The roles of the caudate nucleus in human classification learning

Comparative anatomy of the caudate nucleus in canids and felids: Associations with brain size, curvature, cross-sectional properties, and behavioral ecology

The evolutionary history of canids and felids is marked by a deep time separation that has uniquely shaped their behavior and phenotype toward refined predatory abilities. The caudate nucleus is a subcortical brain structure associated with both motor control and cognitive, emotional, and executive functions. We used a combination of three-dimensional imaging, allometric scaling, and structural analyses to compare the size and shape characteristics of the caudate nucleus. The sample consisted of MRI scan data obtained from six canid species (Canis lupus lupus, Canis latrans, Chrysocyon brachyurus, Lycaon pictus, Vulpes vulpes, Vulpes zerda), two canid subspecies (Canis lupus familiaris, Canis lupus dingo), as well as three felids (Panthera tigris, Panthera uncia, Felis silvestris catus). Results revealed marked conservation in the scaling and shape attributes of the caudate nucleus across species, with only slight deviations. We hypothesize that observed differences in caudate nucleus size and structure for the domestic canids are reflective of enhanced cognitive and emotional pathways that possibly emerged during domestication.

Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

How well do rudimentary plasticity rules predict adult visual object learning?

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer. Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear-in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings. Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and publicly release) empirical benchmarks for comparing learning models to humans. We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

Romantic love evolved by co-opting mother-infant bonding

For 25 years, the predominant evolutionary theory of romantic love has been Fisher's theory of independent emotion systems. That theory suggests that sex drive, romantic attraction (romantic love), and attachment are associated with distinct neurobiological and endocrinological systems which evolved independently of each other. Psychological and neurobiological evidence, however, suggest that a competing theory requires attention. A theory of co-opting mother-infant bonding sometime in the recent evolutionary history of humans may partially account for the evolution of romantic love. I present a case for this theory and a new approach to the science of romantic love drawing on human psychological, neurobiological, and (neuro)endocrinological studies as well as animal studies. The hope is that this theoretical review, along with other publications, will generate debate in the literature about the merits of the theory of co-opting mother-infant bonding and a new evolutionary approach to the science of romantic love.

Lateralization of the cerebral network of inhibition in children before and after cognitive training

Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. IC relies on a lateralized cortico-subcortical brain network including the inferior frontal cortex, anterior parts of insula, anterior cingulate cortex, caudate nucleus and putamen. Brain asymmetries play a critical role for IC efficiency. In parallel to age-related changes, IC can be improved following training. The aim of this study was to (1) assess the lateralization of IC network in children (N = 60, 9-10 y.o.) and (2) examine possible changes in neural asymmetry of this network from anatomical (structural MRI) and functional (resting-state fMRI) levels after 5-week computerized IC vs. active control (AC) training. We observed that IC training, but not AC training, led to a leftward lateralization of the putamen anatomy, similarly to what is observed in adults, supporting that training could accelerate the maturation of this structure.

Texture-based brain networks for characterization of healthy subjects from MRI

Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

Contributions of the Basal Ganglia to Visual Perceptual Decisions

The basal ganglia (BG) make up a prominent nexus between visual and motor-related brain regions. In contrast to the BG's well-established roles in movement control and value-based decision making, their contributions to the transformation of visual input into an action remain unclear, especially in the context of perceptual decisions based on uncertain visual evidence. This article reviews recent progress in our understanding of the BG's contributions to the formation, evaluation, and adjustment of such decisions. From theoretical and experimental perspectives, the review focuses on four key stations in the BG network, namely, the striatum, pallidum, subthalamic nucleus, and midbrain dopamine neurons, which can have different roles and together support the decision process.

Insulin-like growth factor 1 supplementation supports motor coordination and affects myelination in preterm pigs

Introduction

Preterm infants have increased risk of impaired neurodevelopment to which reduced systemic levels of insulin-like growth factor 1 (IGF-1) in the weeks after birth may play a role. Hence, we hypothesized that postnatal IGF-1 supplementation would improve brain development in preterm pigs, used as a model for preterm infants.

Methods

Preterm pigs delivered by cesarean section received recombinant human IGF-1/IGF binding protein-3 complex (rhIGF-1/rhIGFBP-3, 2.25 mg/kg/day) or vehicle from birth to postnatal day 19. Motor function and cognition were assessed by monitoring of in-cage and open field activities, balance beam test, gait parameters, novel object recognition and operant conditioning tests. Collected brains were subject to magnetic resonance imaging (MRI), immunohistochemistry, gene expression analyses and protein synthesis measurements.

Results

The IGF-1 treatment increased cerebellar protein synthesis rates (both in vivo and ex vivo). Performance in the balance beam test was improved by IGF-1 but not in other neurofunctional tests. The treatment decreased total and relative caudate nucleus weights, without any effects to total brain weight or grey/white matter volumes. Supplementation with IGF-1 reduced myelination in caudate nucleus, cerebellum, and white matter regions and decreased hilar synapse formation, without effects to oligodendrocyte maturation or neuron differentiation. Gene expression analyses indicated enhanced maturation of the GABAergic system in the caudate nucleus (decreased NKCC1:KCC2 ratio) with limited effects in cerebellum or hippocampus.

Conclusion

Supplemental IGF-1 during the first three weeks after preterm birth may support motor function by enhancing GABAergic maturation in the caudate nucleus, despite reduced myelination. Supplemental IGF-1 may support postnatal brain development in preterm infants, but more studies are required to identify optimal treatment regimens for subgroups of very or extremely preterm infants.

Striatal hypoactivation during monetary loss anticipation in individuals with substance use disorders in a heterogenous urban American Indian sample

Research suggests that disproportionate exposure to risk factors places American Indian (AI) peoples at higher risk for substance use disorders (SUD). Although SUD is linked to striatal prioritization of drug rewards over other appetitive stimuli, there are gaps in the literature related to the investigation of aversive valuation processing, and inclusion of AI samples. To address these gaps, this study compared striatal anticipatory gain and loss processing between AI-identified with SUD (SUD+; n = 52) and without SUD (SUD-; n = 35) groups from the Tulsa 1000 study who completed a monetary incentive delay (MID) task during functional magnetic resonance imaging. Results indicated that striatal activations in the nucleus accumbens (NAcc), caudate, and putamen were greatest for anticipating gains (ps < 0.001) but showed no group differences. In contrast to gains, the SUD+ exhibited lower NAcc (p = .01, d =0.53) and putamen (p = .04, d =0.40) activation to anticipating large losses than the comparison group. Within SUD+ , lower striatal responses during loss anticipations were associated with slower MID reaction times (NAcc: r = -0.43; putamen: r = -0.35) during loss trials. This is among the first imaging studies to examine underlying neural mechanisms associated with SUD within AIs. Attenuated loss processing provides initial evidence of a potential mechanism wherein blunted prediction of aversive consequences may be a defining feature of SUD that can inform future prevention and intervention targets.

Hierarchical categorization learning is associated with representational changes in the dorsal striatum and posterior frontal and parietal cortex

Learning and recognition can be improved by sorting novel items into categories and subcategories. Such hierarchical categorization is easy when it can be performed according to learned rules (e.g., "if car, then automatic or stick shift" or "if boat, then motor or sail"). Here, we present results showing that human participants acquire categorization rules for new visual hierarchies rapidly, and that, as they do, corresponding hierarchical representations of the categorized stimuli emerge in patterns of neural activation in the dorsal striatum and in posterior frontal and parietal cortex. Participants learned to categorize novel visual objects into a hierarchy with superordinate and subordinate levels based on the objects' shape features, without having been told the categorization rules for doing so. On each trial, participants were asked to report the category and subcategory of the object, after which they received feedback about the correctness of their categorization responses. Participants trained over the course of a one-hour-long session while their brain activation was measured using functional magnetic resonance imaging. Over the course of training, significant hierarchy learning took place as participants discovered the nested categorization rules, as evidenced by the occurrence of a learning trial, after which performance suddenly increased. This learning was associated with increased representational strength of the newly acquired hierarchical rules in a corticostriatal network including the posterior frontal and parietal cortex and the dorsal striatum. We also found evidence suggesting that reinforcement learning in the dorsal striatum contributed to hierarchical rule learning.

Rule and Exemplar-based Transfer in Category Learning

We compared the neural systems involved in transfer to novel stimuli via rule application versus exemplar processing. Participants learned a categorization task involving abstraction of a complex rule and then categorized different types of transfer stimuli without feedback. Rule stimuli used new features and therefore could only be categorized using the rule. Exemplar stimuli included only one of the features necessary to apply the rule and therefore required participants to categorize based on similarity to individual previously learned category members. Consistent and inconsistent stimuli were formed so that both the rule and feature similarity indicated the same category (consistent) or opposite categories (inconsistent). We found that all conditions eliciting rule-based transfer recruited a medial prefrontal-anterior hippocampal network associated with schematic memory. In contrast, exemplar-based transfer recruited areas of the intraparietal sulcus associated with learning and executing stimulus-category mappings along with the posterior hippocampus. These results support theories of categorization that postulate complementary learning and generalization strategies based on schematic and exemplar mechanisms.

Dorsomedial striatum, but not dorsolateral striatum, is necessary for rat category learning

Categorization is an adaptive cognitive function that allows us to generalize knowledge to novel situations. Converging evidence from neuropsychological, neuroimaging, and neurophysiological studies suggest that categorization is mediated by the basal ganglia; however, there is debate regarding the necessity of each subregion of the basal ganglia and their respective functions. The current experiment examined the roles of the dorsomedial striatum (DMS; homologous to the head of the caudate nucleus) and dorsolateral striatum (DLS; homologous to the body and tail of the caudate nucleus) in category learning by combining selective lesions with computational modeling. Using a touchscreen apparatus, rats were trained to categorize distributions of visual stimuli that varied along two continuous dimensions (i.e., spatial frequency and orientation). The tasks either required attention to one stimulus dimension (spatial frequency or orientation; 1D tasks) or both stimulus dimensions (spatial frequency and orientation; 2D tasks). Rats with NMDA lesions of the DMS were impaired on both the 1D tasks and 2D tasks, whereas rats with DLS lesions showed no impairments. The lesions did not affect performance on a discrimination task that had the same trial structure as the categorization tasks, suggesting that the category impairments effected processes relevant to categorization. Model simulations were conducted using a neural network to assess the effect of the DMS lesions on category learning. Together, the results suggest that the DMS is critical to map category representations to appropriate behavioral responses, whereas the DLS is not necessary for categorization.

Dorsal posterior cingulate cortex responds to negative feedback information supporting learning and relearning of response policies

Many challenges in life come without explicit instructions. Instead, humans need to test, select, and adapt their behavioral responses based on feedback from the environment. While reward-centric accounts of feedback processing primarily stress the reinforcing aspect of positive feedback, feedback’s central function from an information-processing perspective is to offer an opportunity to correct errors, thus putting a greater emphasis on the informational content of negative feedback. Independent of its potential rewarding value, the informational value of performance feedback has recently been suggested to be neurophysiologically encoded in the dorsal portion of the posterior cingulate cortex (dPCC). To further test this association, we investigated multidimensional categorization and reversal learning by comparing negative and positive feedback in an event-related functional magnetic resonance imaging experiment. Negative feedback, compared with positive feedback, increased activation in the dPCC as well as in brain regions typically involved in error processing. Only in the dPCC, subarea d23, this effect was significantly enhanced in relearning, where negative feedback signaled the need to shift away from a previously established response policy. Together with previous findings, this result contributes to a more fine-grained functional parcellation of PCC subregions and supports the dPCC’s involvement in the adaptation to behaviorally relevant information from the environment.

Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis

Objective

Mild cognitive impairment (MCI) is a heterogeneous syndrome characterized by cognitive impairment on neurocognitive tests but accompanied by relatively intact daily activities. Due to high variation and no objective methods for diagnosing and treating MCI, guidance on neuroimaging is needed. The study has explored the neuroimaging biomarkers using the support vector machine (SVM) method to predict MCI.

Methods

In total, 53 patients with MCI and 68 healthy controls were involved in scanning resting-state functional magnetic resonance imaging (rs-fMRI). Neurocognitive testing and Structured Clinical Interview, such as Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) test, Activity of Daily Living (ADL) Scale, Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Montreal Cognitive Assessment (MoCA), and Hamilton Rating Scale for Depression (HRSD), were utilized to assess participants' cognitive state. Neuroimaging data were analyzed with the regional homogeneity (ReHo) and SVM methods.

Results

Compared with healthy comparisons (HCs), ReHo of patients with MCI was decreased in the right caudate. In addition, the SVM classification achieved an overall accuracy of 68.6%, sensitivity of 62.26%, and specificity of 58.82%.

Conclusion

The results suggest that abnormal neural activity in the right cerebrum may play a vital role in the pathophysiological process of MCI. Moreover, the ReHo in the right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.

Distributed Neural Systems Support Flexible Attention Updating during Category Learning

To accurately categorize items, humans learn to selectively attend to stimulus dimensions that are most relevant to the task. Models of category learning describe the interconnected cognitive processes that contribute to attentional tuning as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account whereby categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning across trials. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

Projection of Damaged Visual and Language Regions on Low Trail Making Test Part-B Performance in Stroke Patients

Background

The Trail Making Test Part-B (TMT-B) is an attention functional test to investigate cognitive dysfunction. It requires the ability to recognize not only numbers but also letters. We analyzed the relationship between brain lesions in stroke patients and their TMT-B performance.

Methods

From the TMT-B, two parameters (score and completion time) were obtained. The subjects were classified into several relevant groups by their scores and completion times through a data-driven analysis (k-means clustering). The score-classified groups were characterized by low (≤10), moderate (10 &lt; score &lt; 25), and high (25) scores. In terms of the completion time, the subjects were classified into four groups. The lesion degree in the brain was calculated for each of the 116 regions classified by automated anatomical labeling (AAL). For each group, brain sites with a significant difference (corrected p &lt; 0.1) between each of the 116 regions were determined by a Wilcoxon Rank–Sum significant difference test.

Results

Lesions at the cuneus and the superior occipital gyrus, which are mostly involved in visual processing, were significant (corrected p &lt; 0.1) in the low-score group. Furthermore, the moderate-score group showed more-severe lesion degrees (corrected p &lt; 0.05) in the regions responsible for the linguistic functions, such as the superior temporal gyrus and the supramarginal gyrus. As for the completion times, lesions in the calcarine, the cuneus, and related regions were significant (corrected p &lt; 0.1) in the fastest group as compared to the slowest group. These regions are also involved in visual processing.

Conclusion

The TMT-B results revealed that the subjects in the low-score group or the slowest- group mainly had damage in the visual area, whereas the subjects in the moderate-score group mainly had damage in the language area. These results suggest the potential utility of TMT-B performance in the lesion site.

Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia

Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Subcortical structures play a significant role in cognitive and social-emotional behaviors and their abnormalities are associated with neuropsychiatric conditions. This exploratory study utilized ABIDE II/COBRE MRI and corresponding phenotypic datasets to compare subcortical volumes of adults with ASD (n = 29), SZ (n = 51) and age and gender matched neurotypicals (NT). We examined the association between subcortical volumes and select behavioral measures to determine whether core symptomatology of disorders could be explained by subcortical association patterns. We observed volume differences in ASD (viz., left pallidum, left thalamus, left accumbens, right amygdala) but not in SZ compared to their respective NT controls, reflecting morphometric changes specific to one of the disorder groups. However, left hippocampus and amygdala volumes were implicated in both disorders. A disorder-specific negative correlation (r = −0.39, p = 0.038) was found between left-amygdala and scores on the Social Responsiveness Scale (SRS) Social-Cognition in ASD, and a positive association (r = 0.29, p = 0.039) between full scale IQ (FIQ) and right caudate in SZ. Significant correlations between behavior measures and subcortical volumes were observed in NT groups (ASD-NT range; r = −0.53 to −0.52, p = 0.002 to 0.004, SZ-NT range; r = −0.41 to −0.32, p = 0.007 to 0.021) that were non-significant in the disorder groups. The overlap of subcortical volumes implicated in ASD and SZ may reflect common neurological mechanisms. Furthermore, the difference in correlation patterns between disorder and NT groups may suggest dysfunctional connectivity with cascading effects unique to each disorder and a potential role for IQ in mediating behavior and brain circuits.

The Role of Implicit Memory in the Development and Recovery from Trauma-Related Disorders

Post-traumatic Stress Disorder is a chronic condition that occurs following a traumatic experience. Information processing models of PTSD focus on integrating situationally triggered sensory-emotional memories with consciously accessible autobiographical memories. Review of the nature of implicit memory supports the view that sensory-emotional memories are implicit in nature. Dissociation was also found to be associated with the development and severity of PTSD, as well as deficits in autobiographical memory. Moreover, disorganized attachment (DA) was associated with greater degrees of dissociation and PTSD, and like the defining neural activation in PTSD, was found to be associated with basal ganglia activity. In addition, subcortical neuroception of safety promotes a neurophysiological substrate supportive of social engagement and inhibition of fear-based responses. Furthermore, activation of representations of co-created imagined scenes of safety and secure attachment are associated with increases in this neurophysiological substrate. Repeated priming of secure attachment imagery was associated with modification of internal working models of DA along with reductions in dissociation and recovery from complex PTSD. In conclusion, it is posited that adequate recovery from extensive trauma experiences requires more than conscious elaboration of traumatic autobiographical memories and that the application of implicit nonconscious memory modification strategies will facilitate more optimal recovery.

Neural correlates of the inverse base rate effect

The inverse base rate effect (IBRE) is a nonrational behavioral phenomenon in predictive learning. Canonically, participants learn that the AB stimulus compound leads to one outcome and that AC leads to another outcome, with AB being presented three times as often as AC. When subsequently presented with BC, the outcome associated with AC is preferentially selected, in opposition to the underlying base rates of the outcomes. The current leading explanation is based on error‐driven learning. A key component of this account is prediction error, a concept previously linked to a number of brain areas including the anterior cingulate, the striatum, and the dorsolateral prefrontal cortex. The present work is the first fMRI study to directly examine the IBRE. Activations were noted in brain areas linked to prediction error, including the caudate body, the anterior cingulate, the ventromedial prefrontal cortex, and the right dorsolateral prefrontal cortex. Analyzing the difference in activations for singular key stimuli (B and C), as well as frequency matched controls, supports the predictions made by the error‐driven learning account.

Striatal oxidative damages and neuroinflammation correlate with progression and survival of Lewy body and Alzheimer diseases

Neurodegenerative diseases are a class of chronic and complex disorders featuring progressive loss of neurons in distinct brain areas. The mechanisms responsible for the disease progression in neurodegeneration are not fully illustrated. In this observational study, we have examined diverse biochemical parameters in the caudate and putamen of patients with Lewy body diseases (LBDs) and Alzheimer disease (AD), shedding some light on the involvement of oxidative damage and neuroinflammation in advanced neurodegeneration. We performed Spearman and Mantel-Cox analyses to investigate how oxidative stress and neuroinflammation exert comprehensive effects on disease progression and survival. Disease progression in LBDs correlated positively with poly (ADP-Ribose) and triggering receptors expressed on myeloid cell 2 levels in the striatum of LBD cohorts, indicating that potential parthanatos was a dominant feature of worsening disease progression and might contribute to switching microglial inflammatory phenotypes. Disease progression in AD corresponds negatively with 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxo-dG) and myeloperoxidase concentrations in the striatum, suggesting that possible mitochondria dysfunction may be involved in the progression of AD via a mechanism of β-amyloid entering the mitochondria and subsequent free radicals generation. Patients with lower striatal 8-oxo-dG and myeloperoxidase levels had a survival advantage in AD. The age of onset also affected disease progression. Tissue requests for the postmortem biochemistry, genetics, and autoradiography studies were approved by the Washington University Alzheimer’s Disease Research Center (ADRC) Biospecimens Committee (ethics approval reference number: T1705, approval date: August 6, 2019). Recombinant DNA and Hazardous Research Materials were approved by the Washington University Environmental Health & Safety Biological Safety Committee (approval code: 3739, approval date: February 25, 2020). Radioactive Material Authorization was approved by the Washington University Environmental Health & Safety Radiation Safety Committee (approval code: 1056, approval date: September 18, 2019).

FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response

The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling. Since HRF is influenced by non-neural factors, to date it has largely been considered as a confound or has been ignored in many analyses. However, underlying biophysics suggests that the HRF may contain meaningful correlates of neural activity, which might be unavailable through conventional fMRI metrics. Here, we estimated the HRF by performing deconvolution on resting-state fMRI data from a longitudinal sample of 25 healthy controls scanned twice and 44 adults with obsessive-compulsive disorder (OCD) before and after 4-weeks of intensive cognitive-behavioral therapy (CBT). HRF response height, time-to-peak and full-width at half-maximum (FWHM) in OCD were abnormal before treatment and normalized after treatment in regions including the caudate. Pre-treatment HRF predicted treatment outcome (OCD symptom reduction) with 86.4% accuracy, using machine learning. Pre-treatment HRF response height in the caudate head and time-to-peak in the caudate tail were top-predictors of treatment response. Time-to-peak in the caudate tail, a region not typically identified in OCD studies using conventional fMRI activation or connectivity measures, may carry novel importance. Additionally, pre-treatment response height in caudate head predicted post-treatment OCD severity (R = -0.48, P = 0.001), and was associated with treatment-related OCD severity changes (R = -0.44, P = 0.0028), underscoring its relevance. With HRF being a reliable marker sensitive to brain function, OCD pathology, and intervention-related changes, these results could guide future studies towards novel discoveries not possible through conventional fMRI approaches like standard BOLD activation or connectivity.

Structural connectivity changes in the motor execution network after stroke rehabilitation

Background:

Although quite a very few studies have tested structural connectivity changes following an intervention, it reflects only selected key brain regions in the motor network. Thus, the understanding of structural connectivity changes related to the motor recovery process remains unclear.

Objective:

This study investigated structural connectivity changes of the motor execution network following a combined intervention of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (OT) after a stroke using graph theory approach.

Methods:

Fifty-six stroke patients underwent Fugl-Meyer Assessment (FMA), Wolf Motor Function Test-Functional Ability Scale (WMFT-FAS), diffusion tensor imaging (DTI), and T1 weighted imaging before and after the intervention. We examined graph theory measures related to twenty brain regions using structural connectomes.

Results:

The ipsilesional and contralesional hemisphere showed structural connectivity changes post-intervention after stroke. We found significantly increased regional centralities and nodal efficiency within the frontal pole and decreased degree centrality and nodal efficiency in the ipsilesional thalamus. Correlations were found between network measures and clinical assessments in the cuneus, postcentral gyrus, precentral gyrus, and putamen of the ipsilesional hemisphere. The contralesional areas such as the caudate, cerebellum, and frontal pole also showed significant correlations.

Conclusions:

This study was helpful to expand the understanding of structural connectivity changes in both hemispheric networks during the motor recovery process following LF-rTMS and intensive OT after stroke.

Postpartum Stress and Neural Regulation of Emotion among First-Time Mothers

Early parenting relies on emotion regulation capabilities, as mothers are responsible for regulating both their own emotional state and that of their infant during a time of new parenting-related neural plasticity and potentially increased stress. Previous research highlights the importance of frontal cortical regions in facilitating effective emotion regulation, but few studies have investigated the neural regulation of emotion among postpartum women. The current study employed a functional neuroimaging (fMRI) approach to explore the association between perceived stress, depressive symptoms, and the neural regulation of emotion in first-time mothers. Among 59 postpartum mothers, higher perceived stress during the postpartum period was associated with less self-reported use of cognitive reappraisal in everyday life, and greater use of emotion suppression. While viewing standardized aversive images during the Emotion Regulation Task (ERT), mothers were instructed to experience their natural emotional state (Maintain) or to decrease the intensity of their negative emotion by using cognitive reappraisal (Reappraise). Whole-brain analysis revealed a two-way interaction of perceived stress x condition in the right dorsolateral prefrontal cortex (DLPFC) at p < .05 cluster-wise corrected, controlling for postpartum months and scanner type. Higher levels of perceived stress were associated with heightened right DLPFC activity while engaging in cognitive reappraisal versus naturally responding to negative stimuli. Higher right DLPFC activity during Reappraise versus Maintain was further associated with elevated parenting stress. Findings suggest that stress and everyday reappraisal use is reflected in mothers' neural regulation of emotion and may have important implications for their adaptation to parenthood.

Corticostriatal Regulation of Language Functions

The role of corticostriatal circuits in language functions is unclear. In this review, we consider evidence from language learning, syntax, and controlled language production and comprehension tasks that implicate various corticostriatal circuits. Converging evidence from neuroimaging in healthy individuals, studies in populations with subcortical dysfunction, pharmacological studies, and brain stimulation suggests a domain-general regulatory role of corticostriatal systems in language operations. The role of corticostriatal systems in language operations identified in this review is likely to reflect a broader function of the striatum in responding to uncertainty and conflict which demands selection, sequencing, and cognitive control. We argue that this role is dynamic and varies depending on the degree and form of cognitive control required, which in turn will recruit particular corticostriatal circuits and components organised in a cognitive hierarchy.

Neural Mechanisms of Observational Learning: A Neural Working Model

Humans and some animal species are able to learn stimulus-response (S-R) associations by observing others' behavior. It saves energy and time and avoids the danger of trying the wrong actions. Observational learning (OL) depends on the capability of mapping the actions of others into our own behaviors, processing outcomes, and combining this knowledge to serve our goals. Observational learning plays a central role in the learning of social skills, cultural knowledge, and tool use. Thus, it is one of the fundamental processes in which infants learn about and from adults (Byrne and Russon, 1998). In this paper, we review current methodological approaches employed in observational learning research. We highlight the important role of the prefrontal cortex and cognitive flexibility to support this learning process, develop a new neural working model of observational learning, illustrate how imitation relates to observational learning, and provide directions for future research.

A Study on Human Learning Ability during Classification of Motion and Colour Visual Cues and Their Combination

The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

Asthma-Specific Temporal Variability Reveals the Effect of Group Cognitive Behavior Therapy in Asthmatic Patients

Background: Group cognitive behavior therapy (GCBT) is a successful therapy for asthma. However, the neural biomarker of GCBT which could be used in clinic remains unclear. The temporal variability is a novel concept to characterize the dynamic functional connectivity (FC), which has many advantages as biomarker. Therefore, the aim of this study is to explore the potential difference of temporal variability between asthmatic patients and healthy controls, then determine the different patterns of temporal variability between pre- and post-treatment group and reveal the relationship between the variability and the symptoms improvement reduced by GCBT. Methods: At baseline, 40 asthmatic patients and 40 matched controls received resting-state functional magnetic resonance imaging (fMRI) scans and clinical assessments. After 8 weeks of GCBT treatment, 17 patients received fMRI scans, and assessments again. Temporal variability at baseline and post-treatment were calculated for further analysis. Results: Compared with controls, asthmatic patients showed widespread decreases in temporal variability. Moreover, the variability in both right caudate and left putamen were positively correlated with asthma control level. After GCBT, asthma control level and depression of patients were improved. Meanwhile, compared with pre-GCBT, patients after treatment showed lower variability in left opercular of Rolandic, right parahippocampal gyrus and right lingual gyrus, as well as higher variability in left temporal pole. Variability in regions which were found abnormal at baseline did not exhibit significant differences between post-GCBT and controls. Conclusions: Asthma-specific changes of dynamic functional connectivity may serve as promising underpinnings of GCBT for asthma. Clinical Trial Registration: http://www.chictr.org.cn/index.aspx, identifier: Chi-CTR-15007442.

Conceptual anchoring dissociates implicit and explicit category learning

Categorization researchers have long debated the possibility of multiple category-learning systems. The need persists for paradigms that dissociate explicit-declarative category-learning processes (featuring verbalizable category rules) from implicit-procedural processes (featuring stimulus-response associations lying beneath declarative cognition). The authors contribute a new paradigm, using perfectly matched exclusive-or (XOR) category tasks differing only in the availability or absence of easily verbalizable conceptual content. This manipulation transformed learning. The conceptual task alone was learned suddenly, by insightful rule discovery, producing explicit-declarative XOR knowledge. The perceptual task was learned more gradually, consistent with associative-learning processes, producing impoverished declarative knowledge. We also tested participants under regimens of immediate and deferred reinforcement. The conceptual task alone was learned through processes that survive the loss of trial-by-trial reinforcement. All results support the idea that humans have perceptual-associative processes for implicit learning, but also an overlain conceptual system that under the right circumstances constitutes a parallel explicit-declarative category-learning system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Lithium modulates striatal reward anticipation and prediction error coding in healthy volunteers

Lithium is one of the most effective mood-stabilizing medications in bipolar disorder. This study was designed to test whether lithium administration may stabilize mood via effects on reward processing. It was hypothesized that lithium administration would modulate reward processing in the striatum and affect both anticipation and outcome computations. Thirty-seven healthy human participants (18 males, 33 with suitable fMRI data) received 11 (±1) days of lithium carbonate or placebo intervention (double-blind), after which they completed the monetary incentive delay task while fMRI data were collected. The monetary incentive delay task is a robust task with excellent test-retest reliability and is well suited to investigate different phases of reward processing within the caudate and nucleus accumbens. To test for correlations with prediction error signals a Rescorla-Wagner reinforcement-learning model was applied. Lithium administration enhanced activity in the caudate during reward anticipation compared to placebo. In contrast, lithium administration reduced caudate and nucleus accumbens activity during reward outcome. This latter effect seems related to learning as reward prediction errors showed a positive correlation with caudate and nucleus accumbens activity during placebo, which was absent after lithium administration. Lithium differentially modulates the anticipation relative to the learning of rewards. This suggests that lithium might reverse dampened reward anticipation while reducing overactive reward updating in patients with bipolar disorder. This specific effect of lithium suggests that a targeted modulation of reward learning may be a viable approach for novel interventions in bipolar disorder.

On How Psychophysical Thresholds are Altered by Unilateral Brain Injury Due to Stroke

OBJECTIVE

This study examined whether and how the absolute thresholds and the just noticeable difference thresholds for eleven, sensory/perceptual continua are altered by unilateral left and right hemisphere lesions due to stroke relative to healthy subjects.

METHODS

The three subject groups were those with unilateral right hemisphere lesions (n=21), with unilateral left hemisphere lesions (n=13), and age-matched control subjects (n=76). Absolute thresholds of sensory detection and just noticeable difference thresholds were assessed for perceptual continua spanning the visual, tactile, proprioceptive, thermal, and gustatory sensory modalities. For stroke subjects, brain lesions were analyzed using subtraction techniques and volume analysis with the MRIcro and MRIcroN software programs. Stroke subjects also complete tests for spatial neglect, stroke severity and functional independence.

RESULTS

There was no significant difference among subject groups regarding gender, race, hand dominance, age, or educational composition. There was no significant difference between subjects with right and left hemisphere lesions on measures of function, stroke severity, or lesion volume except for those with spatial neglect. The RHL group had a higher percentage of impaired perceptual continua (16%) than both normal controls (4%) and the LHL group (9%). If a stoke subject had an impaired threshold on one side of the body, they were ~5 times more likely to have an impaired threshold on the other side of the body. This result was more consistent and even exaggerated (~8 times more likely) in the small percentage of normal control subjects who demonstrated "impaired" sensory thresholds. Lesion volume was positively correlated with stroke severity and sensory threshold impairment, and it was negatively correlated with functional independence.

CONCLUSIONS

When subjects, have difficulty detecting and discriminating sensory experiences, they tend to do so on both sides of the body. Unilateral right hemisphere stroke appeared to increase the relative frequency of altered thresholds occurring on the contralesional side of the body even though they made errors on both sides.

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.

The basal ganglia corticostriatal loops and conditional learning

Brief maneuvering of the literature as to the various roles attributed to the basal ganglia corticostriatal circuits in a variety of cognitive processes such as working memory, selective attention, and category learning has inspired us to investigate the interplay of the two major basal ganglia open-recurrent loops, namely, visual and executive loops specifically the possible involvement of their overlap in conditional learning. We propose that the interaction of the visual and executive loops reflected through their cortical overlap in the dorsolateral prefrontal cortex (DL-PFC), lateral orbitofrontal cortex (LO-PFC), and presupplementary motor area (SMA) plays an instrumental role preliminary first in forming associations between a series of correct responses following similar stimuli and then in shifting, abstracting, and generalizing conditioned responses. The premotor and supplementary motor areas have been shown essential to producing a sequence of movements while the SMA is engaged in monitoring complex movements. In light of the recent studies, we will suggest that the interaction of visual and executive loops could strengthen or weaken learned associations following different reward values. Furthermore, we speculate that the overlap of the visual and executive loops can account for the switching between the associative vs. rule-based category learning systems.

A role for the medial temporal lobes in category learning

Despite much research, the role of the medial temporal lobes (MTL) in category learning is unclear. Two unstructured categorization experiments explored conditions that might recruit MTL category learning and memory systems—namely, whether the stimulus display includes one or two stimuli, and whether category membership depends on configural properties of the stimulus features. The results supported three conclusions. First, in agreement with prior research, learning with single stimulus displays depended on striatal-mediated procedural learning. Second, and most important, learning with pair displays was mediated by MTL declarative memory systems. Third, the use of stimuli in which category membership depends on configural properties of the stimulus features made MTL learning slightly more likely. Overall, the results suggested that the MTL are most likely to mediate learning when the participant must decide which of two configural stimuli belongs to a selected category.

Spatiotemporal dissociation of fMRI activity in the caudate nucleus underlies human de novo motor skill learning

Numerous real-world motor skills require learning arbitrary relationships between actions and their consequences from scratch. However, little is understood about the neural signatures of de novo motor learning and associated individual variability. In a longitudinal fMRI experiment, where participants learned to control a cursor by moving fingers, we found a gradual transition of performance-related activity from the head to tail of the caudate nucleus. This finding reflects the flexible and stable reward representations in the head and tail, respectively. Additionally, intrinsic cortico-caudate connectivity predicted better learners with weaker head–prefrontal and stronger tail–sensorimotor interactions. The present study provides unprecedented insight into de novo motor learning, which may contribute to the understanding of motor-related disorders, and infant learning.

Motor skill learning involves a complex process of generating novel movement patterns guided by evaluative feedback, such as a reward. Previous literature has suggested anteroposteriorly separated circuits in the striatum to be implicated in early goal-directed and later automatic stages of motor skill learning, respectively. However, the involvement of these circuits has not been well elucidated in human de novomotor skill learning, which requires learning arbitrary action–outcome associations and value-based action selection. To investigate this issue, we conducted a human functional MRI (fMRI) experiment in which participants learned to control a computer cursor by manipulating their right fingers. We discovered a double dissociation of fMRI activity in the anterior and posterior caudate nucleus, which was associated with performance in the early and late learning stages. Moreover, cognitive and sensorimotor cortico-caudate interactions predicted individual learning performance. Our results suggest parallel cortico-caudate networks operating in different stages of human de novomotor skill learning.

Neural processing of olfactory-related words in subjects with congenital and acquired olfactory dysfunction

Olfactory loss can be acquired (patients with a history of olfactory experiences), or inborn (patients without olfactory experiences/life-long inability to smell). Inborn olfactory loss, or congenital anosmia (CA), is relatively rare and there is a knowledge gap regarding the compensatory neural mechanisms involved in this condition. The study aimed to investigate the top-down olfactory processing in patients with CA or idiopathic acquired anosmia (IA) in comparison to normosmia controls (NC) during expectancy and reading of odor-associated words. Functional magnetic resonance imaging was used to assess brain activations in 14 patients with CA, 8 patients with IA, and 16 NC healthy participants during an expectancy and reading task. Words with strong olfactory associations (OW) (e.g. “banana”) or with little or no olfactory associations (CW) (e.g. “chair”) were used as stimuli and were presented with a block design Analyses were conducted to explore the brain activation in response to OW expectancy or OW reading between groups (CW as baseline). During the expectancy condition of OW, IA and NC groups showed stronger activation in posterior OFC extending to right insula, caudate region and frontal medial OFC respectively. Whereas during the reading condition of OW, CA patients showed stronger activation in posterior OFC extending to the insula. Increased activation of higher-order brain regions related to multisensory integration among CA patients suggests a compensatory mechanism for processing semantic olfactory cues.

Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI

Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.

Monkeys (Macaca mulatta) learn two-choice discriminations under displaced reinforcement

To explain animal learning, researchers invoke a dominant associative construct. In contrast, researchers freely acknowledge humans' explicit-declarative learning capacity. Here, we stretched animals' learning performance toward the explicit pole of cognition. We tested four macaques (Macaca mulatta) in new discrimination-learning paradigms. Monkeys learned a series of two-choice discrimination tasks. But immediate reinforcement was denied. Instead, reinforcement was lagged-monkeys received feedback for trial N only after seeing and responding to the N + 1-trial stimulus. Theory suggests that lagged reinforcement will eliminate a dominant form of implicit discrimination learning. Yet monkeys still learned successfully. Thus, monkeys may have alternative learning algorithms usable when reinforcement is displaced and reinforcement learning undermined. This learning may, as in humans, take a more explicit form. This and related methods that disable associative learning-fostering a possible transition to explicit cognition-could have empirical utility and theoretical significance within comparative psychology. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Social anxiety and age are associated with neural response to social evaluation during adolescence

Adolescence is a sensitive period for the development of adaptive social behaviors and social anxiety, possibly due to aspects of brain development. However, research is needed to examine interactions among age, social anxiety, and social dynamics previously shown to influence neural responding. The current functional magnetic resonance imaging (fMRI) study examines brain function in 8-18 year-olds with varying levels of social anxiety. Interactions are examined among age, social anxiety, and two key task factors: valence and predictability of social interactions. Results demonstrate age, social anxiety severity, and each of the two key task-based factors interact to predict neural response in the caudate, middle and superior temporal gyri. In particular, among adolescents less-than 13 years of age, higher social anxiety predicted greater responding to unpredictable negative evaluations. However, in this same age group, the opposite pattern emerged during receipt of unpredictable positive evaluations, with less neural response in more anxious youth. Adolescents aged 13 and older overall showed less robust effects. We discuss these findings in terms of age- and anxiety-related differences in socioemotional processing.

Procedural Learning Improves Cognition in Multiple Sclerosis

Background:

Multiple sclerosis (MS) is considered a neurodegenerative disease and an inflammatory demyelinating neuropathology in young population. Procedural memory has been poorly investigated in MS.

Objective:

We assessed whether the MS group was able to develop a motor-cognitive skill, using a procedural task (PLSC) developed in our laboratory, applying a manual and serial reaction time (RT) paradigm to semantic categorization.

Methods:

We evaluated 26 MS patients and 26 socio-demographic matched control participants using the PLSC task.

Results:

Using non-parametric statistical analyses, we observed a significant improvement of semantic categorization RTs with practice (p = 0.002), even with new verbal material to categorize in MS patients (p = 0.006), despite their motor and executive moderate deficits. This same profile of semantic procedural learning in MS was observed in previous studies carried out with Alzheimer’s and Parkinson’s diseases. Moreover, the visual-motor RTs remained stable or slightly improved over the five blocks in both groups, as well as in the AD groups of previous studies. The MS group showed longer visual-motor reaction times than those of the control group (p < 0.042), except in motor initiation aspect (p = 0.064). Both groups showed no significant differences for any type of error. Additionally, disability level and cognitive performances were not associated with the ratio of semantic procedural learning.

Conclusion:

The present results support the notion that MS patients may be capable of acquiring semantic skill, despite their motor disabilities and executive troubles. This work also addresses the possibilities to improve motor-cognitive skill RTs in neurodegenerative diseases.

Left Inferior Frontal Gyrus Integrates Multisensory Information in Category Learning

Humans are able to categorize things they encounter in the world (e.g., a cat) by integrating multisensory information from the auditory and visual modalities with ease and speed. However, how the brain learns multisensory categories remains elusive. The present study used functional magnetic resonance imaging to investigate, for the first time, the neural mechanisms underpinning multisensory information-integration (II) category learning. A sensory-modality-general network, including the left insula, right inferior frontal gyrus (IFG), supplementary motor area, left precentral gyrus, bilateral parietal cortex, and right caudate and globus pallidus, was recruited for II categorization, regardless of whether the information came from a single modality or from multiple modalities. Putamen activity was higher in correct categorization than incorrect categorization. Critically, the left IFG and left body and tail of the caudate were activated in multisensory II categorization but not in unisensory II categorization, which suggests this network plays a specific role in integrating multisensory information during category learning. The present results extend our understanding of the role of the left IFG in multisensory processing from the linguistic domain to a broader role in audiovisual learning.

Dynamical Role of Pivotal Brain Regions in Parkinson Symptomatology Uncovered with Deep Learning

BACKGROUND

The release of a broad, longitudinal anatomical dataset by the Parkinson's Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology.

OBJECTIVES

The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint.

METHODS

We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features.

RESULTS

The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus.

CONCLUSIONS

While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated.

Abnormal Baseline Brain Activity in Neuromyelitis Optica Patients Without Brain Lesion Detected by Resting-State Functional Magnetic Resonance Imaging

OBJECTIVE

To investigate the baseline brain activity in neuromyelitis optica patients without brain lesion using the regional amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) as indexes.

MATERIALS AND METHODS

Forty-two patients of NMO with normal performance in conventional MRI and 42 healthy controls, matched in gender and age, were enrolled in this study. Resting-state functional magnetic resonance imaging (rs-fMRI) data acquired using the rs-fMRI Data Analysis Toolkit. The relationships between expanded disability states scale (EDSS) scores, abnormal baseline brain activity and disease duration were explored.

RESULTS

The left inferior temporal, left cerebellum_4_5, bilateral superior temporal pole, left caudate, right superior temporal, left middle frontal and left superior occipital showed significantly increased ALFF in the NMO. Regions of abnormal fALFF were similar to those of ALFF except that increased fALFF were also indicated in the right cerebellum crus2, right hippocampus, left parahippocampal gyrus and left supplementary motor area. Furthermore, a significant correlation between EDSS scores and ALFF/fALFF was noted in the left inferior temporal gyrus.

CONCLUSION

Results confirmed the disturbances in NMO-related neural networks, which probably be related to spinal cord damage.

The interactions of dopamine and oxidative damage in the striatum of patients with neurodegenerative diseases

The striatum with a number of dopamine containing neurons, receiving projections from the substantia nigra and ventral tegmental area; plays a critical role in neurodegenerative diseases of motor and memory function. Additionally, oxidative damage to nucleic acid may be vital in the development of age-associated neurodegeneration. The metabolism of dopamine is recognized as one of the sources of reactive oxygen species through the Fenton mechanism. The proposed interactions of oxidative insults and dopamine in the striatum during the progression of diseases are the hypotheses of most interest to our study. This study investigated the possibility of significant interactions between these molecules that are involved in the late-stage of Alzheimer's disease (AD), Parkinson disease (PD), Parkinson disease dementia, dementia with Lewy bodies, and controls using ELISA assays, autoradiography, and mRNA in situ hybridization assay. Interestingly, lower DNA/RNA oxidative adducts levels in the caudate and putamen of diseased brains were observed with the exception of an increased DNA oxidative product in the caudate of AD brains. Similar changes were found for dopamine concentration and vesicular monoamine transporter 2 densities. We also found that downstream pre-synaptic dopamine D1 Receptor binding correlated with dopamine loss in Lewy body disease groups, and RNA damage and β-site APP cleaving enzyme 1 in the caudate of AD. This is the first demonstration of region-specific alterations of DNA/RNA oxidative damage which cannot be viewed in isolation, but rather in connection with the interrelationship between different neuronal events; chiefly DNA oxidative adducts and density of vesicular monoamine transporter 2 densities in AD and PD patients.

Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning

Humans naturally group the world into coherent categories defined by membership rules. Rules can be learned implicitly by building stimulus-response associations using reinforcement learning or by using explicit reasoning. We tested if the striatum, in which activation reliably scales with reward prediction error, would track prediction errors in a task that required explicit rule generation. Using functional magnetic resonance imaging during a categorization task, we show that striatal responses to feedback scale with a "surprise" signal derived from a Bayesian rule-learning model and are inconsistent with RL prediction error. We also find that striatum and caudal inferior frontal sulcus (cIFS) are involved in updating the likelihood of discriminative rules. We conclude that the striatum, in cooperation with the cIFS, is involved in updating the values assigned to categorization rules when people learn using explicit reasoning.

A Masked Aversive Odor Cannot Be Discriminated From the Masking Odor but Can Be Identified Through Odor Quality Ratings and Neural Activation Patterns

Odor masking is a very prominent problem in our daily routines, mainly concerning unpleasant sweat or toilet odors. In the current study we explored the effectiveness of odor masking both on a behavioral and neuronal level. By definition, participants cannot differentiate a fully masked unpleasant odor from the pleasant pure odor used as a masking agent on a behavioral level. We hypothesized, however, that one can still discriminate between a fully masked odor mixture and the pure masking odor on a neuronal level and that, using a reinforcing feedback paradigm, participants could be trained to perceive this difference. A pleasant, lemon-like odor (citral) and a mixture of citral and minor amounts of an unpleasant, goat-like odor (caproic acid) were presented to participants repeatedly using a computer-controlled olfactometer and participants had to decide whether two presented stimuli were the same or different. Accuracy of this task was incentivized with a possible monetary reward. Functional imaging was used throughout the task to investigate central processing of the two stimuli. The participants rated both stimuli as isopleasant and isointense, indicating that the unpleasant odor was fully masked by the pleasant odor. The isolated caproic acid component of the mixture was rated less pleasant than the pleasant odor in a prior experimental session. Although the masked and pure stimuli were not discriminated in the forced-choice task, quality ratings on a dimensional scale differed. Further, we observed an increased activation of the insula and ventral striatum/putamen for the pure in contrast to the fully masked odor, hence revealing a difference in neuronal processing. Our hypothesis that perceptual discrimination and neuronal processing can be enhanced using a reinforcing feedback paradigm is not supported by our data.

Spectral slowing is associated with working memory performance in children born very preterm

Children born very preterm (VPT) often demonstrate selective difficulties in working memory (WM), which may underlie academic difficulties observed in this population. Despite this, few studies have investigated the functional networks underlying WM in young children born VPT, a period when cognitive deficits become apparent. Using magnetoencephalography, we examined the networks underlying the maintenance of visual information in 6-year-old VPT (n = 15) and full-term (FT; n = 20) children. Although task performance was similar, VPT children engaged different oscillatory mechanisms during WM maintenance. Within the FT group, we observed higher mean whole-brain connectivity in the alpha-band during the retention (i.e. maintenance) interval associated with correct compared to incorrect responses. VPT children showed reduced whole-brain alpha synchrony, and a different network organization with fewer connections. In the theta-band, VPT children demonstrated a slight increase in whole-brain connectivity during WM maintenance, and engaged similar network hubs as FT children in the alpha-band, including the left dorsolateral prefrontal cortex and superior temporal gyrus. These findings suggest that VPT children rely on the theta-band to support similar task performance. Altered oscillatory mechanisms may reflect a less mature pattern of functional recruitment underlying WM in VPT children, which may affect the processing in complex ecological situations.

The local properties of bold signal fluctuations at rest monitor inhibitory control training in adolescents

Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. Short-term IC training improves IC abilities in children and adults. Surprisingly, few studies have investigated the IC training effect during adolescence, a developmental period characterized by high neuroplasticity and the protracted development of IC abilities. We investigated behavioural and functional brain changes induced by a 5-week computerized and adaptive IC training in adolescents. We focused on the IC training effects on the local properties of functional Magnetic Resonance Imaging (fMRI) signal fluctuations at rest (i.e., Regional Homogeneity [ReHo] and fractional Amplitude of Low Frequency Fluctuations [fALFF]). Sixty adolescents were randomly assigned to either an IC or an active control training group. In the pre- and post-training sessions, cognitive ('Cool') and emotional ('Hot') IC abilities were assessed using the Colour-Word and Emotional Stroop tasks. We found that ReHo and fALFF signals in IC areas (IFG, ACC, Striatum) were associated with IC efficiency at baseline. This association was different for Cool and Hot IC. Analyses also revealed that ReHo and fALFF signals were sensitive markers to detect and monitor changes after IC training, while behavioural data did not, suggesting that brain functional changes at rest precede behavioural changes following training.

Building Memory Representations for Exemplar-Based Judgment: A Role for Ventral Precuneus

The brain networks underlying human multiple-cue judgment, the judgment of a continuous criterion based on multiple cues, have been examined in a few recent studies, and the ventral precuneus has been found to be a key region. Specifically, activation differences in ventral precuneus (as measured with functional magnetic resonance imaging, fMRI) has been linked to an exemplar-based judgment process, where judgments are based on memory for previous similar cases. Ventral precuneus is implicated in various episodic memory processes, notably such that increased activity during learning in this region as well as in the ventromedial prefrontal cortex (vmPFC) and the medial temporal lobes (MTL) have been linked to retrieval success. The present study used fMRI during a multiple-cue judgment task to gain novel neurocognitive evidence informative for the link between learning-related activity changes in ventral precuneus and exemplar-based judgment. Participants (N = 27) spontaneously learned to make judgments during fMRI, in a multiple-cue judgment task specifically designed to induce exemplar-based processing. Contrasting brain activity during late learning to early learning revealed higher activity in ventral precuneus, the bilateral MTL, and the vmPFC. Activity in the ventral precuneus and the vmPFC was found to parametrically increase between each judgment event, and activity levels in the ventral precuneus predicted performance after learning. These results are interpreted such that the ventral precuneus supports the aspects of exemplar-based processes that are related to episodic memory, tentatively by building, storing, and being implicated in retrieving memory representations for judgment.