Conceptual Challenges and Directions for Social Neuroscience

Social neuroscience has been enormously successful and is making major contributions to fields ranging from psychiatry to economics. Yet deep and interesting conceptual challenges abound. Is social information processing domain specific? Is it universal or susceptible to individual differences and effects of culture? Are there uniquely human social cognitive abilities? What is the “social brain,” and how do we map social psychological processes onto it? Animal models together with fMRI and other cognitive neuroscience approaches in humans are providing an unprecedented level of detail and many surprising results. It may well be that social neuroscience in the near future will give us an entirely new view of who we are, how we evolved, and what might be in store for the future of our species

Human Lesion Studies in the 21st Century

The study of patients with brain lesions has made major historical contributions to cognitive neuroscience. Here I argue for an increased investment in modern lesion mapping, complementing fMRI studies and laying the conceptual and analytic foundations for future techniques that could experimentally manipulate human brain function

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic research in cognitive science and neuroscience. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, recently several neural encoding and decoding models have been proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a brief summary and discussion about future trends. Given the large amount of recently published work in the `computational cognitive neuroscience' community, we believe that this survey nicely organizes the plethora of work and presents it as a coherent story.Comment: 16 pages, 10 figure

Laminar fMRI: applications for cognitive neuroscience

The cortex is a massively recurrent network, characterized by feedforward and feedback connections between brain areas as well as lateral connections within an area. Feedforward, horizontal and feedback responses largely activate separate layers of a cortical unit, meaning they can be dissociated by lamina-resolved neurophysiological techniques. Such techniques are invasive and are therefore rarely used in humans. However, recent developments in high spatial resolution fMRI allow for non-invasive, in vivo measurements of brain responses specific to separate cortical layers. This provides an important opportunity to dissociate between feedforward and feedback brain responses, and investigate communication between brain areas at a more fine- grained level than previously possible in the human species. In this review, we highlight recent studies that successfully used laminar fMRI to isolate layer-specific feedback responses in human sensory cortex. In addition, we review several areas of cognitive neuroscience that stand to benefit from this new technological development, highlighting contemporary hypotheses that yield testable predictions for laminar fMRI. We hope to encourage researchers with the opportunity to embrace this development in fMRI research, as we expect that many future advancements in our current understanding of human brain function will be gained from measuring lamina-specific brain responses

Continuity in the neural system supporting children's theory of mind development: Longitudinal links between task-independent EEG and task-dependent fMRI.

Children's explicit theory of mind (ToM) understandings change over early childhood. We examined whether there is longitudinal stability in the neurobiological bases of ToM across this time period. A previous study found that source-localized resting EEG alpha attributable to the dorsal medial prefrontal cortex (DMPFC) and right temporoparietal junction (RTPJ) was associated with children's performance on a battery of theory of mind tasks. Here, we investigated a small subset of children (N = 12) in that original study as a preliminary investigation of whether behavioral measures of ToM performance, and/or EEG localized to the DMPFC or RTPJ predicted ToM-specific fMRI responses 3.5 years later. Results showed that preschoolers' behavioral ToM-performance positively predicted later ToM-specific fMRI responses in the DMPFC. Preschoolers' resting EEG attributable to the DMPFC also predicted later ToM-specific fMRI responses in the DMPFC. Given the small sample, results represent a first exploration and require replication. Intriguingly, they suggest that early maturation of the area of the DMPFC related to ToM reasoning is positively linked with its specific recruitment for ToM reasoning later in development, affording implications for characterizing conceptual ToM development, and its underlying neural supports

Connecting Levels of Analysis in Educational Neuroscience: A Review of Multi-level Structure of Educational Neuroscience with Concrete Examples

In its origins educational neuroscience has started as an endeavor to discuss implications of neuroscience studies for education. However, it is now on its way to become a transdisciplinary field, incorporating findings, theoretical frameworks and methodologies from education, and cognitive and brain sciences. Given the differences and diversity in the originating disciplines, it has been a challenge for educational neuroscience to integrate both theoretical and methodological perspective in education and neuroscience in a coherent way. We present a multi-level framework for educational neuroscience, which argues for integration of multiple levels of analysis, some originating in brain and cognitive sciences, others in education, as a roadmap for the future of educational neuroscience with concrete examples in moral education

Advances in functional neuroanatomy: a review of combined DTI and fMRI studies in healthy younger and older adults.

Structural connections between brain regions are thought to influence neural processing within those regions. It follows that alterations to the quality of structural connections should influence the magnitude of neural activity. The quality of structural connections may also be expected to differentially influence activity in directly versus indirectly connected brain regions. To test these predictions, we reviewed studies that combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) in younger and older adults. By surveying studies that examined relationships between DTI measures of white matter integrity and fMRI measures of neural activity, we identified variables that accounted for variability in these relationships. Results revealed that relationships between white matter integrity and neural activity varied with (1) aging (i.e., positive and negative DTI-fMRI relationships in younger and older adults, respectively) and (2) spatial proximity of the neural measures (i.e., positive and negative DTI-fMRI relationships when neural measures were extracted from adjacent and non-adjacent brain regions, respectively). Together, the studies reviewed here provided support for both of our predictions

Brain matters…in social sciences

Here we offer a general introduction to cognitive neuroscience and provide examples relevant to psychology, healthcare and bioethics, law and criminology, information studies, of how brain studies have influenced, are influencing or show the potential to influence the social sciences. We argue that social scientists should read, and be enabled to understand, primary sources of evidence in cognitive neuroscience. We encourage cognitive neuroscientists to reflect upon the resonance that their work may have across the social sciences and to facilitate a mutually enriching interdisciplinary dialogue