Executive function in first-episode schizophrenia

BACKGROUND: We tested the hypothesis that schizophrenia is primarily a frontostriatal disorder by examining executive function in first-episode patients. Previous studies have shown either equal decrements in many cognitive domains or specific deficits in memory. Such studies have grouped test results or have used few executive measures, thus, possibly losing information. We, therefore, measured a range of executive ability with tests known to be sensitive to frontal lobe function. METHODS: Thirty first-episode schizophrenic patients and 30 normal volunteers, matched for age and NART IQ, were tested on computerized test of planning, spatial working memory and attentional set shifting from the Cambridge Automated Neuropsychological Test Battery. Computerized and traditional tests of memory were also administered for comparison. RESULTS: Patients were worse on all tests but the profile was non-uniform. A componential analysis indicated that the patients were characterized by a poor ability to think ahead and organize responses but an intact ability to switch attention and inhibit prepotent responses. Patients also demonstrated poor memory, especially for free recall of a story and associate learning of unrelated word pairs. CONCLUSIONS: In contradistinction to previous studies, schizophrenic patients do have profound executive impairments at the beginning of the illness. However, these concern planning and strategy use rather than attentional set shifting, which is generally unimpaired. Previous findings in more chronic patients, of severe attentional set shifting impairment, suggest that executive cognitive deficits are progressive during the course of schizophrenia. The finding of severe mnemonic impairment at first episode suggests that cognitive deficits are not restricted to one cognitive domain

Brain Networks for Integrative Rhythm Formation

Performance of externally paced rhythmic movements requires brain and behavioral integration of sensory stimuli with motor commands. The underlying brain mechanisms to elaborate beat-synchronized rhythm and polyrhythms that musicians readily perform may differ. Given known roles in perceiving time and repetitive movements, we hypothesized that basal ganglia and cerebellar structures would have greater activation for polyrhythms than for on-the-beat rhythms.Using functional MRI methods, we investigated brain networks for performing rhythmic movements paced by auditory cues. Musically trained participants performed rhythmic movements at 2 and 3 Hz either at a 1:1 on-the-beat or with a 3:2 or a 2:3 stimulus-movement structure. Due to their prior musical experience, participants performed the 3:2 or 2:3 rhythmic movements automatically. Both the isorhythmic 1:1 and the polyrhythmic 3:2 or 2:3 movements yielded the expected activation in contralateral primary motor cortex and related motor areas and ipsilateral cerebellum. Direct comparison of functional MRI signals obtained during 3:2 or 2:3 and on-the-beat rhythms indicated activation differences bilaterally in the supplementary motor area, ipsilaterally in the supramarginal gyrus and caudate-putamen and contralaterally in the cerebellum.The activated brain areas suggest the existence of an interconnected brain network specific for complex sensory-motor rhythmic integration that might have specificity for elaboration of musical abilities

Reinforcement Learning Describes the Computational and Neural Processes Underlying Flexible Learning of Values and Attentional Selection

Attention and learning are cognitive control processes that are closely related. This thesis investigates this inter-relatedness by using computational models to describe the mechanisms that are shared between these processes. Computational models describe the transformation of stimuli to observable variables (behaviour) and contain the latent mechanisms that affect this transformation. Here, I captured these mechanisms with the reinforcement learning (RL) framework applied in two different task contexts and three different projects to show 1) how attentional selection of stimuli involves the learning of values for stimuli, 2) how the learning of stimulus values is influenced by previously learned rules, and 3) how explorations of value-related mechanisms in the brain benefit from using intracranial EEG to investigate the strength of oscillatory activity in ventromedial prefrontal cortex. In the first project, the RL framework is applied to a feature-based attention task that required macaques to learn the value of stimulus features while ignoring non-relevant information. By comparing different RL schemes I found that trial-by-trial covert attentional selections were best predicted by a model that only represents expected values for the task relevant feature dimension. In the second project, I explore mechanisms of stimulus-feature value learning in humans in order to understand the influence of learned rules for the flexible, on-going learning of expected values. I test the hypothesis that naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type. I found that two-thirds of subjects (n=22/32) exhibited behaviour that was best fit by a ‘flexible-rule-selection’ model. Low-frequency oscillatory activity in frontal cortex has been associated with cognitive control and integrative brain functions, however, the relationship between expected values for stimuli and band-limited, rhythmic neural activity in the human brain is largely unknown. In the third project, I used intracranial electrocorticography (ECoG) in a proof-of-principle study to reveal spectral power signatures in vmPFC related to the expected values of stimuli predicted by a RL model for a single human subject

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Neural Dynamics of Learning and Performance of Fixed Sequences: Latency Pattern Reorganizations and the N-STREAMS Model

Fixed sequences performed from memory play a key role in human cultural behavior, especially in music and in rapid communication through speaking, handwriting, and typing. Upon first performance, fixed sequences are often produced slowly, but extensive practice leads to performance that is both fluid and as rapid as allowed by constraints inherent in the task or the performer. The experimental study of fixed sequence learning and production has generated a large database with some challenging findings, including practice-related reorganizations of temporal properties of performance. In this paper, we analyze this literature and identify a coherent set of robust experimental effects. Among these are both the sequence length effect on latency, a dependence of reaction time on sequence length, and practice-dependent lost of the lengths effect on latency. We then introduce a neural network architecture capable of explaining these effects. Called the NSTREAMS model, this multi-module architecture embodies the hypothesis that the brain uses several substrates for serial order representation and learning. The theory describes three such substrates and how learning autonomously modifies their interaction over the course of practice. A key feature of the architecture is the co-operation of a 'competitive queuing' performance mechanism with both fundamentally parallel ('priority-tagged') and fundamentally sequential ('chain-like') representations of serial order. A neurobiological interpretation of the architecture suggests how different parts of the brain divide the labor for serial learning and performance. Rhodes (1999) presents a complete mathematical model as implementation of the architecture, and reports successful simulations of the major experimental effects. It also highlights how the network mechanisms incorporated in the architecture compare and contrast with earlier substrates proposed for competitive queuing, priority tagging and response chaining.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-1-0409); National Institute of Health (RO1 DC02852

The neurophysiology of intersensory selective attention and task switching

Our ability to selectively attend to certain aspects of the world and ignore others is fundamental to our day-to-day lives. The need for selective attention stems from capacity limitations inherent in our perceptual and cognitive processing architecture. Because not every elemental piece of our environment can be fully processed in parallel, the nervous system must prioritize processing. This prioritization is generally referred to as selective attention. Meanwhile, we are faced with a world that is constantly in flux, such that we have to frequently shift our attention from one piece of the environment to another and from one task to another. This process is generally referred to as task-switching. Neural oscillations in the alpha band (~8-14 Hz) have been shown to index the distribution of selective attention, and there is increasing evidence that oscillations in this band are in fact utilized by the nervous system to suppress distracting, task-irrelevant information. In order to elaborate on what is known of the function of alpha oscillations as well as current models of both intersensory selective attention and task switching, I investigated the dynamics of alpha amplitude modulations within the context of intersenory selective attention and task switching in neurologically typical young adults. Participants were alternately cued to attend to either the visual or auditory aspect of a compound audio-visual stimulus while high-density electroencephalography was recorded. It is typically found that alpha power increases over parieto-occipital cortices when attention is directed away from the visual modality and to the auditory modality. I report evidence that alpha oscillations play a role in task-switching (e.g., when switching from attending the visual task versus repeating this task), specifically as biasing signals, that may operate to re-weight competition among two tasks-sets. I further investigated the development of these same processes in school-aged children and adolescents. While exhibiting typical patterns of alpha modulations relevant to selective attention, Young school-aged children (8-12 years), compared to older participants, did not demonstrate specific task switching modulation of alpha oscillations, suggesting that this process does not fully develop until late adolescence. Finally, children and adolescents on the autism spectrum failed altogether to exhibit differentiation of alpha power between attend-visual and attend-auditory conditions--an effect present in age and IQ matched controls--suggesting that ASD individuals may have a deficit in the overall top-down deployment of alpha oscillatory biasing signals. This could result in an inability to ignore distracting information in the environment, leading to an overwhelming, disordered experience of the world, resulting in profound effects on the both social interaction and cognitive development. Altogether, these findings add to growing evidence that alpha oscillations serve as domain general biasing signals and are integral to our flexible goal-oriented behavior. Furthermore, the flexible use of these biasing signals in selective attention and task switching develops over a protracted period, and appears to be aberrant in autism spectrum disorder

From rest to task

A primary goal of neuroscience research on psychiatric disorders such as schizophrenia is to enhance the current understanding of underlying biological mechanisms in order to develop novel interventions. Human brain functions are maintained through activity of large-scale brain networks. Accordingly, deficient perceptual and cognitive processing can be caused by failures of functional integration within networks, as reflected by the disconnection hypothesis of schizophrenia. Various neuroimaging techniques can be applied to study functional brain networks, each having different strengths. Frequently used complementary methods are the electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which were shown to have a common basis. Given the feasibility of combined EEG and fMRI measurement, EEG signatures of functional networks have been described, providing complimentary information about the functional state of networks. Both at rest and during task completion, many independent EEG and fMRI studies confirmed deficient network connectivity in schizophrenia. However, a rather diffuse picture with hyper- and hypo- activations within and between specific networks was reported. Furthermore, the theory of state dependent information processing argues that spontaneous and prestimulus brain activity interacts with upcoming task-related processes. Consequently, observed network deficits that vary according to task conditions could be caused by differences in resting or prestimulus state in schizophrenia. Based on that background, the present thesis aimed to increase the understanding of aberrant functional networks in schizophrenia by using simultaneous EEG-fMRI under different conditions. One study investigated integrative mechanisms of networks during eyes-open (EO) resting state using a common-phase synchronization measure in an EEG-informed fMRI analysis (study 3). The other two studies (studies 1&2) used an fMRI-informed EEG analysis: The second study was an extension of the first, which was performed in healthy subjects only. Hence, the same methodologies and analyses were applied in both studies, but in the second study schizophrenia patients were compared to healthy controls. The associations between four temporally coherent networks (TCNs) – the default mode network (DMN), the dorsal attention network (dAN), left and right working memory networks (WMNs) – and power of three EEG frequency bands (theta, alpha, and beta band) during a verbal working memory (WM) task were investigated. Both resting state and task-related studies performed in schizophrenia patients (studies 2&3) revealed altered activation strength, functional states and interaction of TCNs, especially of the DMN. During rest (study 3), the DMN was differently integrated through common-phase synchronization in the delta (0.5 – 3.5Hz) and beta (13 – 30Hz) band. At prestimulus states of a verbal WM task, however, study 2 did not reveal differences in the activation level of the DMN between groups. Furthermore, from pre-to-post stimulus, the association of the DMN with frontal-midline (FM) theta (3 – 7Hz) band was altered, and a reduced suppression of the DMN during WM retention was detected. Schizophrenia patients also demonstrated abnormal interactions between networks: the DMN and dAN showed a reduced anti-correlation and the WMNs demonstrated an absent lateralization effect (study 2). The view that schizophrenia patients display TCN deficiencies is supported by the results of the present thesis. Especially the DMN and its interaction to the task-positive dAN showed specific alterations at different mental states and their interaction (during rest and from pre-to-post stimulus). Those alterations might at least partly explain observed symptomatology as attentional orientation deficits in patients. To conclude, functional networks as the DMN might represent promising targets for novel treatment options such as neurofeedback or transcranial direct current stimulation (tDCS)