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Open Access Publications from the University of California

Scholarly Works (12 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2022)
Systems neuroscience approaches with a focus on large-scale brain organization and network analysis are advancing foundational knowledge of how cognitive control processes are implemented in the brain. Over the past decade, technological and computational innovations in the study of brain connectivity have led to advances in our understanding of how brain networks function, inspiring new conceptualizations of the role of prefrontal cortex (PFC) networks in the coordination of cognitive control. In this review, we describe six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control. Implementation of cognitive control in a constantly changing environment depends on the dynamic and flexible organization of PFC networks. In this context, we describe major empirical and theoretical models that have emerged in recent years and describe how their functional architecture and dynamic organization supports flexible cognitive control. We take an overarching view of advances made in the past few decades and consider fundamental issues regarding PFC network function, global brain dynamics, and cognition that still need to be resolved. We conclude by clarifying important future directions for research on cognitive control and their implications for advancing our understanding of PFC networks in brain disorders.
    Cover page: The role of PFC networks in cognitive control and executive function
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2022)
    The n-back task is a widely used behavioral task for measuring working memory and the ability to inhibit interfering information. We develop a novel model of the commonly used 2-back task using the cognitive psychometric framework provided by Multinomial Processing Trees. Our model involves three parameters: a memory parameter, corresponding to how well an individual encodes and updates sequence information about presented stimuli; a decision parameter corresponding to how well participants execute choices based on information stored in memory; and a base-rate parameter corresponding to bias for responding yes or no. We test the parameter recovery properties of the model using existing 2-back experimental designs, and demonstrate the application of the model to two previous data sets: one from social psychology involving faces corresponding to different races (Stelter and Degner, British Journal of Psychology 109:777-798, 2018), and one from cognitive neuroscience involving more than 1000 participants from the Human Connectome Project (Van Essen et al., Neuroimage 80:62-79, 2013). We demonstrate that the model can be used to infer interpretable individual-level parameters. We develop a hierarchical extension of the model to test differences between stimulus conditions, comparing faces of different races, and comparing face to non-face stimuli. We also develop a multivariate regression extension to examine the relationship between the model parameters and individual performance on standardized cognitive measures including the List Sorting and Flanker tasks. We conclude by discussing how our model can be used to dissociate underlying cognitive processes such as encoding failures, inhibition failures, and binding failures.
      Cover page: A Multinomial Processing Tree Model of the 2-back Working Memory Task.
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2022)
      The social worlds of young children primarily revolve around parents and caregivers, who play a key role in guiding childrens social and cognitive development. However, a hallmark of adolescence is a shift in orientation toward nonfamilial social targets, an adaptive process that prepares adolescents for their independence. Little is known regarding neurobiological signatures underlying changes in adolescents social orientation. Using functional brain imaging of human voice processing in children and adolescents (ages 7-16), we demonstrate distinct neural signatures for mothers voice and nonfamilial voices across child and adolescent development in reward and social valuation systems, instantiated in nucleus accumbens and ventromedial prefrontal cortex. While younger children showed greater activity in these brain systems for mothers voice compared with nonfamilial voices, older adolescents showed the opposite effect with increased activity for nonfamilial compared with mothers voice. Findings uncover a critical role for reward and social valuative brain systems in the pronounced changes in adolescents orientation toward nonfamilial social targets. Our approach provides a template for examining developmental shifts in social reward and motivation in individuals with pronounced social impairments, including adolescents with autism.SIGNIFICANCE STATEMENT Childrens social worlds undergo a transformation during adolescence. While socialization in young children revolves around parents and caregivers, adolescence is characterized by a shift in social orientation toward nonfamilial social partners. Here we show that this shift is reflected in neural activity measured from reward processing regions in response to brief vocal samples. When younger children hear their mothers voice, reward processing regions show greater activity compared with when they hear nonfamilial, unfamiliar voices. Strikingly, older adolescents show the opposite effect, with increased activity for nonfamilial compared with mothers voice. Findings identify the brain basis of adolescents switch in social orientation toward nonfamilial social partners and provides a template for understanding neurodevelopment in clinical populations with social and communication difficulties.
        Cover page: A Neurodevelopmental Shift in Reward Circuitry from Mothers to Nonfamilial Voices in Adolescence.
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2009)

        The common factor that underlies several types of functional brain imaging is the electric current of masses of dendrites. The prodigious demands for the energy that is required to drive the dendritic currents are met by hemodynamic and metabolic responses that are visualized with fMRI and PET techniques. The high current densities in parallel dendritic shafts and the broad distributions of the loop currents outside the dendrites generate both the scalp EEG and the magnetic fields seen in the MEG. The measurements of image intensities and potential fields provide state variables for modeling. The relationships between the intensities of current density and the electric, magnetic, and hemodynamic state variables are complex and far from proportionate. The state variables are complementary, because the information they convey comes from differing albeit overlapping neural populations, so that efforts to cross-validate localization of neural activity relating to specified cognitive behaviors have not always been successful. We propose an alternative way to use the three methods in combination through studies of hemisphere-wide, high-resolution spatiotemporal patterns of neural activity recorded non-invasively and analyzed with multivariate statistics. Success in this proposed endeavor requires specification of what patterns to look for. At the present level of understanding, an appropriate pattern is any significant departure from random noise in the spectral, temporal and spatial domains that can be scaled into the coarse-graining of time by fMRI/BOLD and the coarse-graining of space by EEG and MEG. Here the requisite patterns are predicted to be large-scale spatial amplitude modulation (AM) of synchronized neuronal signals in the beta and gamma ranges that are coordinated but not correlated with fMRI intensities.

          Cover page: Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2019)
          UNLABELLED: Engaging with vocal sounds is critical for childrens social-emotional learning, and children with autism spectrum disorder (ASD) often tune out voices in their environment. Little is known regarding the neurobiological basis of voice processing and its link to social impairments in ASD. Here, we perform the first comprehensive brain network analysis of voice processing in children with ASD. We examined neural responses elicited by unfamiliar voices and mothers voice, a biologically salient voice for social learning, and identified a striking relationship between social communication abilities in children with ASD and activation in key structures of reward and salience processing regions. Functional connectivity between voice-selective and reward regions during voice processing predicted social communication in children with ASD and distinguished them from typically developing children. Results support the Social Motivation Theory of ASD by showing reward system deficits associated with the processing of a critical social stimulus, mothers voice, in children with ASD. EDITORIAL NOTE: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editors assessment is that minor issues remain unresolved (see decision letter).
            Cover page: Impaired voice processing in reward and salience circuits predicts social communication in children with autism.
            • Article
            UC Irvine Previously Published Works (2023)

            The circular drift-diffusion model (CDDM) is a sequential sampling model designed to account for decisions and response times in decision-making tasks with a circular set of choice alternatives. We present and demonstrate a fully Bayesian implementation and extension of the CDDM. This development allows researchers to apply the CDDM to data from complex experiments and draw conclusions about targeted hypotheses. The Bayesian implementation relies on a custom JAGS module. We describe the module and demonstrate its adequacy through a simulation study. We then illustrate the advantages of the implementation by revisiting data from a continuous orientation judgment task. We develop a graphical model for the analysis that is based on the CDDM, but extends it with hierarchical and latent-mixture structures.We then demonstrate how these extensions are used to accommodate the design of the experiment and to implement psychological assumptions about individual differences, the difficulty of different stimulus conditions, and the impact of cues on decision making. Finally, we demonstrate how the computational Bayesian inference enabled by JAGS allows these assumptions to be tested and addresses psychological research questions about people's decision making.

              Creative Commons 'BY-SA' version 4.0 license
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2023)
              Cognitive control deficits are a hallmark of attention deficit hyperactivity disorder (ADHD) in children. Theoretical models posit that cognitive control involves reactive and proactive control processes but their distinct roles and inter-relations in ADHD are not known, and the contributions of proactive control remain vastly understudied. Here, we investigate the dynamic dual cognitive control mechanisms associated with both proactive and reactive control in 50 children with ADHD (16F/34M) and 30 typically developing (TD) children (14F/16M) aged 9-12 years across two different cognitive controls tasks using a within-subject design. We found that while TD children were capable of proactively adapting their response strategies, children with ADHD demonstrated significant deficits in implementing proactive control strategies associated with error monitoring and trial history. Children with ADHD also showed weaker reactive control than TD children, and this finding was replicated across tasks. Furthermore, while proactive and reactive control functions were correlated in TD children, such coordination between the cognitive control mechanisms was not present in children with ADHD. Finally, both reactive and proactive control functions were associated with behavioral problems in ADHD, and multi-dimensional features derived from the dynamic dual cognitive control framework predicted inattention and hyperactivity/impulsivity clinical symptoms. Our findings demonstrate that ADHD in children is characterized by deficits in both proactive and reactive control, and suggest that multi-componential cognitive control measures can serve as robust predictors of clinical symptoms.
                Cover page: Both reactive and proactive control are deficient in children with ADHD and predictive of clinical symptoms.
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2021)
                Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.
                  Cover page: Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention
                  • Article
                  • Peer Reviewed
                  UC San Francisco Previously Published Works (2013)
                  The primary goal of this review is to highlight current research and theories describing the neurobiological basis of math (MD), reading (RD), and comorbid math and reading disability (MD+RD). We first describe the unique brain and cognitive processes involved in acquisition of math and reading skills, emphasizing similarities and differences in each domain. Next we review functional imaging studies of MD and RD in children, integrating relevant theories from experimental psychology and cognitive neuroscience to characterize the functional neuroanatomy of cognitive dysfunction in MD and RD. We then review recent research on the anatomical correlates of MD and RD. Converging evidence from morphometry and tractography studies are presented to highlight distinct patterns of white matter pathways which are disrupted in MD and RD. Finally, we examine how the intersection of MD and RD provides a unique opportunity to clarify the unique and shared brain systems which adversely impact learning and skill acquisition in MD and RD, and point out important areas for future work on comorbid learning disabilities.
                    Cover page: Neurobiological Underpinnings of Math and Reading Learning Disabilities
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2012)

                    Context

                    Focused hypnotic concentration is a model for brain control over sensation and behavior. Pain and anxiety can be effectively alleviated by hypnotic suggestion, which modulates activity in brain regions associated with focused attention, but the specific neural network underlying this phenomenon is not known.

                    Objective

                    To investigate the brain basis of hypnotizability.

                    Design

                    Cross-sectional, in vivo neuroimaging study performed from November 2005 through July 2006.

                    Setting

                    Academic medical center at Stanford University School of Medicine.

                    Patients

                    Twelve adults with high and 12 adults with low hypnotizability.

                    Main outcome measures

                    Functional magnetic resonance imaging to measure functional connectivity networks at rest, including default-mode, salience, and executive-control networks; structural T1 magnetic resonance imaging to measure regional gray and white matter volumes; and diffusion tensor imaging to measure white matter microstructural integrity.

                    Results

                    High compared with low hypnotizable individuals had greater functional connectivity between the left dorsolateral prefrontal cortex, an executive-control region of the brain, and the salience network composed of the dorsal anterior cingulate cortex, anterior insula, amygdala, and ventral striatum, involved in detecting, integrating, and filtering relevant somatic, autonomic, and emotional information using independent component analysis. Seed-based analysis confirmed elevated functional coupling between the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex in high compared with low hypnotizable individuals. These functional differences were not due to any variation in brain structure in these regions, including regional gray and white matter volumes and white matter microstructure.

                    Conclusions

                    Our results provide novel evidence that altered functional connectivity in the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex may underlie hypnotizability. Future studies focusing on how these functional networks change and interact during hypnosis are warranted.
                      Cover page: Functional Brain Basis of Hypnotizability
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