Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD. METHODS: Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors. RESULTS: LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects. CONCLUSIONS: We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

The tail of the caudate is sensitive to both gain and loss feedback during information integration categorization.

Although most category learning studies use feedback for training, little attention has been paid to how individuals utilize feedback implemented as gains or losses during categorization. We compared skilled categorization under three different conditions: Gain (earn points for correct answers), Gain and Loss (earn points for correct answers and lose points for wrong answers) and Correct or Wrong (accuracy feedback only). We also manipulated difficulty and point value, with near boundary stimuli having the highest number of points to win or lose, and stimuli far from the boundary having the lowest point value. We found that the tail of the caudate was sensitive to feedback condition, with highest activity when both Gain and Loss feedback were present and least activity when only Gain or accuracy feedback was present. We also found that activity across the caudate was affected by distance from the decision bound, with greatest activity for the near boundary high value stimuli, and lowest for far low value stimuli. Overall these results indicate that the tail of the caudate is sensitive not only to positive rewards but also to loss and punishment, consistent with recent animal research finding tail of the caudate activity in aversive learning.

Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder.

Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.

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.

Alpha oscillations encode Bayesian belief updating underlying attentional allocation in dynamic environments.

In a dynamic environment, expectations of the future constantly change based on updated evidence and affect the dynamic allocation of attention. To further investigate the neural mechanisms underlying attentional expectancies, we employed a modified Central Cue Posner Paradigm in which the probability of cues being valid (that is, accurately indicated the upcoming target location) was manipulated. Attentional deployment to the cued location (α), which was governed by precision of predictions on previous trials, was estimated using a hierarchical Bayesian model and was included as a regressor in the analyses of electrophysiological (EEG) data. Our results revealed that before the target appeared, alpha oscillations (8∼13 Hz) for high-predictability cues (88 % valid) were significantly predicted by precision-dependent attention (α). This relationship was not observed under low-predictability conditions (69 % and 50 % valid cues). After the target appeared, precision-dependent attention (α) correlated with alpha band oscillations only in the valid cue condition and not in the invalid condition. Further analysis under conditions of significant attentional modulation by precision suggested a separate effect of cue orientation. These results provide new insights on how trial-by-trial Bayesian belief updating relates to alpha band encoding of environmentally-sensitive allocation of visual spatial attention.

Obsessive-compulsive disorder is characterized by decreased Pavlovian influence on instrumental behavior.

Obsessive-compulsive disorder (OCD) is characterized by uncontrollable repetitive actions thought to rely on abnormalities within fundamental instrumental learning systems. We investigated cognitive and computational mechanisms underlying Pavlovian biases on instrumental behavior in both clinical OCD patients and healthy controls using a Pavlovian-Instrumental Transfer (PIT) task. PIT is typically evidenced by increased responding in the presence of a positive (previously rewarded) Pavlovian cue, and reduced responding in the presence of a negative cue. Thirty OCD patients and thirty-one healthy controls completed the Pavlovian Instrumental Transfer test, which included instrumental training, Pavlovian training for positive, negative and neutral cues, and a PIT phase in which participants performed the instrumental task in the presence of the Pavlovian cues. Modified Rescorla-Wagner models were fitted to trial-by-trial data of participants to estimate underlying computational mechanism and quantify individual differences during training and transfer stages. Bayesian hierarchical methods were used to estimate free parameters and compare the models. Behavioral and computational results indicated a weaker Pavlovian influence on instrumental behavior in OCD patients than in HC, especially for negative Pavlovian cues. Our results contrast with the increased PIT effects reported for another set of disorders characterized by compulsivity, substance use disorders, in which PIT is enhanced. A possible reason for the reduced PIT in OCD may be impairment in using the contextual information provided by the cues to appropriately adjust behavior, especially when inhibiting responding when a negative cue is present. This study provides deeper insight into our understanding of deficits in OCD from the perspective of Pavlovian influences on instrumental behavior and may have implications for OCD treatment modalities focused on reducing compulsive behaviors.

The neural substrates of how model-based learning affects risk taking: Functional coupling between right cerebellum and left caudate.

Higher executive control capacity allows people to appropriately evaluate risk and avoid both excessive risk aversion and excessive risk-taking. The neural mechanisms underlying this relationship between executive function and risk taking are still unknown. We used voxel-based morphometry (VBM) analysis combined with resting-state functional connectivity (rs-FC) to evaluate how one component of executive function, model-based learning, relates to risk taking. We measured individuals' use of the model-based learning system with the two-step task, and risk taking with the Balloon Analogue Risk Task. Behavioral results indicated that risk taking was positively correlated with the model-based weighting parameter ω. The VBM results showed a positive association between model-based learning and gray matter volume in the right cerebellum (RCere) and left inferior parietal lobule (LIPL). Functional connectivity results suggested that the coupling between RCere and the left caudate (LCAU) was correlated with both model-based learning and risk taking. Mediation analysis indicated that RCere-LCAU functional connectivity completely mediated the effect of model-based learning on risk taking. These results indicate that learners who favor model-based strategies also engage in more appropriate risky behaviors through interactions between reward-based learning, error-based learning and executive control subserved by a caudate, cerebellar and parietal network.

Imbalance in functional and structural connectivity underlying goal-directed and habitual learning systems in obsessive-compulsive disorder.

An imbalance between the goal-directed and habitual learning systems has been proposed to underlie compulsivity in obsessive-compulsive disorder (OCD). In addition, the overall balance between these systems may be influenced by stress hormones. We examined the multimodal networks underlying these dual learning systems. Both functional and structural measures indicated reduced connectivity within the goal-directed subnetwork (FC: P = 0.042; SC-FN: P = 0.014) and reduced connectivity between the goal-directed and habitual subnetworks (FC: P = 0.014; SC-FA: P = 0.052), but no differences within the habitual subnetwork in patients with OCD compared with controls. Path modeling indicated that anatomical connectivity in the goal-directed subnetwork influenced compulsive symptoms (R2 = 0.41), whereas functional connectivity within the habit subnetwork and between goal-directed and habitual subnetworks influenced obsessive symptoms (R2 = 0.63). In addition, the relationship between anatomical connectivity in the goal-directed subnetwork and compulsion was moderated by the stress hormone ACTH (adrenocorticotropic hormone), such that at low levels of ACTH greater connectivity resulted in lower compulsion, but at high levels of ACTH this relationship was reversed. These results provide new insights into the neural correlates of the imbalance between dual learning systems, and their relationship with symptom dimensions in patients with OCD. It may further support the reconceptualization of OCD as "compulsive-obsessive disorder," with a greater focus on the transdiagnostic dimension of compulsivity.

Abnormal brain functional network dynamics in obsessive-compulsive disorder patients and their unaffected first-degree relatives.

We utilized dynamic functional network connectivity (dFNC) analysis to compare participants with obsessive-compulsive disorder (OCD) with their unaffected first-degree relative (UFDR) and healthy controls (HC). Resting state fMRI was performed on 46 OCD, 24 UFDR, and 49 HCs, along with clinical assessments. dFNC analyses revealed two distinct connectivity states: a less frequent, integrated state characterized by the predominance of between-network connections (State I), and a more frequent, segregated state with strong within-network connections (State II). OCD patients spent more time in State II and less time in State I than HC, as measured by fractional windows and mean dwell time. Time in each state for the UFDR were intermediate between OCD patients and HC. Within the OCD group, fractional windows of time spent in State I was positively correlated with OCD symptoms (as measured by the obsessive compulsive inventory-revised [OCI-R], r = .343, p<.05, FDR correction) and time in State II was negatively correlated with symptoms (r = -.343, p<.05, FDR correction). Within each state we also examined connectivity within and between established intrinsic connectivity networks, and found that UFDR were similar to the OCD group in State I, but more similar to the HC groups in State II. The similarities between OCD and UFDR groups in temporal properties and State I connectivity indicate that these features may reflect the endophenotype for OCD. These results indicate that the temporal dynamics of functional connectivity could be a useful biomarker to identify those at risk.

The neural mechanism of spatial-positional association in working memory: A fMRI study.

In the last decade, research has reported that items at the beginning of a memorized sequence are responded to faster with the left hand, whereas items at the end are responded to faster with the right hand. This Spatial-Positional Associations of Response Codes effect has been extensively studied using behavioral methods. However, the neural networks underlying it remain unclear. We found using functional magnetic resonance imaging (fMRI) that the dorsal attention network was involved in spatial-positional associations, in particular a region of the right superior frontal cortex / pre-supplementary motor area (pre-SMA), within which neural activity correlated with behavioral measures of the strength of spatial-positional associations. Psychophysiological interaction (PPI) analysis revealed functional connectivity between this area and other regions of the dorsal attentional network including the SMA, and with the hippocampal-retrosplenial network. In contrast, explicit processing of serial order independent of spatial-positional associations was related to activity in the inferior parietal cortex. Our results provide new insight into positional coding theories of working memory, including the mental whiteboard hypothesis. They suggest that the behavioral effects of positional coding (congruency between hand and ordinal position within the list) are mediated through spatial and motor control maps in the dorsal attentional system.

Brain Mechanisms of Concept Learning.

Concept learning, the ability to extract commonalities and highlight distinctions across a set of related experiences to build organized knowledge, is a critical aspect of cognition. Previous reviews have focused on concept learning research as a means for dissociating multiple brain systems. The current review surveys recent work that uses novel analytical approaches, including the combination of computational modeling with neural measures, focused on testing theories of specific computations and representations that contribute to concept learning. We discuss in detail the roles of the hippocampus, ventromedial prefrontal, lateral prefrontal, and lateral parietal cortices, and how their engagement is modulated by the coherence of experiences and the current learning goals. We conclude that the interaction of multiple brain systems relating to learning, memory, attention, perception, and reward support a flexible concept-learning mechanism that adapts to a range of category structures and incorporates motivational states, making concept learning a fruitful research domain for understanding the neural dynamics underlying complex behaviors.

Impairment in the goal-directed corticostriatal learning system as a biomarker for obsessive-compulsive disorder.

BACKGROUND: Compulsive behaviors in obsessive-compulsive disorder (OCD) have been related to impairment within the associative cortical-striatal system connecting the caudate and prefrontal cortex that underlies consciously-controlled goal-directed learning and behavior. However, little is known whether this impairment may serve as a biomarker for vulnerability to OCD. METHODS: Using resting-state functional magnetic resonance imaging (fMRI), we employed Granger causality analysis (GCA) to measure effective connectivity (EC) in previously validated striatal sub-regions, including the caudate, putamen, and the nucleus accumbens, in 35 OCD patients, 35 unaffected first-degree relatives and 35 matched healthy controls. RESULTS: Both OCD patients and their first-degree relatives showed greater EC than controls between the left caudate and the orbital frontal cortex (OFC). Both OCD patients and their first-degree relatives showed lower EC than controls between the left caudate and lateral prefrontal cortex. These results are consistent with findings from task-related fMRI studies which found impairment in the goal-directed system in OCD patients. CONCLUSIONS: The same changes in EC were present in both OCD patients and their unaffected first-degree relatives suggest that impairment in the goal-directed learning system may be a biomarker for OCD.

The assessment dimension of regulatory mode mediates the relation between frontoparietal connectivity and risk-taking: Evidence from voxel-base morphometry and functional connectivity analysis.

We used voxel-based morphometry and resting-state functional magnetic resonance imaging (rs-fMRI) to investigate whether the regulatory mode orientation of assessment (the tendency of each individual to self-regulate by critically evaluating alternatives) interacts with neural systems underlying risk-taking. Across a sample of 112 participants, propensity for risk-taking (measured using the Wheel of Fortune task) was negatively correlated with assessment orientation, such that a greater tendency to critically evaluate alternatives was associated with a lower tendency for risk-taking. VBM revealed a negative correlation between assessment orientation and right inferior parietal lobe (RIPL) gray matter volume. Resting-state functional connectivity (rs-FC) between this same RIPL region and the left inferior frontal gyrus (LIFG) was positively correlated with assessment orientation in an independent sample of 41 participants. Most importantly, based on the rs-FC results, a mediation analysis indicated that assessment orientation played a completely mediating role in the relation between the functional connectivity of RIPL-LIFG and risk-taking. These results suggest that assessment orientation may affect risk-taking via the RIPL and its connectivity with LIFG. On the whole, the present study yields the insights into how the assessment dimension of regulatory mode affects risk-taking, and provides a novel account of the neural substrate of this relationship.

The benefit of interleaved presentation in category learning is independent of working memory.

We tested whether working memory (WM) resources were necessary for the interleaved presentation benefit over blocked presentation in category learning. We examined category learning in the Kornell and Bjork [2008. Learning concepts and categories: Is spacing the "enemy of induction"? Psychological Science, 19(6), 585] artistic style task while participants performed the numerical Stroop task as a dual task in order to interfere with WM maintenance and WM dependent executive functions. In addition, we evaluated whether individual differences in WM capacity (WMC), assessed via complex span tasks, would affect learning. The results revealed a superiority for interleaved presentation in both single-task and dual-task conditions, as well as superior performance for participants with relatively high WMC. Importantly, there was no interaction between the presence of the dual task and interleaving, or WMC and interleaving, indicating that the benefits of interleaving are independent of WM. We also probed participants' metacognitive judgments about whether blocking or interleaving was superior for learning, and found that most participants reported blocking was more effective, contrary to the reality that interleaving led to the best performance. These results support theories of the interleaving effect that are independent of working memory resources and pose a challenge to theories that rely on working memory mediated comparisons of items across trials.

Rhythmic auditory cues shape neural network recruitment in Parkinson's disease during repetitive motor behavior.

It is well established clinically that rhythmic auditory cues can improve gait and other motor behaviors in Parkinson's disease (PD) and other disorders. However, the neural systems underlying this therapeutic effect are largely unknown. To investigate this question we scanned people with PD and age-matched healthy controls using functional magnetic resonance imaging (fMRI). All subjects performed a rhythmic motor behavior (right hand finger tapping) with and without simultaneous auditory rhythmic cues at two different speeds (1 and 4 Hz). We used spatial independent component analysis (ICA) and regression to identify task-related functional connectivity networks and assessed differences between groups in intra- and inter-network connectivity. Overall, the control group showed greater intra-network connectivity in perceptual and motor related networks during motor tapping both with and without rhythmic cues. The PD group showed greater inter-network connectivity between the auditory network and the executive control network, and between the executive control network and the motor/cerebellar network associated with the motor task performance. We interpret our results as indicating that the temporal rhythmic auditory information may assist compensatory mechanisms through network-level effects, reflected in increased interaction between auditory and executive networks that in turn modulate activity in cortico-cerebellar networks.

Perceptual and categorical processing and representation in color categorization.

We used fMRI to dissociate decisional and perceptual functions in color categorization. Participants viewed sequences of colored squares which varied in perceptual distance (0, 1 or 2 hue steps) and color (green, blue) and then judged whether one or two colors were present. Occipital, caudate, and anterior insula regions were active when more than one hue was presented, indicating a role in perceptual processing and attentional monitoring. Dorsolateral prefrontal cortex showed greater activity when two colors were present than a single color, indicating a role in coding color category. Cognitive control regions of the intraparietal sulcus and presupplementary motor area were sensitive to the interaction of decision and distance in perceptual space, indicating a role in combining these functions during decision making. These results support theories that colors are represented categorically at high levels of the cognitive hierarchy, and that visual cortex is sensitive to hue rather than color category.

Occipitotemporal Category Representations Are Sensitive to Abstract Category Boundaries Defined by Generalization Demands.

Categorization involves organizing perceptual information so as to maximize differences along dimensions that predict class membership while minimizing differences along dimensions that do not. In the current experiment, we investigated how neural representations reflecting learned category structure vary according to generalization demands. We asked male and female human participants to switch between two rules when determining whether stimuli should be considered members of a single known category. When categorizing according to the "strict" rule, participants were required to limit generalization to make fine-grained distinctions between stimuli and the category prototype. When categorizing according to the "lax" rule, participants were required to generalize category knowledge to highly atypical category members. As expected, frontoparietal regions were primarily sensitive to decisional demands (i.e., the distance of each stimulus from the active category boundary), whereas occipitotemporal representations were primarily sensitive to stimulus typicality (i.e., the similarity between each exemplar and the category prototype). Interestingly, occipitotemporal representations of stimulus typicality differed between rules. While decoding models were able to predict unseen data when trained and tested on the same rule, they were unable to do so when trained and tested on different rules. We additionally found that the discriminability of the multivariate signal negatively covaried with distance from the active category boundary. Thus, whereas many accounts of occipitotemporal cortex emphasize its important role in transforming visual information to accentuate learned category structure, our results highlight the flexible nature of these representations with regards to transient decisional demands.SIGNIFICANCE STATEMENT Occipitotemporal representations are known to reflect category structure and are often assumed to be largely invariant with regards to transient decisional demands. We found that representations of equivalent stimuli differed between strict and lax generalization rules, and that the discriminability of these representations increased as distance from abstract category boundaries decreased. Our results therefore indicate that occipitotemporal representations are flexibly modulated by abstract decisional factors.

Category learning in the brain.

The ability to group items and events into functional categories is a fundamental characteristic of sophisticated thought. It is subserved by plasticity in many neural systems, including neocortical regions (sensory, prefrontal, parietal, and motor cortex), the medial temporal lobe, the basal ganglia, and midbrain dopaminergic systems. These systems interact during category learning. Corticostriatal loops may mediate recursive, bootstrapping interactions between fast reward-gated plasticity in the basal ganglia and slow reward-shaded plasticity in the cortex. This can provide a balance between acquisition of details of experiences and generalization across them. Interactions between the corticostriatal loops can integrate perceptual, response, and feedback-related aspects of the task and mediate the shift from novice to skilled performance. The basal ganglia and medial temporal lobe interact competitively or cooperatively, depending on the demands of the learning task.

Generalization in category learning: the roles of representational and decisional uncertainty.

Effective generalization in a multiple-category situation involves both assessing potential membership in individual categories and resolving conflict between categories while implementing a decision bound. We separated generalization from decision bound implementation using an information integration task in which category exemplars varied over two incommensurable feature dimensions. Human subjects first learned to categorize stimuli within limited training regions, and then, during fMRI scanning, they also categorized transfer stimuli from new regions of perceptual space. Transfer stimuli differed both in distance from the training region prototype and distance from the decision bound, allowing us to independently assess neural systems sensitive to each. Across all stimulus regions, categorization was associated with activity in the extrastriate visual cortex, basal ganglia, and the bilateral intraparietal sulcus. Categorizing stimuli near the decision bound was associated with recruitment of the frontoinsular cortex and medial frontal cortex, regions often associated with conflict and which commonly coactivate within the salience network. Generalization was measured in terms of greater distance from the decision bound and greater distance from the category prototype (average training region stimulus). Distance from the decision bound was associated with activity in the superior parietal lobe, lingual gyri, and anterior hippocampus, whereas distance from the prototype was associated with left intraparietal sulcus activity. The results are interpreted as supporting the existence of different uncertainty resolution mechanisms for uncertainty about category membership (representational uncertainty) and uncertainty about decision bound (decisional uncertainty).

Categorical evidence, confidence, and urgency during probabilistic categorization.

We used a temporally extended categorization task to investigate the neural substrates underlying our ability to integrate information over time and across multiple stimulus features. Using model-based fMRI, we tracked the temporal evolution of two important variables as participants deliberated about impending choices: (1) categorical evidence, and (2) confidence (the total amount of evidence provided by the stimuli, irrespective of the particular category favored). Importantly, in each model, we also included a covariate that allowed us to differentiate signals related to information accumulation from other, evidence-independent functions that increased monotonically with time (such as urgency or cognitive load). We found that somatomotor regions tracked the temporal evolution of categorical evidence, while regions in both medial and lateral prefrontal cortex, inferior parietal cortex, and the striatum tracked decision confidence. As both theory and experimental work suggest that patterns of activity thought to be related to information-accumulation may reflect, in whole or in part, an interaction between sensory evidence and urgency, we additionally investigated whether urgency might modulate the slopes of the two evidence-dependent functions. We found that the slopes of both functions were likely modulated by urgency such that the difference between the high and low evidence states increased as the response deadline loomed.