case study “The Autist’s Artist” as its centerpiece and traces how the figure of “José” is narrativized as an autistic subject. The paper asks how we might understand “José” as a “LatinX” subject.
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A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attenti...
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A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique - the correlation generalized exponential random graph model (cGERGM) - to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs - consistent patterns of coactivation (i.e., connectivity) between collections of nodes that describe how the regions within a network are organized (e.g., clustering) - and anatomical properties - relationships between the regions that are dictated by anatomy (e.g., Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i.e., hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.
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Well-studied physiological mechanisms for relatively simple instinctive behaviors like sex and aggression are unlikely to have secondary roles, subservient to affect systems. Adding psychoanalytic ideation does not advance their u...
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Well-studied physiological mechanisms for relatively simple instinctive behaviors like sex and aggression are unlikely to have secondary roles, subservient to affect systems. Adding psychoanalytic ideation does not advance their understanding. However, concepts of the dynamic unconscious may find parallels in current day neuroscience.
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For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using...
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For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.
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