Systematic investigation of changes in oxidized cerebral cytochrome c oxidase concentration during frontal lobe activation in healthy adults

Using transcranial near-infrared spectroscopy (NIRS) to measure changes in the redox state of cerebral cytochrome c oxidase (Δ[oxCCO]) during functional activation in healthy adults is hampered by instrumentation and algorithm issues. This study reports the Δ[oxCCO] response measured in such a setting and investigates possible confounders of this measurement. Continuous frontal lobe NIRS measurements were collected from 11 healthy volunteers during a 6-minute anagram-solving task, using a hybrid optical spectrometer (pHOS) that combines multi-distance frequency and broadband components. Only data sets showing a hemodynamic response consistent with functional activation were interrogated for a Δ[oxCCO] response. Simultaneous systemic monitoring data were also available. Possible influences on the Δ[oxCCO] response were systematically investigated and there was no effect of: 1) wavelength range chosen for fitting the measured attenuation spectra; 2) constant or measured, with the pHOS in real-time, differential pathlength factor; 3) systemic hemodynamic changes during functional activation; 4) changes in optical scattering during functional activation. The Δ[oxCCO] response measured in the presence of functional activation was heterogeneous, with the majority of subjects showing significant increase in oxidation, but others having a decrease. We conclude that the heterogeneity in the Δ[oxCCO] response is physiological and not induced by confounding factors in the measurements. © 2012 Optical Society of America

Involvement of right dorsolateral prefrontal cortex in ill-structured design cognition: An fMRI study

In ill-structured tasks, the problem to be solved is poorly specified and there is no unique correct solution. Most evidence on brain mechanisms involved in dealing with such tasks comes from neuropsychology. Here, we developed an ill-structured design task suitable for testing in a functional neuroimaging environment and compared it with a matched well-structured problem-solving task using fMRI. Consistent with prior neuropsychological results, the design task was associated with greater activity in right dorsolateral prefrontal cortex compared with problem solving. This differential activity was specific to the problem studying phase rather than performance. Furthermore, the design and problem-solving tasks differed not only in overall levels of brain activity but also in patterns of functional interactions between brain regions. These results provide new evidence on the role of right dorsolateral prefrontal cortex in ill-structured situations, such as those involved in design cognition. Additionally, these results confirm the suitability of functional neuroimaging for studying such situations

Neural changes when actions change: Adaptation of strong and weak expectations

Repeated experiences with an event create the expectation that subsequent events will expose an analog structure. These spontaneous expectations rely on an internal model of the event that results from learning. But what happens when events change? Do experience-based internal models get adapted instantaneously, or is model adaptation a function of the solidity of, i.e., familiarity with, the corresponding internal model? The present fMRI study investigated the effects of model solidity on model adaptation in an action observation paradigm. Subjects were made acquainted with a set of action movies that displayed an altered script when encountered again in the scanning session. We found model adaptation to result in an attenuation of the premotor-parietal network for action observation. Model solidity was found to modulate activation in the parahippocampal gyrus and the anterior cerebellar lobules, where increased solidity correlated with activity increase. Finally, the comparison between early and late stages of learning indicated an effect of model solidity on adaptation rate. This contrast revealed the involvement of a fronto-mesial network of Brodmann area 10 and the ACC in those states of learning that were signified by high model solidity, no matter if the memorized original or the altered action model was the more solid component. Findings suggest that the revision of an internal model is dependent on its familiarity. Unwarranted adaptations, but also perseverations may thus be prevented

Major Thought Restructuring: The Roles of Different Prefrontal Cortical Regions

An important question for understanding the neural basis of problem solving is whether the regions of human prefrontal cortices play qualitatively different roles in the major cognitive restructuring required to solve difficult problems. However, investigating this question using neuroimaging faces a major dilemma: either the problems do not require major cognitive restructuring, or if they do, the restructuring typically happens once, rendering repeated measurements of the critical mental process impossible. To circumvent these problems, young adult participants were challenged with a one-dimensional Subtraction (or Nim) problem [Bouton, C. L. Nim, a game with a complete mathematical theory. The Annals of Mathematics, 3, 35-39, 1901] that can be tackled using two possible strategies. One, often used initially, is effortful, slow, and error-prone, whereas the abstract solution, once achieved, is easier, quicker, and more accurate. Behaviorally, success was strongly correlated with sex. Using voxel-based morphometry analysis controlling for sex, we found that participants who found the more abstract strategy (i.e., Solvers) had more gray matter volume in the anterior medial, ventrolateral prefrontal, and parietal cortices compared with those who never switched from the initial effortful strategy (i.e., Explorers). Removing the sex covariate showed higher gray matter volume in Solvers (vs. Explorers) in the right ventrolateral prefrontal and left parietal cortex

The Imaginative Mind

The astounding capacity for the human imagination to be engaged across a wide range of contexts is limitless and fundamental to our day-to-day experiences. Although processes of imagination are central to human psychological function, they rarely occupy center stage in academic discourse or empirical study within psychological and neuroscientific realms. The aim of the paper is to tackle this imbalance by drawing together the multitudinous facets of imagination within a common framework. The processes fall into one of five categories depending on whether they are characterized as involving perceptual/motor related mental imagery, intentionality or recollective processing, novel combinatorial or generative processing, exceptional phenomenology in the aesthetic response, or altered psychological states which range from commonplace to dysfunctional. These proposed categories are defined on the basis of theoretical ideas from philosophy as well as empirical evidence from neuroscience. By synthesizing the findings across these domains of imagination, this novel five-part or quinquepartite classification of the human imagination aids in systematizing, and thereby abets, our understanding of the workings and brain basis of the human imagination. It would serve as a blueprint to direct further advances in the field of imagination while also promoting crosstalk with reference to stimulus-oriented facets of information processing. A biologically and ecologically valid psychology is one that seeks to explain fundamental aspects of human nature. Given the ubiquitous nature of the imaginative operations in our daily lives, there can be little doubt that these quintessential aspects of the mind should be central to the discussion

The Neural Substrate of the Eureka Effect

The Eureka effect, also known as Aha effect, insight or epiphany, refers to the common experience of suddenly solving a problem. Here we study this effect in a pattern recognition paradigm that requires the segmentation of complex scenes and recognition of objects on the basis of Gestalt rules and prior knowledge. In the experiments both sensory evidence and prior knowledge were manipulated in order to obtain trials that do or do not converge towards a perceptual solution. Subjects had to detect objects in blurred scenes and signal recognition with manual responses. Neural dynamics were analysed with high density Electroencephalography (EEG) recordings. We determined changes in spectral distribution, coherence, phase locking and fractal dimension. The Eureka effect was associated with increased coherent oscillations in the alpha and theta band over widely distributed regions of the cortical mantle predominantly in the right hemisphere. This increase in coherence was associated with a decrease of beta band activity over parietal and central regions, and with a decrease of alpha band activity over frontal and occipital areas. In addition, there was a lateralized reduction of fractal dimensionality for activity recorded from the right hemisphere. These results suggest that the transition towards the solution of a perceptual task is mainly associated with a change of network dynamics in the right hemisphere that is characterized by enhanced coherence and reduced complexity. We propose that the Eureka effect requires cooperation of cortical regions involved in working memory, creative thinking and the control of attention

Investigation of hemispheric asymmetry in reasoning with HD-tDCS and fMRI

Studies on multiple patient groups suggest that reasoning has a hemispheric asymmetry component. Previously, we proposed that neural networks in the left hemisphere are driven toward increasing and maintaining certainty, while right frontal networks prioritize congruence between beliefs and evidence. We tested the predictions of this framework with two high definition transcranial direct current stimulation (HD-tDCS) experiments and one functional magnetic resonance imaging (fMRI) experiment. In both HD-tDCS studies, we aimed to induce (or amplify) hemispheric asymmetry in healthy participants as they completed novel reasoning tasks. Each participant completed three tDCS sessions: a LH-bias session, in which the anode was placed over the left inferior frontal gyrus (IFG; BA45) and the cathode over the right IFG; a RH-bias session, in which the anode was placed over the right IFG and the cathode over the left IFG; and a sham session, which served as a control. In the first HD-tDCS experiment, participants (N=26) completed a probabilistic inference task that required the integration of evidence and one’s prior background knowledge. Consistent with predictions, we found that the intensity of RH-bias stimulation was associated with 1) collecting more evidence, 2) adopting a higher threshold for stopping evidence collection, and 3) making less certain guesses than an ideal Bayesian updater during the evidence presentation. Contrary to predictions, we found that greater LH-bias intensity was associated with more evidence collection, and LH-bias stimulation was associated with greater belief backtracks after encountering conflicting evidence than RH-bias or sham stimulation. The second HD-tDCS experiment followed a similar stimulation protocol but used reasoning problems that were more deeply embedded in real-world contexts in order to create more salient belief-evidence conflicts. During each stimulation session, 24 participants 1) judged whether a criminal suspect was guilty or not guilty based on crime scene evidence, 2) judged whether or not to pass a law based on arguments in favor and in opposition to it, and 3) judged whether a news headline was real or fake. We found that RH-bias stimulation reduced belief polarization after conflict, which was consistent with our predictions. Similarly, when evidence conflicted participants’ strong beliefs, they backtracked on their beliefs more under RH-bias stimulation compared to sham stimulation and, albeit to a lesser extent, compared to LH-bias stimulation. Under RH-bias stimulation, participants were less likely to judge real news headlines as being real, which resulted in poorer discrimination of real vs. fake headlines compared to sham and LH-bias stimulation. Finally, in the fMRI experiment, we examined lateralization in frontal anatomical regions for contrasts that we predicted to be more left-lateralized or more right-lateralized. Participants (N=36) completed a modified version of the state guessing task that was used in the first tDCS experiment. Consistent with predictions, contrasts involving uncertainty and belief advances were generally more left-lateralized and contrasts involving conflicting evidence and belief backtracks were more right-lateralized. We show that HD-tDCS can alter belief updating in healthy individuals in a way that is consistent with the patient literature, but additional experiments are necessary to disentangle the causal relationships between different reasoning biases and neural activity in left and right frontal neural networks

Neurobiology of incremental speech comprehension

Understanding spoken language requires the rapid transition from perceptual processing of the auditory input through a variety of cognitive processes involved in constructing the mental representation of the message that the speaker is intending to convey. Listeners carry out these complex processes very rapidly and accurately as they hear each word incrementally unfolding in a sentence. However, little is known about the specific spatiotemporal patterning of this wide range of incremental processing operations that underpin the dynamic transitions from the speech input to the development of a meaning interpretation of an utterance. This thesis aims to address this set of issues by investigating the spatiotemporal dynamics of brain activity as spoken sentences unfold over time in order to illuminate the neurocomputational properties of the human language processing system and determine how the representation of a spoken sentence develops incrementally as each upcoming word is heard. Using a novel application of multidimensional probabilistic modelling combined with models from computational linguistics, I developed models of a variety of computational processes associated with accessing and processing the syntactic and semantic properties of sentences and tested these models at various points as sentences unfolded over time. Since a wide range of incremental processes occur very rapidly during speech comprehension, it is crucial to keep track of the temporal dynamics of the neural computations involved. To do this, I used combined electroencephalography and magnetoencephalography (EMEG) to record neural activity with millisecond resolution and analyzed the recordings in source space using univariate and/or multivariate approaches. The results confirm the value of this combination of methods in examining the properties of incremental speech processing. My findings corroborate the predictive nature of human speech comprehension and demonstrate that the effects of early semantic constraint are not dependent on explicit syntactic knowledge

Queueing Network Modeling of Human Performance and Mental Workload in Perceptual-Motor Tasks.

Integrated with the mathematical modeling approaches, this thesis uses Queuing Network-Model Human Processors (QN-MHP) as a simulation platform to quantify human performance and mental workload in four representative perceptual-motor tasks with both theoretical and practical importance: discrete perceptual-motor tasks (transcription typing and psychological refractory period) and continuous perceptual-motor tasks (visual-manual tracking and vehicle steering with secondary tasks). The properties of queuing networks (queuing/waiting in processing information, serial and parallel information processing capability, overall mathematical structure, and entity-based network arrangement) allow QN-MHP to quantify several important aspects of the perceptual-motor tasks and unify them into one cognitive architecture. In modeling the discrete perceptual-motor task in a single task situation (transcription typing), QN-MHP quantifies and unifies 32 transcription typing phenomena involving many aspects of human performance--interkey time, typing units and spans, typing errors, concurrent task performance, eye movements, and skill effects, providing an alternative way to model this basic and common activities in human-machine interaction. In quantifying the discrete perceptual-motor task in a dual-task situation (psychological refractory period), the queuing network model is able to account for various experimental findings in PRP including all of these major counterexamples of existing models with less or equal number of free parameters and no need to use task-specific lock/unlock assumptions, demonstrating its unique advantages in modeling discrete dual-task performance. In modeling the human performance and mental workload in the continuous perceptual-motor tasks (visual-manual tracking and vehicle steering), QN-MHP is used as a simulation platform and a set of equations is developed to establish the quantitative relationships between queuing networks (e.g., subnetwork s utilization and arrival rate) and P300 amplitude measured by ERP techniques and subjective mental workload measured by NASA-TLX, predicting and visualizing mental workload in real-time. Moreover, this thesis also applies QN-MHP into the design of an adaptive workload management system in vehicles and integrates QN-MHP with scheduling methods to devise multimodal in-vehicle systems. Further development of the cognitive architecture in theory and practice is also discussed.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/55678/2/changxuw_1.pd