Journal articles on the topic 'Computational neuroimaging, cognitive neuroscience'
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Medaglia, John D., Mary-Ellen Lynall, and Danielle S. Bassett. "Cognitive Network Neuroscience." Journal of Cognitive Neuroscience 27, no. 8 (August 2015): 1471–91. http://dx.doi.org/10.1162/jocn_a_00810.
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Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
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McIntosh, Randy, Sean Hill, and Olaf Sporns. "Editorial: Focus feature on consciousness and cognition." Network Neuroscience 6, no. 4 (2022): 934–36. http://dx.doi.org/10.1162/netn_e_00273.
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Abstract Consciousness and cognition are an increasing focus of theoretical and experimental research in neuroscience, leveraging the methods and tools of brain dynamics and connectivity. This Focus Feature brings together a collection of articles that examine the various roles of brain networks in computational and dynamic models, and in studies of physiological and neuroimaging processes that underpin and enable behavioral and cognitive function.
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Rudrauf, David. "Structure-Function Relationships behind the Phenomenon of Cognitive Resilience in Neurology: Insights for Neuroscience and Medicine." Advances in Neuroscience 2014 (August 4, 2014): 1–28. http://dx.doi.org/10.1155/2014/462765.
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The phenomenon of cognitive resilience, that is, the dynamical preservation of normal functions despite neurological disorders, demonstrates that cognition can be highly robust to devastating brain injury. Here, cognitive resilience is considered across a range of neurological conditions. Simple computational models of structure-function relationships are used to discuss hypotheses about the neural mechanisms of resilience. Resilience expresses functional redundancies in brain networks and suggests a process of dynamic rerouting of brain signals. This process is underlined by a global renormalization of effective connectivity, capable of restoring information transfer between spared brain structures via alternate pathways. Local mechanisms of synaptic plasticity mediate the renormalization at the lowest level of implementation, but it is also driven by top-down cognition, with a key role of self-awareness in fostering resilience. The presence of abstraction layers in brain computation and networking is hypothesized to account for the renormalization process. Future research directions and challenges are discussed regarding the understanding and control of resilience based on multimodal neuroimaging and computational neuroscience. The study of resilience will illuminate ways by which the brain can overcome adversity and help inform prevention and treatment strategies. It is relevant to combating the negative neuropsychological impact of aging and fostering cognitive enhancement.
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Nadel, L., A. Samsonovich, L. Ryan, and M. Moscovitch. "Multiple trace theory of human memory: Computational, neuroimaging, and neuropsychological results." Hippocampus 10, no. 4 (2000): 352–68. http://dx.doi.org/10.1002/1098-1063(2000)10:4<352::aid-hipo2>3.0.co;2-d.
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Zmigrod, Leor, and Manos Tsakiris. "Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1822 (February 22, 2021): 20200130. http://dx.doi.org/10.1098/rstb.2020.0130.
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Although the study of political behaviour has been traditionally restricted to the social sciences, new advances in political neuroscience and computational cognitive science highlight that the biological sciences can offer crucial insights into the roots of ideological thought and action. Echoing the dazzling diversity of human ideologies, this theme issue seeks to reflect the multiplicity of theoretical and methodological approaches to understanding the nature of the political brain. Cutting-edge research along three thematic strands is presented, including (i) computational approaches that zoom in on fine-grained mechanisms underlying political behaviour, (ii) neurocognitive perspectives that harness neuroimaging and psychophysiological techniques to study ideological processes, and (iii) behavioural studies and policy-minded analyses of such understandings across cultures and across ideological domains. Synthesizing these findings together, the issue elucidates core questions regarding the nature of uncertainty in political cognition, the mechanisms of social influence and the cognitive structure of ideological beliefs. This offers key directions for future biologically grounded research as well as a guiding map for citizens, psychologists and policymakers traversing the uneven landscape of modern polarization, misinformation, intolerance and dogmatism. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms'.
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Popal, Haroon, Yin Wang, and Ingrid R. Olson. "A Guide to Representational Similarity Analysis for Social Neuroscience." Social Cognitive and Affective Neuroscience 14, no. 11 (November 1, 2019): 1243–53. http://dx.doi.org/10.1093/scan/nsz099.
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Abstract Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.
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Chatham, Christopher H., Seth A. Herd, Angela M. Brant, Thomas E. Hazy, Akira Miyake, Randy O'Reilly, and Naomi P. Friedman. "From an Executive Network to Executive Control: A Computational Model of the n-back Task." Journal of Cognitive Neuroscience 23, no. 11 (November 2011): 3598–619. http://dx.doi.org/10.1162/jocn_a_00047.
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A paradigmatic test of executive control, the n-back task, is known to recruit a widely distributed parietal, frontal, and striatal “executive network,” and is thought to require an equally wide array of executive functions. The mapping of functions onto substrates in such a complex task presents a significant challenge to any theoretical framework for executive control. To address this challenge, we developed a biologically constrained model of the n-back task that emergently develops the ability to appropriately gate, bind, and maintain information in working memory in the course of learning to perform the task. Furthermore, the model is sensitive to proactive interference in ways that match findings from neuroimaging and shows a U-shaped performance curve after manipulation of prefrontal dopaminergic mechanisms similar to that observed in studies of genetic polymorphisms and pharmacological manipulations. Our model represents a formal computational link between anatomical, functional neuroimaging, genetic, behavioral, and theoretical levels of analysis in the study of executive control. In addition, the model specifies one way in which the pFC, BG, parietal, and sensory cortices may learn to cooperate and give rise to executive control.
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Mujica-Parodi, Lilianne R., and Helmut H. Strey. "Making Sense of Computational Psychiatry." International Journal of Neuropsychopharmacology 23, no. 5 (March 27, 2020): 339–47. http://dx.doi.org/10.1093/ijnp/pyaa013.
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Abstract In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.
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Parkinson, Carolyn. "Computational methods in social neuroscience: recent advances, new tools and future directions." Social Cognitive and Affective Neuroscience 16, no. 8 (June 24, 2021): 739–44. http://dx.doi.org/10.1093/scan/nsab073.
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Abstract Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.
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Rojek-Giffin, Michael, Mael Lebreton, H. Steven Scholte, Frans van Winden, K. Richard Ridderinkhof, and Carsten K. W. De Dreu. "Neurocognitive Underpinnings of Aggressive Predation in Economic Contests." Journal of Cognitive Neuroscience 32, no. 7 (July 2020): 1276–88. http://dx.doi.org/10.1162/jocn_a_01545.
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Competitions are part and parcel of daily life and require people to invest time and energy to gain advantage over others and to avoid (the risk of) falling behind. Whereas the behavioral mechanisms underlying competition are well documented, its neurocognitive underpinnings remain poorly understood. We addressed this using neuroimaging and computational modeling of individual investment decisions aimed at exploiting one's counterpart (“attack”) or at protecting against exploitation by one's counterpart (“defense”). Analyses revealed that during attack relative to defense (i) individuals invest less and are less successful; (ii) computations of expected reward are strategically more sophisticated (reasoning level k = 4 vs. k = 3 during defense); (iii) ventral striatum activity tracks reward prediction errors; (iv) risk prediction errors were not correlated with neural activity in either ROI or whole-brain analyses; and (v) successful exploitation correlated with neural activity in the bilateral ventral striatum, left OFC, left anterior insula, left TPJ, and lateral occipital cortex. We conclude that, in economic contests, coming out ahead (vs. not falling behind) involves sophisticated strategic reasoning that engages both reward and value computation areas and areas associated with theory of mind.
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Krigolson, Olav E., Cameron D. Hassall, and Todd C. Handy. "How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans." Journal of Cognitive Neuroscience 26, no. 3 (March 2014): 635–44. http://dx.doi.org/10.1162/jocn_a_00509.
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Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors—discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833–1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129–141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769–776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679–709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.
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O’Reilly, Jill X., and Rogier B. Mars. "Computational neuroimaging: localising Greek letters? Comment on Forstmann et al." Trends in Cognitive Sciences 15, no. 10 (October 2011): 450. http://dx.doi.org/10.1016/j.tics.2011.07.012.
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Jaffe-Dax, Sagi, Alex M. Boldin, Nathaniel D. Daw, and Lauren L. Emberson. "A Computational Role for Top–Down Modulation from Frontal Cortex in Infancy." Journal of Cognitive Neuroscience 32, no. 3 (March 2020): 508–14. http://dx.doi.org/10.1162/jocn_a_01497.
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Recent findings have shown that full-term infants engage in top–down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top–down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top–down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top–down signals in young infants when they are engaged in a probabilistic associative learning.
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Ramey, Tatiana, and Paul S. Regier. "Cognitive impairment in substance use disorders." CNS Spectrums 24, no. 1 (December 28, 2018): 102–13. http://dx.doi.org/10.1017/s1092852918001426.
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Cognitive impairments in substance use disorders have been extensively researched, especially since the advent of cognitive and computational neuroscience and neuroimaging methods in the last 20 years. Conceptually, altered cognitive function can be viewed as a hallmark feature of substance use disorders, with documented alterations in the well-known “executive” domains of attention, inhibition/regulation, working memory, and decision-making. Poor cognitive (sometimes referred to as “top-down”) regulation of downstream motivational processes—whether appetitive (reward, incentive salience) or aversive (stress, negative affect)—is recognized as a fundamental impairment in addiction and a potentially important target for intervention. As addressed in this special issue, cognitive impairment is a transdiagnostic domain; thus, advances in the characterization and treatment of cognitive dysfunction in substance use disorders could have benefit across multiple psychiatric disorders. Toward this general goal, we summarize current findings in the abovementioned cognitive domains of substance use disorders, while suggesting a potentially useful expansion to include processes that bothprecede(precognition) andsupersede(social cognition) what is usually thought of as strictly cognition. These additional two areas have received relatively less attention but phenomenologically and otherwise are important features of substance use disorders. The review concludes with suggestions for research and potential therapeutic targeting of both the familiar and this more comprehensive version of cognitive domains related to substance use disorders.
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Decety, Jean, and Stephanie Cacioppo. "The speed of morality: a high-density electrical neuroimaging study." Journal of Neurophysiology 108, no. 11 (December 1, 2012): 3068–72. http://dx.doi.org/10.1152/jn.00473.2012.
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Neuroscience research indicates that moral reasoning is underpinned by distinct neural networks including the posterior superior temporal sulcus (pSTS), amygdala, and ventromedial prefrontal cortex, which support communication between computational systems underlying affective states, cognitions, and motivational processes. To characterize real-time neural processing underpinning moral computations, high-density event-related potentials were measured in participants while they viewed short, morally laden visual scenarios depicting intentional and accidental harmful actions. Current source density maxima in the right pSTS as fast as 62 ms poststimulus first distinguished intentional vs. accidental actions. Responses in the amygdala/temporal pole (122 ms) and ventromedial prefrontal cortex (182 ms) were then evoked by the perception of harmful actions, indicative of fast information processing associated with early stages of moral cognition. Our data strongly support the notion that intentionality is the first input to moral computations. They also demonstrate that emotion acts as a gain antecedent to moral judgment by alerting the individual to the moral salience of a situation and provide evidence for the pervasive role of affect in moral sensitivity and reasoning.
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Gardner, Justin L., and Elisha P. Merriam. "Population Models, Not Analyses, of Human Neuroscience Measurements." Annual Review of Vision Science 7, no. 1 (September 15, 2021): 225–55. http://dx.doi.org/10.1146/annurev-vision-093019-111124.
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Selectivity for many basic properties of visual stimuli, such as orientation, is thought to be organized at the scale of cortical columns, making it difficult or impossible to measure directly with noninvasive human neuroscience measurement. However, computational analyses of neuroimaging data have shown that selectivity for orientation can be recovered by considering the pattern of response across a region of cortex. This suggests that computational analyses can reveal representation encoded at a finer spatial scale than is implied by the spatial resolution limits of measurement techniques. This potentially opens up the possibility to study a much wider range of neural phenomena that are otherwise inaccessible through noninvasive measurement. However, as we review in this article, a large body of evidence suggests an alternative hypothesis to this superresolution account: that orientation information is available at the spatial scale of cortical maps and thus easily measurable at the spatial resolution of standard techniques. In fact, a population model shows that this orientation information need not even come from single-unit selectivity for orientation tuning, but instead can result from population selectivity for spatial frequency. Thus, a categorical error of interpretation can result whereby orientation selectivity can be confused with spatial frequency selectivity. This is similarly problematic for the interpretation of results from numerous studies of more complex representations and cognitive functions that have built upon the computational techniques used to reveal stimulus orientation. We suggest in this review that these interpretational ambiguities can be avoided by treating computational analyses as models of the neural processes that give rise to measurement. Building upon the modeling tradition in vision science using considerations of whether population models meet a set of core criteria is important for creating the foundation for a cumulative and replicable approach to making valid inferences from human neuroscience measurements.
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Husain, F. T., M. A. Tagamets, S. J. Fromm, A. R. Braun, and B. Horwitz. "Relating neuronal dynamics for auditory object processing to neuroimaging activity: a computational modeling and an fMRI study." NeuroImage 21, no. 4 (April 2004): 1701–20. http://dx.doi.org/10.1016/j.neuroimage.2003.11.012.
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Rasero, Javier, Amy Isabella Sentis, Fang-Cheng Yeh, and Timothy Verstynen. "Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability." PLOS Computational Biology 17, no. 3 (March 5, 2021): e1008347. http://dx.doi.org/10.1371/journal.pcbi.1008347.
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Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
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O'Toole, Alice J., Fang Jiang, Hervé Abdi, and James V. Haxby. "Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex." Journal of Cognitive Neuroscience 17, no. 4 (April 2005): 580–90. http://dx.doi.org/10.1162/0898929053467550.
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Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency “line drawings” of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.
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Hejazi, Sara, Waldemar Karwowski, Farzad V. Farahani, Tadeusz Marek, and P. A. Hancock. "Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review." Brain Sciences 13, no. 2 (January 31, 2023): 246. http://dx.doi.org/10.3390/brainsci13020246.
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(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph–theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.
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Zhang, Lei, Lukas Lengersdorff, Nace Mikus, Jan Gläscher, and Claus Lamm. "Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices." Social Cognitive and Affective Neuroscience 15, no. 6 (June 2020): 695–707. http://dx.doi.org/10.1093/scan/nsaa089.
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Abstract The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla–Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.
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Kawato, Mitsuo, and Aurelio Cortese. "From internal models toward metacognitive AI." Biological Cybernetics 115, no. 5 (October 2021): 415–30. http://dx.doi.org/10.1007/s00422-021-00904-7.
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AbstractIn several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition—the ability to monitor one’s own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the “cognitive reality monitoring network” (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a “responsibility signal” that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.
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Kini, Lohith G., James C. Gee, and Brian Litt. "Computational analysis in epilepsy neuroimaging: A survey of features and methods." NeuroImage: Clinical 11 (2016): 515–29. http://dx.doi.org/10.1016/j.nicl.2016.02.013.
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Cantlon, Jessica F., Melissa E. Libertus, Philippe Pinel, Stanislas Dehaene, Elizabeth M. Brannon, and Kevin A. Pelphrey. "The Neural Development of an Abstract Concept of Number." Journal of Cognitive Neuroscience 21, no. 11 (November 2009): 2217–29. http://dx.doi.org/10.1162/jocn.2008.21159.
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As literate adults, we appreciate numerical values as abstract entities that can be represented by a numeral, a word, a number of lines on a scorecard, or a sequence of chimes from a clock. This abstract, notation-independent appreciation of numbers develops gradually over the first several years of life. Here, using functional magnetic resonance imaging, we examine the brain mechanisms that 6- and 7-year-old children and adults recruit to solve numerical comparisons across different notation systems. The data reveal that when young children compare numerical values in symbolic and nonsymbolic notations, they invoke the same network of brain regions as adults including occipito-temporal and parietal cortex. However, children also recruit inferior frontal cortex during these numerical tasks to a much greater degree than adults. Our data lend additional support to an emerging consensus from adult neuroimaging, nonhuman primate neurophysiology, and computational modeling studies that a core neural system integrates notation-independent numerical representations throughout development but, early in development, higher-order brain mechanisms mediate this process.
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Moore, Adam, Sujin Hong, and Laura Cram. "Trust in information, political identity and the brain: an interdisciplinary fMRI study." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1822 (February 22, 2021): 20200140. http://dx.doi.org/10.1098/rstb.2020.0140.
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Misinformation has triggered government inquiries and threatens the perceived legitimacy of campaign processes and electoral outcomes. A new identity polarization has arisen between Remain and Leave sympathizers in the UK Brexit debate, with associated accusations of misinformation use. Competing psychological accounts of how people come to accept and defend misinformation pit self-reinforcing motivated cognition against lack of systematic reasoning as possible explanations. We harness insights from political science, cognitive neuroscience and psychology to examine the impact of trust and identity on information processing regarding Brexit in a group of Remain identifiers. Behaviourally, participants' affective responses to Brexit-related information are affected by whether the emotional valence of the message is compatible with their beliefs on Brexit (positive/negative) but not by their trust in the source of information. However, belief in the information is significantly affected by both (dis)trust in information source and by belief compatibility with the valence of the information. Neuroimaging results confirm this pattern, identifying areas involved in judgements of the self, others and automatic processing of affectively threatening stimuli, ultimately supporting motivated cognition accounts of misinformation endorsement. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.
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Collie, Alexander, Catherine Myers, Geoffrey Schnirman, Stephen Wood, and Paul Maruff. "Selectively Impaired Associative Learning in Older People with Cognitive Decline." Journal of Cognitive Neuroscience 14, no. 3 (April 1, 2002): 484–92. http://dx.doi.org/10.1162/089892902317361994.
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Older people with declining cognitive function typically display deficits in declarative memory processes, often most evident on tests of associative learning (AL). The hippocampal formation (HF) is thought to be critically involved in the encoding and retrieval of such associations, consistent with neuroimaging findings that the HF is damaged in early stages of neurodegenerative disease and in older people with AL impairments. In the clinic, older people with cognitive decline commonly report difficulties associating names with faces. However, we have observed that such people are particularly impaired on tests requiring the association of novel stimuli. In Experiment 1, a series of AL tasks were administered to older people with cognitive decline to determine whether they were impaired at simply making associations, or at making associations between novel stimuli. In Experiment 2, we measured HF function in these subjects by administering an AL task designed to differentiate between HF-damaged and HF-intact individuals. Our experimental protocols were guided by a computational model of HF function in AL described by Gluck and Myers (1997). Older people with cognitive decline displayed impaired performance on tasks designed to be highly dependent upon intact HF function, including a task in which novel patterns and spatial locations were to be associated. These results suggest that the AL impairments observed in older people with cognitive decline may be due to HF dysfunction.
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Knops, André. "Probing the Neural Correlates of Number Processing." Neuroscientist 23, no. 3 (May 17, 2016): 264–74. http://dx.doi.org/10.1177/1073858416650153.
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The cognitive and neural mechanisms that enable humans to encode and manipulate numerical information have been subject to an increasing number of experimental studies over the past 25 years or so. Here, I highlight recent findings about how numerical information is neurally coded, focusing on the theoretical implications derived from the most influential theoretical framework in numerical cognition—the Triple Code Model. At the core of this model is the assumption that bilateral parietal cortex hosts an approximate number system that codes for the cardinal value of perceived numerals. I will review studies that ask whether or not the numerical coding within this system is invariant to varying input notation, format, or modality, and whether or not the observed parietal activity is number-specific over and above the parietal involvement in response-related processes. Extant computational models of numerosity (the number of objects in a set) perception are summarized and related to empirical data from human neuroimaging and monkey neurophysiology.
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Schlegel, Alexander, Dedeepya Konuthula, Prescott Alexander, Ethan Blackwood, and Peter U. Tse. "Fundamentally Distributed Information Processing Integrates the Motor Network into the Mental Workspace during Mental Rotation." Journal of Cognitive Neuroscience 28, no. 8 (August 2016): 1139–51. http://dx.doi.org/10.1162/jocn_a_00965.
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The manipulation of mental representations in the human brain appears to share similarities with the physical manipulation of real-world objects. In particular, some neuroimaging studies have found increased activity in motor regions during mental rotation, suggesting that mental and physical operations may involve overlapping neural populations. Does the motor network contribute information processing to mental rotation? If so, does it play a similar computational role in both mental and manual rotation, and how does it communicate with the wider network of areas involved in the mental workspace? Here we used multivariate methods and fMRI to study 24 participants as they mentally rotated 3-D objects or manually rotated their hands in one of four directions. We find that information processing related to mental rotations is distributed widely among many cortical and subcortical regions, that the motor network becomes tightly integrated into a wider mental workspace network during mental rotation, and that motor network activity during mental rotation only partially resembles that involved in manual rotation. Additionally, these findings provide evidence that the mental workspace is organized as a distributed core network that dynamically recruits specialized subnetworks for specific tasks as needed.
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Dutta, Anirban. "Portable neuroimaging and computational modeling approach to elucidate potential cognitive confounds in non-invasive stimulation of the motor cerebellum." Brain Stimulation 14, no. 5 (September 2021): 1133–34. http://dx.doi.org/10.1016/j.brs.2021.06.010.
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Szinte, Martin, and Tomas Knapen. "Visual Organization of the Default Network." Cerebral Cortex 30, no. 6 (February 6, 2019): 3518–27. http://dx.doi.org/10.1093/cercor/bhz323.
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Abstract The default network (DN) is a brain network with correlated activities spanning frontal, parietal, and temporal cortical lobes. The DN activates for high-level cognition tasks and deactivates when subjects are actively engaged in perceptual tasks. Despite numerous observations, the role of DN deactivation remains unclear. Using computational neuroimaging applied to a large dataset of the Human Connectome Project (HCP) and to two individual subjects scanned over many repeated runs, we demonstrate that the DN selectively deactivates as a function of the position of a visual stimulus. That is, we show that spatial vision is encoded within the DN by means of deactivation relative to baseline. Our results suggest that the DN functions as a set of high-level visual regions, opening up the possibility of using vision-science tools to understand its putative function in cognition and perception.
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Walia, Pushpinder, Abhishek Ghosh, Shubhmohan Singh, and Anirban Dutta. "Portable Neuroimaging-Guided Noninvasive Brain Stimulation of the Cortico-Cerebello-Thalamo-Cortical Loop—Hypothesis and Theory in Cannabis Use Disorder." Brain Sciences 12, no. 4 (March 26, 2022): 445. http://dx.doi.org/10.3390/brainsci12040445.
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Background: Maladaptive neuroplasticity-related learned response in substance use disorder (SUD) can be ameliorated using noninvasive brain stimulation (NIBS); however, inter-individual variability needs to be addressed for clinical translation. Objective: Our first objective was to develop a hypothesis for NIBS for learned response in SUD based on a competing neurobehavioral decision systems model. The next objective was to develop the theory by conducting a computational simulation of NIBS of the cortico-cerebello-thalamo-cortical (CCTC) loop in cannabis use disorder (CUD)-related dysfunctional “cue-reactivity”—a construct closely related to “craving”—that is a core symptom. Our third objective was to test the feasibility of a neuroimaging-guided rational NIBS approach in healthy humans. Methods: “Cue-reactivity” can be measured using behavioral paradigms and portable neuroimaging, including functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) metrics of sensorimotor gating. Therefore, we conducted a computational simulation of NIBS, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) of the cerebellar cortex and deep cerebellar nuclei (DCN) of the CCTC loop for its postulated effects on fNIRS and EEG metrics. We also developed a rational neuroimaging-guided NIBS approach for the cerebellar lobule (VII) and prefrontal cortex based on a healthy human study. Results: Simulation of cerebellar tDCS induced gamma oscillations in the cerebral cortex, while transcranial temporal interference stimulation induced a gamma-to-beta frequency shift. A preliminary healthy human study (N = 10) found that 2 mA cerebellar tDCS evoked similar oxyhemoglobin (HbO) response in the range of 5 × 10−6 M across the cerebellum and PFC brain regions (α = 0.01); however, infra-slow (0.01–0.10 Hz) prefrontal cortex HbO-driven phase–amplitude-coupled (PAC; 4 Hz, ±2 mA (max)) cerebellar tACS evoked HbO levels in the range of 10−7 M that were statistically different (α = 0.01) across these brain regions. Conclusion: Our healthy human study showed the feasibility of fNIRS of cerebellum and PFC and closed-loop fNIRS-driven ctACS at 4 Hz, which may facilitate cerebellar cognitive function via the frontoparietal network. Future work needs to combine fNIRS with EEG for multi-modal imaging for closed-loop NIBS during operant conditioning.
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Binney, Richard J., Geoffrey J. M. Parker, and Matthew A. Lambon Ralph. "Convergent Connectivity and Graded Specialization in the Rostral Human Temporal Lobe as Revealed by Diffusion-Weighted Imaging Probabilistic Tractography." Journal of Cognitive Neuroscience 24, no. 10 (October 2012): 1998–2014. http://dx.doi.org/10.1162/jocn_a_00263.
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In recent years, multiple independent neuroscience investigations have implicated critical roles for the rostral temporal lobe in auditory and visual perception, language, and semantic memory. Although arising in the context of different cognitive functions, most of these suggest that there is a gradual convergence of sensory information in the temporal lobe that culminates in modality- and perceptually invariant representations at the most rostral aspect. Currently, however, too little is known regarding connectivity within the human temporal lobe to be sure of exactly how and where convergence occurs; existing hypotheses are primarily derived on the basis of cross-species generalizations from invasive nonhuman primate studies, the validity of which is unclear, especially where language function is concerned. In this study, we map the connectivity of the human rostral temporal lobe in vivo for the first time using diffusion-weighted imaging probabilistic tractography. The results indicate that convergence of sensory information in the temporal lobe is in fact a graded process that occurs along both its longitudinal and lateral axes and culminates in the most rostral limits. We highlight the consistency of our results with those of prior functional neuroimaging, computational modeling, and patient studies. By going beyond simple fasciculus reconstruction, we systematically explored the connectivity of specific temporal lobe areas to frontal and parietal language regions. In contrast to the graded within-temporal lobe connectivity, this intertemporal connectivity was found to dissociate across caudal, mid, and rostral subregions. Furthermore, we identified a basal rostral temporal region with very limited connectivity to areas outside the temporal lobe, which aligns with recent evidence that this subregion underpins the extraction of modality- and context-invariant semantic representations.
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Lambon Ralph, Matthew A. "Neurocognitive insights on conceptual knowledge and its breakdown." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1634 (January 19, 2014): 20120392. http://dx.doi.org/10.1098/rstb.2012.0392.
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Conceptual knowledge reflects our multi-modal ‘semantic database’. As such, it brings meaning to all verbal and non-verbal stimuli, is the foundation for verbal and non-verbal expression and provides the basis for computing appropriate semantic generalizations. Multiple disciplines (e.g. philosophy, cognitive science, cognitive neuroscience and behavioural neurology) have striven to answer the questions of how concepts are formed, how they are represented in the brain and how they break down differentially in various neurological patient groups. A long-standing and prominent hypothesis is that concepts are distilled from our multi-modal verbal and non-verbal experience such that sensation in one modality (e.g. the smell of an apple) not only activates the intramodality long-term knowledge, but also reactivates the relevant intermodality information about that item (i.e. all the things you know about and can do with an apple). This multi-modal view of conceptualization fits with contemporary functional neuroimaging studies that observe systematic variation of activation across different modality-specific association regions dependent on the conceptual category or type of information. A second vein of interdisciplinary work argues, however, that even a smorgasbord of multi-modal features is insufficient to build coherent, generalizable concepts. Instead, an additional process or intermediate representation is required. Recent multidisciplinary work, which combines neuropsychology, neuroscience and computational models, offers evidence that conceptualization follows from a combination of modality-specific sources of information plus a transmodal ‘hub’ representational system that is supported primarily by regions within the anterior temporal lobe, bilaterally.
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Chadderdon, George L., and Olaf Sporns. "A Large-scale Neurocomputational Model of Task-oriented Behavior Selection and Working Memory in Prefrontal Cortex." Journal of Cognitive Neuroscience 18, no. 2 (February 1, 2006): 242–57. http://dx.doi.org/10.1162/jocn.2006.18.2.242.
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The prefrontal cortex (PFC) is crucially involved in the executive component of working memory, representation of task state, and behavior selection. This article presents a large-scale computational model of the PFC and associated brain regions designed to investigate the mechanisms by which working memory and task state interact to select adaptive behaviors from a behavioral repertoire. The model consists of multiple brain regions containing neuronal populations with realistic physiological and anatomical properties, including extrastriate visual cortical regions, the inferotemporal cortex, the PFC, the striatum, and midbrain dopamine (DA) neurons. The onset of a delayed match-to-sample or delayed nonmatch-to-sample task triggers tonic DA release in the PFC causing a switch into a persistent, stimulus-insensitive dynamic state that promotes the maintenance of stimulus representations within prefrontal networks. Other modeled prefrontal and striatal units select cognitive acceptance or rejection behaviors according to which task is active and whether prefrontal working memory representations match the current stimulus. Working memory task performance and memory fields of prefrontal delay units are degraded by extreme elevation or depletion of tonic DA levels. Analyses of cellular and synaptic activity suggest that hyponormal DA levels result in increased prefrontal activation, whereas hypernormal DA levels lead to decreased activation. Our simulation results suggest a range of predictions for behavioral, single-cell, and neuroimaging response data under the proposed task set and under manipulations of DA concentration.
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Scerif, Gaia, Michael S. Worden, Matthew Davidson, Liat Seiger, and B. J. Casey. "Context Modulates Early Stimulus Processing when Resolving Stimulus-response Conflict." Journal of Cognitive Neuroscience 18, no. 5 (May 1, 2006): 781–92. http://dx.doi.org/10.1162/jocn.2006.18.5.781.
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When responding to stimuli in our environment, the presence of multiple items associated with task-relevant responses affects both ongoing response selection and subsequent behavior. Computational modeling of conflict monitoring and neuroimaging data predict that the recent context of response competition will bias the selection of certain stimuli over others very early in the processing stream through increased focal spatial attention. We used high-density EEG to test this hypothesis and to investigate the contextual effects on nonspatial, early stimulus processing in a modified flanker task. Subjects were required to respond to a central arrow and to ignore potentially conflicting information from flanking arrows in trials preceded by a series of either compatible or incompatible trials. On some trials, we presented the flanking arrows in the absence of the central target. The visual P1 component was selectively enhanced only for incompatible trials when preceded by incompatible ones, suggesting that contextual effects depend on feature-based processing, and not only simple enhancement of the target location. Context effects also occurred on no-target trials as evidenced by an enhanced early-evoked response when they followed compatible compared to incompatible trials, suggesting that spatial attention was also modulated by recent context. These results support a multi-componential account of spatial and nonspatial attention and they suggest that contextually driven cognitive control mechanisms can operate on specific stimulus features at extremely early stages of processing within stimulus-response conflict tasks.
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Mensch, Arthur, Julien Mairal, Bertrand Thirion, and Gaël Varoquaux. "Extracting representations of cognition across neuroimaging studies improves brain decoding." PLOS Computational Biology 17, no. 5 (May 3, 2021): e1008795. http://dx.doi.org/10.1371/journal.pcbi.1008795.
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Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.
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Rosenthal, Zachary P., Ryan V. Raut, Ping Yan, Deima Koko, Andrew W. Kraft, Leah Czerniewski, Benjamin Acland, et al. "Local Perturbations of Cortical Excitability Propagate Differentially Through Large-Scale Functional Networks." Cerebral Cortex 30, no. 5 (February 10, 2020): 3352–69. http://dx.doi.org/10.1093/cercor/bhz314.
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Abstract Electrophysiological recordings have established that GABAergic interneurons regulate excitability, plasticity, and computational function within local neural circuits. Importantly, GABAergic inhibition is focally disrupted around sites of brain injury. However, it remains unclear whether focal imbalances in inhibition/excitation lead to widespread changes in brain activity. Here, we test the hypothesis that focal perturbations in excitability disrupt large-scale brain network dynamics. We used viral chemogenetics in mice to reversibly manipulate parvalbumin interneuron (PV-IN) activity levels in whisker barrel somatosensory cortex. We then assessed how this imbalance affects cortical network activity in awake mice using wide-field optical neuroimaging of pyramidal neuron GCaMP dynamics as well as local field potential recordings. We report 1) that local changes in excitability can cause remote, network-wide effects, 2) that these effects propagate differentially through intra- and interhemispheric connections, and 3) that chemogenetic constructs can induce plasticity in cortical excitability and functional connectivity. These findings may help to explain how focal activity changes following injury lead to widespread network dysfunction.
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Marcus, Daniel S., Anthony F. Fotenos, John G. Csernansky, John C. Morris, and Randy L. Buckner. "Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults." Journal of Cognitive Neuroscience 22, no. 12 (December 2010): 2677–84. http://dx.doi.org/10.1162/jocn.2009.21407.
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The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have “mild cognitive impairment.” Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n = 62) and women (n = 88). For each scanning session, three or four individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast to noise, making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole-brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.
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Meyer, Georg F., Mark Greenlee, and Sophie Wuerger. "Interactions between Auditory and Visual Semantic Stimulus Classes: Evidence for Common Processing Networks for Speech and Body Actions." Journal of Cognitive Neuroscience 23, no. 9 (September 2011): 2291–308. http://dx.doi.org/10.1162/jocn.2010.21593.
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Incongruencies between auditory and visual signals negatively affect human performance and cause selective activation in neuroimaging studies; therefore, they are increasingly used to probe audiovisual integration mechanisms. An open question is whether the increased BOLD response reflects computational demands in integrating mismatching low-level signals or reflects simultaneous unimodal conceptual representations of the competing signals. To address this question, we explore the effect of semantic congruency within and across three signal categories (speech, body actions, and unfamiliar patterns) for signals with matched low-level statistics. In a localizer experiment, unimodal (auditory and visual) and bimodal stimuli were used to identify ROIs. All three semantic categories cause overlapping activation patterns. We find no evidence for areas that show greater BOLD response to bimodal stimuli than predicted by the sum of the two unimodal responses. Conjunction analysis of the unimodal responses in each category identifies a network including posterior temporal, inferior frontal, and premotor areas. Semantic congruency effects are measured in the main experiment. We find that incongruent combinations of two meaningful stimuli (speech and body actions) but not combinations of meaningful with meaningless stimuli lead to increased BOLD response in the posterior STS (pSTS) bilaterally, the left SMA, the inferior frontal gyrus, the inferior parietal lobule, and the anterior insula. These interactions are not seen in premotor areas. Our findings are consistent with the hypothesis that pSTS and frontal areas form a recognition network that combines sensory categorical representations (in pSTS) with action hypothesis generation in inferior frontal gyrus/premotor areas. We argue that the same neural networks process speech and body actions.
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Hackel, Leor M., Julian A. Wills, and Jay J. Van Bavel. "Shifting prosocial intuitions: neurocognitive evidence for a value-based account of group-based cooperation." Social Cognitive and Affective Neuroscience 15, no. 4 (April 2020): 371–81. http://dx.doi.org/10.1093/scan/nsaa055.
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Abstract Cooperation is necessary for solving numerous social issues, including climate change, effective governance and economic stability. Value-based decision models contend that prosocial tendencies and social context shape people’s preferences for cooperative or selfish behavior. Using functional neuroimaging and computational modeling, we tested these predictions by comparing activity in brain regions previously linked to valuation and executive function during decision-making—the ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC), respectively. Participants played Public Goods Games with students from fictitious universities, where social norms were selfish or cooperative. Prosocial participants showed greater vmPFC activity when cooperating and dlPFC-vmPFC connectivity when acting selfishly, whereas selfish participants displayed the opposite pattern. Norm-sensitive participants showed greater dlPFC-vmPFC connectivity when defying group norms. Modeling expectations of cooperation was associated with activity near the right temporoparietal junction. Consistent with value-based models, this suggests that prosocial tendencies and contextual norms flexibly determine whether people prefer cooperation or defection.
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Bohland, Jason W., Daniel Bullock, and Frank H. Guenther. "Neural Representations and Mechanisms for the Performance of Simple Speech Sequences." Journal of Cognitive Neuroscience 22, no. 7 (July 2010): 1504–29. http://dx.doi.org/10.1162/jocn.2009.21306.
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Speakers plan the phonological content of their utterances before their release as speech motor acts. Using a finite alphabet of learned phonemes and a relatively small number of syllable structures, speakers are able to rapidly plan and produce arbitrary syllable sequences that fall within the rules of their language. The class of computational models of sequence planning and performance termed competitive queuing models have followed K. S. Lashley [The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior (pp. 112–136). New York: Wiley, 1951] in assuming that inherently parallel neural representations underlie serial action, and this idea is increasingly supported by experimental evidence. In this article, we developed a neural model that extends the existing DIVA model of speech production in two complementary ways. The new model includes paired structure and content subsystems [cf. MacNeilage, P. F. The frame/content theory of evolution of speech production. Behavioral and Brain Sciences, 21, 499–511, 1998 ] that provide parallel representations of a forthcoming speech plan as well as mechanisms for interfacing these phonological planning representations with learned sensorimotor programs to enable stepping through multisyllabic speech plans. On the basis of previous reports, the model's components are hypothesized to be localized to specific cortical and subcortical structures, including the left inferior frontal sulcus, the medial premotor cortex, the basal ganglia, and the thalamus. The new model, called gradient order DIVA, thus fills a void in current speech research by providing formal mechanistic hypotheses about both phonological and phonetic processes that are grounded by neuroanatomy and physiology. This framework also generates predictions that can be tested in future neuroimaging and clinical case studies.
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Venkatraman, Vinod, Soon Chun Siong, Michael W. L. Chee, and Daniel Ansari. "Effect of Language Switching on Arithmetic: A Bilingual fMRI Study." Journal of Cognitive Neuroscience 18, no. 1 (January 1, 2006): 64–74. http://dx.doi.org/10.1162/089892906775250030.
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The role of language in performing numerical computations has been a topic of special interest in cognition. The “Triple Code Model” proposes the existence of a language-dependent verbal code involved in retrieving arithmetic facts related to addition and multiplication, and a language-independent analog magnitude code subserving tasks such as number comparison and estimation. Neuroimaging studies have shown dissociation between dependence of arithmetic computations involving exact and approximate processing on language-related circuits. However, a direct manipulation of language using different arithmetic tasks is necessary to assess the role of language in forming arithmetic representations and in solving problems in different languages. In the present study, 20 English-Chinese bilinguals were trained in two unfamiliar arithmetic tasks in one language and scanned using fMRI on the same problems in both languages (English and Chinese). For the exact “base-7 addition” task, language switching effects were found in the left inferior frontal gyrus (LIFG) and left inferior parietal lobule extending to the angular gyrus. In the approximate “percentage estimation” task, language switching effects were found predominantly in the bilateral posterior intraparietal sulcus and LIFG, slightly dorsal to the LIFG activation seen for the base-7 addition task. These results considerably strengthen the notion that exact processing relies on verbal and language-related networks, whereas approximate processing engages parietal circuits typically involved in magnitude-related processing.
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Izuma, Keise, Daisuke N. Saito, and Norihiro Sadato. "Processing of the Incentive for Social Approval in the Ventral Striatum during Charitable Donation." Journal of Cognitive Neuroscience 22, no. 4 (April 2010): 621–31. http://dx.doi.org/10.1162/jocn.2009.21228.
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Human behaviors are motivated not only by materialistic rewards but also by abstract social rewards, such as the approval of others. When choosing an action in social situations, to evaluate each action, the brain must convert different types of reward (such as money or social approval) into a common scale. Here using fMRI, we investigated the neural correlates of such valuation computations while individuals freely decided whether to donate to real charities or to take the money for themselves in the presence or absence of observers. Behavioral evidence showed that the mere presence of observers increased donation rates, and neuroimaging results revealed that activation in the ventral striatum before the same choice (“donate” or “not donate”) was significantly modulated by the presence of observers. Particularly high striatal activations were observed when a high social reward was expected (donation in public) and when there was the potential for monetary gain without social cost (no donation in the absence of observers). These findings highlight the importance of this area in representing both social and monetary rewards as a “decision utility” and add to the understanding of how the brain makes a choice using a “common neural currency” in social situations.
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Adams, Rick A., Michael Moutoussis, Matthew M. Nour, Tarik Dahoun, Declan Lewis, Benjamin Illingworth, Mattia Veronese, et al. "Variability in Action Selection Relates to Striatal Dopamine 2/3 Receptor Availability in Humans: A PET Neuroimaging Study Using Reinforcement Learning and Active Inference Models." Cerebral Cortex 30, no. 6 (February 21, 2020): 3573–89. http://dx.doi.org/10.1093/cercor/bhz327.
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Abstract Choosing actions that result in advantageous outcomes is a fundamental function of nervous systems. All computational decision-making models contain a mechanism that controls the variability of (or confidence in) action selection, but its neural implementation is unclear—especially in humans. We investigated this mechanism using two influential decision-making frameworks: active inference (AI) and reinforcement learning (RL). In AI, the precision (inverse variance) of beliefs about policies controls action selection variability—similar to decision ‘noise’ parameters in RL—and is thought to be encoded by striatal dopamine signaling. We tested this hypothesis by administering a ‘go/no-go’ task to 75 healthy participants, and measuring striatal dopamine 2/3 receptor (D2/3R) availability in a subset (n = 25) using [11C]-(+)-PHNO positron emission tomography. In behavioral model comparison, RL performed best across the whole group but AI performed best in participants performing above chance levels. Limbic striatal D2/3R availability had linear relationships with AI policy precision (P = 0.029) as well as with RL irreducible decision ‘noise’ (P = 0.020), and this relationship with D2/3R availability was confirmed with a ‘decision stochasticity’ factor that aggregated across both models (P = 0.0006). These findings are consistent with occupancy of inhibitory striatal D2/3Rs decreasing the variability of action selection in humans.
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Breveglieri, Rossella, Claudio Galletti, Annalisa Bosco, Michela Gamberini, and Patrizia Fattori. "Object Affordance Modulates Visual Responses in the Macaque Medial Posterior Parietal Cortex." Journal of Cognitive Neuroscience 27, no. 7 (July 2015): 1447–55. http://dx.doi.org/10.1162/jocn_a_00793.
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Area V6A is a visuomotor area of the dorsomedial visual stream that contains cells modulated by object observation and by grip formation. As different objects have different shapes but also evoke different grips, the response selectivity during object presentation could reflect either the coding of object geometry or object affordances. To clarify this point, we here investigate neural responses of V6A cells when monkeys observed two objects with similar visual features but different contextual information, such as the evoked grip type. We demonstrate that many V6A cells respond to the visual presentation of objects and about 30% of them by the object affordance. Given that area V6A is an early stage in the visuomotor processes underlying grasping, these data suggest that V6A may participate in the computation of object affordances. These results add some elements in the recent literature about the role of the dorsal visual stream areas in object representation and contribute in elucidating the neural correlates of the extraction of action-relevant information from general object properties, in agreement with recent neuroimaging studies on humans showing that vision of graspable objects activates action coding in the dorsomedial visual steam.
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Walia, Pushpinder, Kavya Narendra Kumar, and Anirban Dutta. "Neuroimaging Guided Transcranial Electrical Stimulation in Enhancing Surgical Skill Acquisition. Comment on Hung et al. The Efficacy of Transcranial Direct Current Stimulation in Enhancing Surgical Skill Acquisition: A Preliminary Meta-Analysis of Randomized Controlled Trials. Brain Sci. 2021, 11, 707." Brain Sciences 11, no. 8 (August 18, 2021): 1078. http://dx.doi.org/10.3390/brainsci11081078.
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Surgical skill acquisition may be facilitated with a safe application of transcranial direct current stimulation (tDCS). A preliminary meta-analysis of randomized control trials showed that tDCS was associated with significantly better improvement in surgical performance than the sham control; however, meta-analysis does not address the mechanistic understanding. It is known from skill learning studies that the hierarchy of cognitive control shows a rostrocaudal axis in the frontal lobe where a shift from posterior to anterior is postulated to mediate progressively abstract, higher-order control. Therefore, optimizing the transcranial electrical stimulation to target surgical task-related brain activation at different stages of motor learning may provide the causal link to the learning behavior. This comment paper presents the computational approach for neuroimaging guided tDCS based on open-source software pipelines and an open-data of functional near-infrared spectroscopy (fNIRS) for complex motor tasks. We performed an fNIRS-based cortical activation analysis using AtlasViewer software that was used as the target for tDCS of the motor complexity-related brain regions using ROAST software. For future studies on surgical skill training, it is postulated that the higher complexity laparoscopic suturing with intracorporeal knot tying task may result in more robust activation of the motor complexity-related brain areas when compared to the lower complexity laparoscopic tasks.
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Hummos, Ali, Bin A. Wang, Sabrina Drammis, Michael M. Halassa, and Burkhard Pleger. "Thalamic regulation of frontal interactions in human cognitive flexibility." PLOS Computational Biology 18, no. 9 (September 12, 2022): e1010500. http://dx.doi.org/10.1371/journal.pcbi.1010500.
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Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.
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Ng, V. W. K., E. T. Bullmore, G. I. de Zubicaray, A. Cooper, J. Suckling, and S. C. R. Williams. "Identifying Rate-Limiting Nodes in Large-Scale Cortical Networks for Visuospatial Processing: An Illustration using fMRI." Journal of Cognitive Neuroscience 13, no. 4 (May 1, 2001): 537–45. http://dx.doi.org/10.1162/08989290152001943.
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With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.
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Yeatman, Jason D., and Anthony M. Norcia. "Temporal Tuning of Word- and Face-selective Cortex." Journal of Cognitive Neuroscience 28, no. 11 (November 2016): 1820–27. http://dx.doi.org/10.1162/jocn_a_01002.
Full textAbstract:
Sensitivity to temporal change places fundamental limits on object processing in the visual system. An emerging consensus from the behavioral and neuroimaging literature suggests that temporal resolution differs substantially for stimuli of different complexity and for brain areas at different levels of the cortical hierarchy. Here, we used steady-state visually evoked potentials to directly measure three fundamental parameters that characterize the underlying neural response to text and face images: temporal resolution, peak temporal frequency, and response latency. We presented full-screen images of text or a human face, alternated with a scrambled image, at temporal frequencies between 1 and 12 Hz. These images elicited a robust response at the first harmonic that showed differential tuning, scalp topography, and delay for the text and face images. Face-selective responses were maximal at 4 Hz, but text-selective responses, by contrast, were maximal at 1 Hz. The topography of the text image response was strongly left-lateralized at higher stimulation rates, whereas the response to the face image was slightly right-lateralized but nearly bilateral at all frequencies. Both text and face images elicited steady-state activity at more than one apparent latency; we observed early (141–160 msec) and late (>250 msec) text- and face-selective responses. These differences in temporal tuning profiles are likely to reflect differences in the nature of the computations performed by word- and face-selective cortex. Despite the close proximity of word- and face-selective regions on the cortical surface, our measurements demonstrate substantial differences in the temporal dynamics of word- versus face-selective responses.
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Ramsey, Richard, Peter Hansen, Ian Apperly, and Dana Samson. "Seeing It My Way or Your Way: Frontoparietal Brain Areas Sustain Viewpoint-independent Perspective Selection Processes." Journal of Cognitive Neuroscience 25, no. 5 (May 2013): 670–84. http://dx.doi.org/10.1162/jocn_a_00345.
Full textAbstract:
A hallmark of human social interaction is the ability to consider other people's mental states, such as what they see, believe, or desire. Prior neuroimaging research has predominantly investigated the neural mechanisms involved in computing one's own or another person's perspective and largely ignored the question of perspective selection. That is, which brain regions are engaged in the process of selecting between self and other perspectives? To address this question, the current fMRI study used a behavioral paradigm that required participants to select between competing visual perspectives. We provide two main extensions to current knowledge. First, we demonstrate that brain regions within dorsolateral prefrontal and parietal cortices respond in a viewpoint-independent manner during the selection of task-relevant over task-irrelevant perspectives. More specifically, following the computation of two competing visual perspectives, common regions of frontoparietal cortex are engaged to select one's own viewpoint over another's as well as select another's viewpoint over one's own. Second, in the absence of conflict between the content of competing perspectives, we showed a reduced engagement of frontoparietal cortex when judging another's visual perspective relative to one's own. This latter finding provides the first brain-based evidence for the hypothesis that, in some situations, another person's perspective is automatically and effortlessly computed, and thus, less cognitive control is required to select it over one's own perspective. In doing so, we provide stronger evidence for the claim that we not only automatically compute what other people see but also, in some cases, we compute this even before we are explicitly aware of our own perspective.