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1. Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts

   https://doi.org/10.1038/s41539-020-0065-x  

   Cetron, Joshua S.; Connolly, Andrew C.; Diamond, Solomon G.; May, Vicki V.; Haxby, James V.; Kraemer, David J. (December 2020, npj Science of Learning)

   null (Ed.)

   Abstract How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices’ when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world. 

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2. Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results

   https://doi.org/10.1162/netn_a_00050  

   Bottenhorn, Katherine L.; Flannery, Jessica S.; Boeving, Emily R.; Riedel, Michael C.; Eickhoff, Simon B.; Sutherland, Matthew T.; Laird, Angela R. (January 2019, Network Neuroscience)

   Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior. 

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3. Control-theoretic integration of stimulation and electrophysiology for cognitive enhancement

   https://doi.org/10.3389/fnimg.2022.982288  

   Singh, Matthew F.; Cole, Michael W.; Braver, Todd S.; Ching, ShiNung (November 2022, Frontiers in Neuroimaging)

   Transcranial electrical stimulation (tES) technology and neuroimaging are increasingly coupled in basic and applied science. This synergy has enabled individualized tES therapy and facilitated causal inferences in functional neuroimaging. However, traditional tES paradigms have been stymied by relatively small changes in neural activity and high inter-subject variability in cognitive effects. In this perspective, we propose a tES framework to treat these issues which is grounded in dynamical systems and control theory. The proposed paradigm involves a tight coupling of tES and neuroimaging in which M/EEG is used to parameterize generative brain models as well as control tES delivery in a hybrid closed-loop fashion. We also present a novel quantitative framework for cognitive enhancement driven by a new computational objective: shaping how the brain reacts to potential “inputs” (e.g., task contexts) rather than enforcing a fixed pattern of brain activity. 

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4. Computational approaches to the neuroscience of social perception

   https://doi.org/10.1093/scan/nsaa127  

   Brooks, Jeffrey A; Stolier, Ryan M; Freeman, Jonathan B (September 2020, Social Cognitive and Affective Neuroscience)

   null (Ed.)

   Abstract Across multiple domains of social perception - including social categorization, emotion perception, impression formation, and mentalizing - multivariate pattern analysis (MVPA) of fMRI data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception. 

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5. Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

   https://doi.org/10.1017/S0890060423000021  

   Hu, Mo; McComb, Christopher; Goucher-Lambert, Kosa (January 2023, Artificial Intelligence for Engineering Design, Analysis and Manufacturing)

   Abstract The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial properties, HMM enables an automatic inference on states in neurocognitive activation data that are highly likely to occur in concept generation. The states with a higher likelihood of occupancy show more activation in the brain regions from the executive control network, the default mode network, and the middle temporal cortex. Different activation patterns and transfers are associated with these states, linking to varying cognitive functions, for example, semantic processing, memory retrieval, executive control, and visual processing, that characterize possible transitions in cognition related to concept generation. HMM offers new insights into cognitive dynamics in design by uncovering the temporal and spatial patterns in neurocognition related to concept generation. Future research can explore new avenues of data analysis methods to investigate design neurocognition and provide a more detailed description of cognitive dynamics in design. 

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