Rule-based categorization deficits in focal basal ganglia lesion and Parkinson's disease patients

Bridging flexible goal-directed cognition and consciousness: The Goal-Aligning Representation Internal Manipulation theory

Goal-directed manipulation of internal representations is a key element of human flexible behaviour, while consciousness is commonly associated with higher-order cognition and human flexibility. Current perspectives have only partially linked these processes, thus preventing a clear understanding of how they jointly generate flexible cognition and behaviour. Moreover, these limitations prevent an effective exploitation of this knowledge for technological scopes. We propose a new theoretical perspective that extends our 'three-component theory of flexible cognition' toward higher-order cognition and consciousness, based on the systematic integration of key concepts from Cognitive Neuroscience and AI/Robotics. The theory proposes that the function of conscious processes is to support the alignment of representations with multi-level goals. This higher alignment leads to more flexible and effective behaviours. We analyse here our previous model of goal-directed flexible cognition (validated with more than 20 human populations) as a starting GARIM-inspired model. By bridging the main theories of consciousness and goal-directed behaviour, the theory has relevant implications for scientific and technological fields. In particular, it contributes to developing new experimental tasks and interpreting clinical evidence. Finally, it indicates directions for improving machine learning and robotics systems and for informing real-world applications (e.g., in digital-twin healthcare and roboethics).

Forward and backward spatial recall in Parkinson's disease and matched controls: A 1-year follow-up study

Patients with Parkinson's disease (PD) exhibit a domain-general visuospatial dysfunction; however, no previous study has examined changes over time in forward and backward spatial recall in PD against controls. To evaluate changes in short-term (STM) and working memory (WM) dysfunction in PD, the current study assessed performance on a computer-modified version of the Corsi Block-Tapping Test (forward and backward recall) at two-time points 1 year apart, while simultaneously exploring associations with potentially relevant demographic and clinical variables. We enrolled 38 patients with PD and 38 controls matched for age, sex, and Montreal Cognitive Assessment (MoCA) total scores. Linear mixed-effects models analyzed the primary measured variables (forward and backward scores). At baseline, the dysfunction effect sizes were as follows: forward recall (-0.45, 95% CI [-0.90, 0.01]) and backward recall (-0.26, 95% CI [-0.71, 0.19]). At follow-up, patients exhibited substantially greater difficulties in backward recall (-0.65, 95% CI [-1.18, -0.13]) compared to the baseline assessment, whereas the forward dysfunction effect size remained almost the same (-0.43, 95% CI [-0.94, 0.09]). Age (p = .005, f = 0.35) and total scores on MoCA (p = .017, f = 0.18), irrespective of group and recall condition, were significant predictors of spatial block scores. The pattern of dysfunction effect sizes indicates that, in contrast to forward recall, backward recall dysfunction in PD worsened 1-year after the baseline assessment, presumably reflecting the progression of PD-related visuospatial WM dysfunction.

Altered static and dynamic spontaneous neural activity in patients with ischemic pontine stroke

Objective

The purpose of the study was to investigate the abnormality both of static spontaneous brain activity and dynamic temporal variances following a pontine infarction.

Methods

Forty-six patients with chronic left pontine infarction (LPI), thirty-two patients with chronic right pontine infarction (RPI), and fifty healthy controls (HCs) were recruited for the study. The static amplitude of low-frequency fluctuations (sALFF), static regional homogeneity (sReHo), dynamic ALFF (dALFF), and dynamic ReHo (dReHo) were employed to detect the alterations in brain activity induced by an infarction. The Rey Auditory Verbal Learning Test and Flanker task were used to evaluate the verbal memory and visual attention function, respectively. Receiver operating characteristic curve analysis was used to reveal the potential capacity of these metrics to distinguish the patients from HCs.

Results

There were significant variations of these static and dynamic metrics in patients with chronic pontine infarction. The altered regions involved the supratentorial regions, including cortex and subcortical. Moreover, the altered metrics were significantly correlated with verbal memory and visual attention. In addition, these static and dynamic metrics also showed potential in distinguishing stroke patients with behavior deficits from HCs.

Conclusion

The pontine infarction-induced cerebral activation changes are observed in both motor and cognitive systems, indicating the functional damage and reorganization across the global cerebral level in these patients with subtentorial infarction, and there is a reciprocal effect between motor and cognitive impairment and repair.

Computational mechanisms of distributed value representations and mixed learning strategies

Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

Tracking prototype and exemplar representations in the brain across learning

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

Involvement of the Cortico-Basal Ganglia-Thalamocortical Loop in Developmental Stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.

Evidence of stable individual differences in implicit learning

There is a fundamental psychological and neuropsychological distinction between explicit and implicit memory, and it has been proposed that whereas there are stable trait individual differences in explicit memory ability, there are not such differences across people for implicit learning. There is, however, little evidence about whether or not there are stable trait differences in implicit learning. Here we performed a test-retest reliability study with healthy young adults in which they performed four implicit learning tasks (artificial grammar learning, probabilistic classification, serial response, and implicit category learning) twice, about a week apart. We found medium (by Cohen's guidelines) test-retest reliability for three of the tasks: probabilistic classification, serial response, and implicit category learning, suggesting that differences in implicit learning ability are more stable than originally thought. In addition, implicit learning on all tasks was unrelated to explicit measures: we did not find any correlation between implicit learning measures and independent measures of IQ, working memory, or explicit learning ability. These findings indicate that implicit learning, like explicit learning, varies reliably across individuals.

Impact of putamen stroke on task context updating: Evidence from P300 brain waves

According to the context updating theory, the oddball P300 component indexes brain activities underlying revision of the mental representation induced by incoming stimuli. It involves an attention-driven comparison process that evaluates the representation of the previous event in working memory. Delayed latencies have been reported for various types of stroke such as unilateral thalamic stroke. We investigated memory updating effects in patients with putamen stroke. Two groups of patients with putamen and thalamic stroke were recruited along with two control groups of young and old healthy participants. Auditory and visual P300 were elicited respectively in a two-stimulus oddball paradigm. The auditory P300 peak latencies were significantly longer in patients with a putamen lesion than in the aged and young control groups and the same pattern was found in the thalamus-lesioned patient. The delayed auditory P300 component in both patient groups but neither control group suggests impairment of memory updating in patients with putamen stroke comparable with thalamic stroke. Our study illustrates the important role of subcortical structures subserved in context updating.

White matter developmental trajectories associated with persistence and recovery of childhood stuttering

Stuttering affects the fundamental human ability of fluent speech production, and can have a significant negative impact on an individual's psychosocial development. While the disorder affects about 5% of all preschool children, approximately 80% of them recover naturally within a few years of stuttering onset. The pathophysiology and neuroanatomical development trajectories associated with persistence and recovery of stuttering are still largely unknown. Here, the first mixed longitudinal diffusion tensor imaging (DTI) study of childhood stuttering has been reported. A total of 195 high quality DTI scans from 35 children who stutter (CWS) and 43 controls between 3 and 12 years of age were acquired, with an average of three scans per child, each collected approximately a year apart. Fractional anisotropy (FA), a measure reflecting white matter structural coherence, was analyzed voxel-wise to examine group and age-related differences using a linear mixed-effects (LME) model. Results showed that CWS exhibited decreased FA relative to controls in the left arcuate fasciculus, underlying the inferior parietal and posterior temporal areas, and the mid body of corpus callosum. Further, white matter developmental trajectories reflecting growth rate of these tract regions differentiated children with persistent stuttering from those who recovered from stuttering. Specifically, a reduction in FA growth rate (i.e., slower FA growth with age) in persistent children relative to fluent controls in the left arcuate fasciculus and corpus callosum was found, which was not evident in recovered children. These findings provide first glimpses into the possible neural mechanisms of onset, persistence, and recovery of childhood stuttering. Hum Brain Mapp 38:3345-3359, 2017. © 2017 Wiley Periodicals, Inc.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.

A Comparison of the neural correlates that underlie rule-based and information-integration category learning

The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc.

Criterion learning in rule-based categorization: simulation of neural mechanism and new data

In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning.

Cognitive deficits in Parkinson's disease: a cognitive neuroscience perspective

Progress in characterization of the nature, neural basis, and treatment of cognitive deficits in Parkinson's disease is reviewed from the perspective of cognitive neuroscience. An initial emphasis on fronto-striatal executive deficits is surveyed along with the discoveries of disruption as well as remediation of certain impairments by dopaminergic mediation and their association with theories of reinforcement learning. Subsequent focus on large cohorts has revealed considerable heterogeneity in the cognitive impairments as well as a suggestion of at least two distinct syndromes, with the dopamine-dependent fronto-striatal deficits being somewhat independent of other signs commonly associated with Parkinson's disease dementia. The utility is proposed of a new, integrated cognitive neuroscience approach based on combining genetic and neuroimaging methodologies with neuropsychological and, ultimately, psychopharmacological approaches.

Feature diagnosticity affects representations of novel and familiar objects

Many features can describe a concept, but only some features define a concept in that they enable discrimination of items that are instances of a concept from (similar) items that are not. We refer to this property of some features as feature diagnosticity. Previous work has described the behavioral effects of feature diagnosticity, but there has been little work on explaining why and how these effects arise. In this study, we aimed to understand the impact of feature diagnosticity on concept representations across two complementary experiments. In Experiment 1, we manipulated the diagnosticity of one feature, color, for a set of novel objects that human participants learned over the course of 1 week. We report behavioral and neural evidence that diagnostic features are likely to be automatically recruited during remembering. Specifically, individuals activated color-selective regions of ventral temporal cortex (specifically, left fusiform gyrus and left inferior temporal gyrus) when thinking about the novel objects, although color information was never explicitly probed during the task. Moreover, multiple behavioral and neural measures of the effects of feature diagnosticity were correlated across participants. In Experiment 2, we examined relative color association in familiar object categories, which varied in feature diagnosticity (fruits and vegetables, household items). Taken together, these results offer novel insights into the neural mechanisms underlying concept representations by demonstrating that automatic recruitment of diagnostic information gives rise to behavioral effects of feature diagnosticity.

Fronto-striatal gray matter contributions to discrimination learning in Parkinson's disease

Discrimination learning deficits in Parkinson's disease (PD) have been well-established. Using both behavioral patient studies and computational approaches, these deficits have typically been attributed to dopamine imbalance across the basal ganglia. However, this explanation of impaired learning in PD does not account for the possible contribution of other pathological changes that occur in the disease process, importantly including gray matter loss. To address this gap in the literature, the current study explored the relationship between fronto-striatal gray matter atrophy and learning in PD. We employed a discrimination learning task and computational modeling in order to assess learning rates in non-demented PD patients. Behaviorally, we confirmed that learning rates were reduced in patients relative to controls. Furthermore, voxel-based morphometry imaging analysis demonstrated that this learning impairment was directly related to gray matter loss in discrete fronto-striatal regions (specifically, the ventromedial prefrontal cortex, inferior frontal gyrus and nucleus accumbens). These findings suggest that dopaminergic imbalance may not be the sole determinant of discrimination learning deficits in PD, and highlight the importance of factoring in the broader pathological changes when constructing models of learning in PD.

Nonlinguistic learning in individuals with aphasia: effects of training method and stimulus characteristics

PURPOSE

The purpose of the current study was to explore nonlinguistic learning ability in individuals with aphasia, examining the impact of stimulus typicality and feedback on success with learning.

METHOD

Eighteen individuals with aphasia and 8 nonaphasic controls participated in this study. All participants completed 4 computerized, nonlinguistic category-learning tasks. Learning ability was probed under 2 methods of instruction: feedback-based (FB) and paired-associate (PA). The impact of task complexity on learning ability was also examined, comparing 2 stimulus conditions: typical and atypical. Performance was compared between groups and across conditions.

RESULTS

The controls were able to successfully learn categories under all conditions. For the individuals with aphasia, 2 patterns of performance arose: One subgroup of individuals was able to maintain learning across task manipulations and conditions; the other subgroup demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions.

CONCLUSION

Results support the hypothesis that impairments of general learning are present in individuals with aphasia. Some individuals demonstrated the ability to extract category information under complex training conditions; others learned only under conditions that were simplified and that emphasized salient category features. Overall, the typical training condition facilitated learning for all of the participants. Findings have implications for treatment, which are discussed.

Dopamine-mediated learning and switching in cortico-striatal circuit explain behavioral changes in reinforcement learning

The basal ganglia are thought to play a crucial role in reinforcement learning. Central to the learning mechanism are dopamine (DA) D1 and D2 receptors located in the cortico-striatal synapses. However, it is still unclear how this DA-mediated synaptic plasticity is deployed and coordinated during reward-contingent behavioral changes. Here we propose a computational model of reinforcement learning that uses different thresholds of D1- and D2-mediated synaptic plasticity which are antagonized by DA-independent synaptic plasticity. A phasic increase in DA release caused by a larger-than-expected reward induces long-term potentiation (LTP) in the direct pathway, whereas a phasic decrease in DA release caused by a smaller-than-expected reward induces a cessation of long-term depression, leading to LTP in the indirect pathway. This learning mechanism can explain the robust behavioral adaptation observed in a location-reward-value-association task where the animal makes shorter latency saccades to reward locations. The changes in saccade latency become quicker as the monkey becomes more experienced. This behavior can be explained by a switching mechanism which activates the cortico-striatal circuit selectively. Our model also shows how D1- or D2-receptor blocking experiments affect selectively either reward or no-reward trials. The proposed mechanisms also explain the behavioral changes in Parkinson's disease.