A neurocomputational account of cognitive deficits in Parkinson's disease

Internal manipulation of perceptual representations in human flexible cognition: A computational model

Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.

Nutraceutical Properties of Mulberries Grown in Southern Italy (Apulia)

In this work, for the first time, were analyzed mulberry genotypes grown in Apulia (Southern Italy, Salento region) were analyzed. Two local varieties of Morus alba (cv. Legittimo nero and cv. Nello) and one of Morus nigra were characterized for content in simple sugars, organic acids, phenols, anthocyanins; fruit antioxidant activity (AA) was also evaluated by three different methods (2,2-Diphenyl-1-picrylhydrazyl, DPPH; 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), ABTS; and Ferric reducing antioxidant potential, FRAP test). The results showed that the sugars amount ranged between 6.29 and 7.66 g/100 g fresh weight (FW) while the malic and citric acids content was low, at about 0.1–1 g/100 g FW. Mulberries are a good source of phenols which are present in higher values in M. nigra and M. alba cv. Legittimo nero (485 and 424 mg Gallic Acid Equivalent (GAE)/ 100 g FW, respectively). The high performance liquid chromatography/diode array detector/mass spectrometry (HPLC/DAD/MS) analysis identified 5 main anthocyanin compounds present in different concentrations in each variety of mulberry: cyanidin 3-sophoroside, cyanidin 3-glucoside, cyanidin 3-rutinoside, pelargonidin 3-glucoside, pelargonidin 3-rutinoside. The highest concentration of anthocyanins was determined in Morus alba Legittimo (about 300 mg/100 g FW) while the lowest content (about 25 mg/100 g FW) was measured in M. alba cv. Nello. Morus nigra showed a good AA in comparison with the different M. alba genotypes with all the used methods; its AA was equal to 33, 26 and 21 μmols Trolox/g FW when using DPPH, ABTS and FRAP tests, respectively. All genotypes showed an anti-inflammatory activity (measured by cyclooxygenase (COX) inhibitory assay) which was also compared with two commercial anti-inflammatory drugs. The data obtained support the high biological qualities of mulberry fruits and their diffusion in human nutrition.

A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.

Acetaminophen enhances the reflective learning process

Acetaminophen has been shown to influence cognitive and affective behavior possibly via alterations in serotonin function. This study builds upon this previous work by examining the relationship between acetaminophen and dual-learning systems, comprising reflective (rule-based) and reflexive (information-integration) processing. In a double-blind, placebo-controlled study, a sample of community-recruited adults (N = 87) were randomly administered acetaminophen (1000 mg) or placebo and then completed reflective-optimal and reflexive-optimal category learning tasks. For the reflective-optimal category learning task, acetaminophen compared to placebo was associated with enhanced accuracy prior to the first rule switch (but not overall accuracy), with needing fewer trials to reach criterion and with a faster learning rate. Acetaminophen modestly attenuated performance on the reflexive-optimal category learning task compared to placebo. These findings indirectly support two positions that have been proposed elsewhere. First, they are consistent with the view that acetaminophen has an influence on the serotonergic system. Second, the findings are consistent with a proposed link between elevated serotonin function and relative dominance of effortful, rule-based processing.

Categorization system-switching deficits in typical aging and Parkinson's disease

OBJECTIVE

Numerous studies documenting cognitive deficits in Parkinson's disease (PD) revealed impairment in a variety of tasks related to memory, learning, and attention. One ubiquitous task that has not received much attention, is categorization system-switching. Categorization system-switching is a form of task-switching requiring participants to switch between different categorization systems. In this article, we explore whether older adults and people with PD show deficits in categorization system-switching.

METHOD

Twenty older adults diagnosed with PD, 20 neurologically intact older adults, and 67 young adults participated in this study. Participants were first trained in rule-based (RB) and later information-integration (II) categorization separately. After training on the tasks, participants performed a block of trial-by-trial switching where the RB and II trials were randomly intermixed. Finally, the last block of trials also intermixed RB and II trials were randomly but additionally changed the location of the response buttons.

RESULTS

Contrary to our hypothesis, the results show no difference in accuracy between older adults and people with PD during the intermixed trial block, as well as no difference in response time (RT) switch cost. However, both groups were less accurate during intermixed trial blocks and had a higher RT switch cost when compared with young adults. In addition, the proportion of participants able to switch systems was smaller in people with PD than in young adults.

CONCLUSIONS

The results suggest that older adults and people with PD have impaired categorization system-switching ability, and that this ability may be related to a decrease in tonic dopamine (DA) levels associated with normal aging and PD. (PsycINFO Database Record

Quantitative modeling of category learning deficits in various patient populations

OBJECTIVE

To provide a select review of our applications of quantitative modeling to highlight the utility of such approaches to better understand the neuropsychological deficits associated with various neurologic and psychiatric diseases.

METHOD

We review our work examining category learning in various patient populations, including individuals with basal ganglia disorders (Huntington's Disease and Parkinson's disease), amnesia and Eating Disorders.

RESULTS

Our review suggests that the use of quantitative models has enabled a better understanding of the learning deficits often observed in these conditions and has allowed us to form novel hypotheses about the neurobiological bases of their deficits.

CONCLUSIONS

We feel that the use of neurobiologically inspired quantitative modeling holds great promise in neuropsychological assessment and that future clinical measures should incorporate the use of such models as part of their standard scoring. Appropriate studies need to be completed, however, to determine whether such modeling techniques adhere to the rigorous psychometric properties necessary for a valid and reliable application in a clinical setting. (PsycINFO Database Record

Practice and Preparation Time Facilitate System-Switching in Perceptual Categorization

Mounting evidence suggests that category learning is achieved using different psychological and biological systems. While existing multiple-system theories and models of categorization may disagree about the number or nature of the different systems, all assume that people can switch between systems seamlessly. However, little empirical data has been collected to test this assumption, and recent available data suggest that system-switching is difficult. The main goal of this article is to identify factors influencing the proportion of participants who successfully learn to switch between procedural and declarative systems on a trial-by-trial basis. Specifically, we tested the effects of preparation time and practice, two factors that have been useful in task-switching, in a system-switching experiment. The results suggest that practice and preparation time can be beneficial to system-switching (as calculated by a higher proportion of switchers and lower switch costs), especially when they are jointly present. However, this improved system-switching comes at the cost of a larger button-switch interference when changing the location of the response buttons. The article concludes with a discussion of the implications of these findings for empirical research on system-switching and theoretical work on multiple-systems of category learning.

A neural interpretation of exemplar theory

Exemplar theory assumes that people categorize a novel object by comparing its similarity to the memory representations of all previous exemplars from each relevant category. Exemplar theory has been the most prominent cognitive theory of categorization for more than 30 years. Despite its considerable success in providing good quantitative fits to a wide variety of accuracy data, it has never had a detailed neurobiological interpretation. This article proposes a neural interpretation of exemplar theory in which category learning is mediated by synaptic plasticity at cortical-striatal synapses. In this model, categorization training does not create new memory representations, rather it alters connectivity between striatal neurons and neurons in sensory association cortex. The new model makes identical quantitative predictions as exemplar theory, yet it can account for many empirical phenomena that are either incompatible with or outside the scope of the cognitive version of exemplar theory. (PsycINFO Database Record

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning.

Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach

Procedural learning of skills depends on dopamine-mediated striatal plasticity. Most prior work investigated single stimulus-response procedural learning followed by feedback. However, many skills include several actions that must be performed before feedback is available. A new procedural-learning task is developed in which three independent and successive unsupervised categorization responses receive aggregate feedback indicating either that all three responses were correct, or at least one response was incorrect. Experiment 1 showed superior learning of stimuli in position 3, and that learning in the first two positions was initially compromised, and then recovered. An extensive theoretical analysis that used parameter space partitioning found that a large class of procedural-learning models, which predict propagation of dopamine release from feedback to stimuli, and/or an eligibility trace, fail to fully account for these data. The analysis also suggested that any dopamine released to the second or third stimulus impaired categorization learning in the first and second positions. A second experiment tested and confirmed a novel prediction of this large class of procedural-learning models that if the to-be-learned actions are introduced one-by-one in succession then learning is much better if training begins with the first action (and works forwards) than if it begins with the last action (and works backwards).

Simulating the Effect of Reinforcement Learning on Neuronal Synchrony and Periodicity in the Striatum

The study of rhythms and oscillations in the brain is gaining attention. While it is unclear exactly what the role of oscillation, synchrony, and rhythm is, it appears increasingly likely that synchrony is related to normal and abnormal brain states and possibly cognition. In this article, we explore the relationship between basal ganglia (BG) synchrony and reinforcement learning. We simulate a biologically-realistic model of the striatum initially proposed by Ponzi and Wickens (2010) and enhance the model by adding plastic cortico-BG synapses that can be modified using reinforcement learning. The effect of reinforcement learning on striatal rhythmic activity is then explored, and disrupted using simulated deep brain stimulation (DBS). The stimulator injects current in the brain structure to which it is attached, which affects neuronal synchrony. The results show that training the model without DBS yields a high accuracy in the learning task and reduced the number of active neurons in the striatum, along with an increased firing periodicity and a decreased firing synchrony between neurons in the same assembly. In addition, a spectral decomposition shows a stronger signal for correct trials than incorrect trials in high frequency bands. If the DBS is ON during the training phase, but not the test phase, the amount of learning in the model is reduced, along with firing periodicity. Similar to when the DBS is OFF, spectral decomposition shows a stronger signal for correct trials than for incorrect trials in high frequency domains, but this phenoemenon happens in higher frequency bands than when the DBS is OFF. Synchrony between the neurons is not affected. Finally, the results show that turning the DBS ON at test increases both firing periodicity and striatal synchrony, and spectral decomposition of the signal show that neural activity synchronizes with the DBS fundamental frequency (and its harmonics). Turning the DBS ON during the test phase results in chance performance regardless of whether the DBS was ON or OFF during training. We conclude that reinforcement learning is related to firing periodicity, and a stronger signal for correct trials when compared to incorrect trials in high frequency bands.

Effect of black mulberry (Morus nigra) extract treatment on cognitive impairment and oxidative stress status of D-galactose-induced aging mice

CONTEXT

Morus nigra L. (Moraceae) has various uses in traditional medicine. However, the effect of M. nigra on cognitive impairment has not been investigated yet.

OBJECTIVE

The objective of this study is to determine the phenolic acid content and DNA damage protection potential of M. nigra leaf extract and to investigate the extract effect on cognitive impairment and oxidative stress in aging mice.

MATERIALS AND METHODS

Phenolic acid content was determined by quantitative chromatographic analysis. DNA damage protection potential was evaluated on pBR322 plasmid DNA. Thirty-two Balb-C mice were randomly divided into four groups (control, d-galactose, d-galactose + M. nigra 50, and d-galactose + M. nigra 100). Mice were administered d-galactose (100 mg/kg, subcutaneous) and M. nigra (50 or 100 mg/kg, orally) daily for 8 weeks. Behavioral responses were evaluated with Morris water maze. Activities of antioxidant enzymes and levels of malondialdehyde (MDA) were assayed in serum, brain, and liver.

RESULTS

In extract, vanillic (632.093 μg/g) and chlorogenic acids (555.0 μg/g) were determined. The extract between 0.02 and 0.05 mg/mL effectively protected all DNA bands against the hazardous effect of UV and H2O2. Morus nigra significantly improved learning dysfunctions (p < 0.01), increased memory retention (p < 0.01), reduced MDA levels (p < 0.05), and elevated SOD, GPx, and CAT activities (p < 0.05) compared with the d-galactose group.

DISCUSSION AND CONCLUSION

These results show that M. nigra has the potential in improving cognitive deficits in mice and that M. nigra may be useful to suppress aging, partially due to its scavenging activity of free radicals and high antioxidant capacity.

Computational modeling of neurostimulation in brain diseases

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.

A neurocomputational model of automatic sequence production

Most behaviors unfold in time and include a sequence of submovements or cognitive activities. In addition, most behaviors are automatic and repeated daily throughout life. Yet, relatively little is known about the neurobiology of automatic sequence production. Past research suggests a gradual transfer from the associative striatum to the sensorimotor striatum, but a number of more recent studies challenge this role of the BG in automatic sequence production. In this article, we propose a new neurocomputational model of automatic sequence production in which the main role of the BG is to train cortical-cortical connections within the premotor areas that are responsible for automatic sequence production. The new model is used to simulate four different data sets from human and nonhuman animals, including (1) behavioral data (e.g., RTs), (2) electrophysiology data (e.g., single-neuron recordings), (3) macrostructure data (e.g., TMS), and (4) neurological circuit data (e.g., inactivation studies). We conclude with a comparison of the new model with existing models of automatic sequence production and discuss a possible new role for the BG in automaticity and its implication for Parkinson's disease.

A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing

The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning—results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500 ms compared to delays of 0 and 1000 ms, and highly impaired with delays of 2.5 s or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 s. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa

OBJECTIVE

To examine set shifting in a group of women previously diagnosed with anorexia nervosa who are now weight-restored (AN-WR) and then apply a biologically based computational model (Competition between Verbal and Implicit Systems [COVIS]) to simulate the pattern of category learning and set shifting performances observed.

METHOD

Nineteen AN-WR women and 35 control women (CW) were administered an explicit category learning task that required rule acquisition and then a set shift following a rule change. COVIS was first fit to the behavioral results of the controls and then parameters of the model theoretically relevant to AN were altered to mimic the behavioral results.

RESULTS

Relative to CW, the AN-WR group displayed steeper learning curves (i.e., hyper learning) before the rule shift, but greater difficulty in learning the new categories after the rule shift (i.e., a deficit in set shifting). Hyper learning and set shifting deficits in the AN-WR group were not associated and differentially correlated with clinical measures. Hyper learning in the AN-WR group was simulated by increasing the model parameter that represents sensitivity to negative feedback (δ parameter), whereas the deficit in set shifting was simulated by altering the parameters that represent changes in rule selection and flexibility (λ and γ parameters, respectively).

CONCLUSIONS

These simulations suggest that multiple factors can impact category learning and set shifting in AN-WR individuals (e.g., alterations in sensitivity to negative feedback, rule selection deficits, and inflexibility) and provide an important starting point to further investigate this pervasive deficit in adult AN.

A neurocomputational theory of how explicit learning bootstraps early procedural learning

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.

Dissociating the cognitive effects of levodopa versus dopamine agonists in a neurocomputational model of learning in Parkinson's disease

BACKGROUND/AIMS

Levodopa and dopamine agonists have different effects on the motor, cognitive, and psychiatric aspects of Parkinson's disease (PD).

METHODS

Using a computational model of basal ganglia (BG) and prefrontal cortex (PFC) dopamine, we provide a theoretical synthesis of the dissociable effects of these dopaminergic medications on brain and cognition. Our model incorporates the findings that levodopa is converted by dopamine cells into dopamine, and thus activates prefrontal and striatal D(1) and D(2) dopamine receptors, whereas antiparkinsonian dopamine agonists directly stimulate D(2) receptors in the BG and PFC (although some have weak affinity to D(1) receptors).

RESULTS

In agreement with prior neuropsychological studies, our model explains how levodopa enhances, but dopamine agonists impair or have no effect on, stimulus-response learning and working memory.

CONCLUSION

Our model explains how levodopa and dopamine agonists have differential effects on motor and cognitive processes in PD.