Linking dynamic patterns of neural activity in orbitofrontal cortex with decision making

In relentless pursuit of the white whale: A role for the ventral midline thalamus in behavioral flexibility and adaption?

The reuniens (Re) nucleus is located in the ventral midline thalamus. It has fostered increasing interest, not only for its participation in a variety of cognitive functions (e.g., spatial working memory, systemic consolidation, reconsolidation, extinction of fear or generalization), but also for its neuroanatomical positioning as a bidirectional relay between the prefrontal cortex (PFC) and the hippocampus (HIP). In this review we compile and discuss recent studies having tackled a possible implication of the Re nucleus in behavioral flexibility, a major PFC-dependent executive function controlling goal-directed behaviors. Experiments considered explored a possible role for the Re nucleus in perseveration, reversal learning, fear extinction, and set-shifting. They point to a contribution of this nucleus to behavioral flexibility, mainly by its connections with the PFC, but possibly also by those with the hippocampus, and even with the amygdala, at least for fear-related behavior. As such, the Re nucleus could be a crucial crossroad supporting a PFC-orchestrated ability to cope with new, potentially unpredictable environmental contingencies, and thus behavioral flexibility and adaption.

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making

Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.

Altered neural correlates of optimal decision-making in individuals with depressive status

Making optimal decisions by computing risk and benefit is necessary for humans. However, whether individuals with depressive status could utilize the optimal strategy to guide decision and its neural correlates remain unclear. The current study explored these issues by combining a decision task and high temporal-resolution electroencephalogram (EEG). The decision task involved an eight-box trial in which participants successively decided whether to open a box containing a potential reward or punishment, deciding to stop guaranteed they would retain the rewards already accumulated. Theoretically, the optimal strategy in the task was to stop at the fourth box, which had the largest expected value. We found that individuals with depressive status stopped fewer trials at the fourth box, relative to healthy controls, indicating their impaired optimal strategy during decision-making. Moreover, compared to healthy controls, individuals with depressive status showed weaker P2 amplitude and weaker beta-band oscillation at the frontocentral scalp when deciding whether to open the fourth box. Additionally, for healthy controls but not for individuals with depressive status, the P2 amplitude fully mediated the relationship between participants' degree of expected benefit (as reflected by the recreational risk-taking scale) and the frequency of trials stopped at the fourth box. Overall, this study revealed that the P2 amplitude and beta-band oscillation might explain the altered optimal decision-making in individuals with depressive status.

Amygdala-cortical collaboration in reward learning and decision making

Adaptive reward-related decision making requires accurate prospective consideration of the specific outcome of each option and its current desirability. These mental simulations are informed by stored memories of the associative relationships that exist within an environment. In this review, I discuss recent investigations of the function of circuitry between the basolateral amygdala (BLA) and lateral (lOFC) and medial (mOFC) orbitofrontal cortex in the learning and use of associative reward memories. I draw conclusions from data collected using sophisticated behavioral approaches to diagnose the content of appetitive memory in combination with modern circuit dissection tools. I propose that, via their direct bidirectional connections, the BLA and OFC collaborate to help us encode detailed, outcome-specific, state-dependent reward memories and to use those memories to enable the predictions and inferences that support adaptive decision making. Whereas lOFC→BLA projections mediate the encoding of outcome-specific reward memories, mOFC→BLA projections regulate the ability to use these memories to inform reward pursuit decisions. BLA projections to lOFC and mOFC both contribute to using reward memories to guide decision making. The BLA→lOFC pathway mediates the ability to represent the identity of a specific predicted reward and the BLA→mOFC pathway facilitates understanding of the value of predicted events. Thus, I outline a neuronal circuit architecture for reward learning and decision making and provide new testable hypotheses as well as implications for both adaptive and maladaptive decision making.

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text]) or trials ([Formula: see text]). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

Altered dynamic functional connectivity of insular subregions could predict symptom severity of male patients with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties with social communication and restricted or repetitive patterns of behavior. This disorder was characterized by widespread abnormalities involving distributed brain networks. As one such key network node, the insular cortex has been regarded as a research focus of ASD neuropathology. The insula is a functionally complex brain structure. However, it is not fully clear if dynamic characteristics of resting-state functional magnetic resonance imaging (R-fMRI) signals in insular heterogeneous could be used to depict abnormalities in ASD. To address this question, we investigated dynamic functional connectivity (dFC) of 12 insular subregions. Data were obtained from 44 individuals with ASD and 65 typically developing age-matched controls (TDC). We assessed dFC by sliding-window method and quantified its temporal variability. Multivariable linear regression models were constructed to determine whether dFC support complementary information about symptom severity of individuals with ASD rather than static functional connectivity (sFC). The results showed that individuals with ASD exhibited dFC and sFC alterations in distinct insular subregions. Some brain regions showed only abnormal dFC but not sFC with insular subregions. These abnormal dFC could significantly predict the symptom severity of individuals with ASD. Our findings might advance our knowledge about the potential of insular heterogeneity and dynamic characteristics in understanding the neuropathology mechanism of ASD and in developing neuroimaging biomarkers for clinical applications.

Dissociating Value-Based Neurocomputation from Subsequent Selection-Related Activations in Human Decision-Making

Human decision-making requires the brain to fulfill neural computation of benefit and risk and therewith a selection between options. It remains unclear how value-based neural computation and subsequent brain activity evolve to achieve a final decision and which process is modulated by irrational factors. We adopted a sequential risk-taking task that asked participants to successively decide whether to open a box with potential reward/punishment in an eight-box trial, or not to open. With time-resolved multivariate pattern analyses, we decoded electroencephalography and magnetoencephalography responses to two successive low- and high-risk boxes before open-box action. Referencing the specificity of decoding-accuracy peak to a first-stage processing completion, we set it as the demarcation and dissociated the neural time course of decision-making into valuation and selection stages. The behavioral hierarchical drift diffusion modeling confirmed different information processing in two stages, that is, the valuation stage was related to the drift rate of evidence accumulation, while the selection stage was related to the nondecision time spent in response-producing. We further observed that medial orbitofrontal cortex participated in the valuation stage, while superior frontal gyrus engaged in the selection stage of irrational open-box decisions. Afterward, we revealed that irrational factors influenced decision-making through the selection stage rather than the valuation stage.

Neural Representation of Costs and Rewards in Decision Making

Decision making is crucial for animal survival because the choices they make based on their current situation could influence their future rewards and could have potential costs. This review summarises recent developments in decision making, discusses how rewards and costs could be encoded in the brain, and how different options are compared such that the most optimal one is chosen. The reward and cost are mainly encoded by the forebrain structures (e.g., anterior cingulate cortex, orbitofrontal cortex), and their value is updated through learning. The recent development on dopamine and the lateral habenula's role in reporting prediction errors and instructing learning will be emphasised. The importance of dopamine in powering the choice and accounting for the internal state will also be discussed. While the orbitofrontal cortex is the place where the state values are stored, the anterior cingulate cortex is more important when the environment is volatile. All of these structures compare different attributes of the task simultaneously, and the local competition of different neuronal networks allows for the selection of the most appropriate one. Therefore, the total value of the task is not encoded as a scalar quantity in the brain but, instead, as an emergent phenomenon, arising from the computation at different brain regions.

Effects of perinatal HIV-infection on the cortical thickness and subcortical gray matter volumes in young adulthood

ABSTRACT

Brain atrophy has been observed in perinatally HIV-infected patients (PHIV) despite initiation on combined antiretroviral treatment (cART), but neuroimaging studies are limited. We aimed to evaluate cortical thickness (CT) and subcortical gray matter (GM) volumes of PHIV youths with stable immunovirological situation and with a normal daily performance.A prospective cross-sectional study was conducted. A total of 25 PHIV patients on cART and 25 HIV-negative (HIV-) controls matched by age, sex, level of education, and socioeconomic status underwent a magnetic resonance imaging scan. CAT12 toolbox was used to extract CT values from T1w images using parcellations from Desikan-Killiany atlas (DK40). To measure regional brain volumes, native segmented images were parceled in regions of interest according to the Neuromorphometrics Atlas. Neuropsychological assessment and psychopathological symptoms were documented.Fifty participants were included (60% females, median age 20 years [interquartile range, IQR 19-23], 64% Whites). No differences regarding neuropsychological tests or psychopathological symptoms were found between groups (all P > .05). All participants presented an average performance in the Fluid Intelligence (FI) test (PHIV mean: -0.12, HIV- mean: 0.24), When comparing CT, PHIV-infected patients showed thinner cortices compared with their peers in fusiform gyrus (P = .000, P = .009), lateral-orbitofrontal gyrus (P = .006, P = .0024), and right parsobitalis gyrus (P = .047). Regarding subcortical GM volumes, PHIV patients showed lower right amygdala (P = .014) and left putamen (P = .016) volumes when compared with HIV- controls. Within the PHIV group, higher CD4 count was associated with higher volumes in right putamen (B = 0.00000038, P = .045). Moreover, increased age at cART initiation and lower nadir CD4 count was associated with larger volumes in left accumbens (B = 0.0000046, P = .033; B = -0.00000008, P = .045, respectively).PHIV patients showed thinner cortices of areas in temporal, orbito-frontal and occipital lobes and lower volumes of subcortical GM volumes when compared with the HIV- control group, suggesting cortical and subcortical brain alterations in otherwise neuroasymptomatic patients. Nevertheless, larger and longitudinal studies are required to determine the impact of HIV on brain structure in PHIV patients and to further identify risk and protective factors that could be implicated.

Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task

Cortical activity related to erroneous behavior in discrimination or decision-making tasks is rarely analyzed, yet it can help clarify which computations are essential during a specific task. Here, we use a hidden Markov model (HMM) to perform a trial-by-trial analysis of the ensemble activity of dorsolateral prefrontal cortex (PFdl) neurons of rhesus monkeys performing a distance discrimination task. By segmenting the neural activity into sequences of metastable states, HMM allows us to uncover modulations of the neural dynamics related to internal computations. We find that metastable dynamics slow down during error trials, while state transitions at a pivotal point during the trial take longer in difficult correct trials. Both these phenomena occur during the decision interval, with errors occurring in both easy and difficult trials. Our results provide further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

Alcohol and sweet reward are encoded by distinct meta-ensembles

Cue-reward associations form distinct memories that can drive appetitive behaviors and cravings for both drugs and natural rewards. It is still unclear how such memories are encoded in the brain's reward system. We trained rats to concurrently self-administer either alcohol or a sweet saccharin solution as drug or natural rewards, respectively. Memory recall due to cue exposure reactivated reward-associated functional ensembles in reward-related brain regions, marked by a neural cFos response. While the local ensembles activated by cue presentation for either reward consisted of similar numbers of neurons, using advanced statistical network theory, we found robust reward-specific co-activation patterns across brain regions. Interestingly, the resulting meta-ensemble networks differed by the most influential regions, which in case of saccharin comprised the prefrontal cortex, while for alcohol seeking control shifted to insular cortex with strong involvement of the amygdala. Our results support the view of memory representation as a differential co-activation of local neuronal ensembles. This article is part of the special issue on 'Neurocircuitry Modulating Drug and Alcohol Abuse'.

Anticipatory feelings: Neural correlates and linguistic markers

This review introduces anticipatory feelings (AF) as a new construct related to the process of anticipation and prediction of future events. AF, defined as the state of awareness of physiological and neurocognitive changes that occur within an oganism in the form of a process of adapting to future events, are an important component of anticipation and expectancy. They encompass bodily-related interoceptive and affective components and are influenced by intrapersonal and dispositional factors, such as optimism, hope, pessimism, or worry. In the present review, we consider evidence from animal and human research, including neuroimaging studies, to characterize the brain structures and brain networks involved in AF. The majority of studies reviewed revealed three brain regions involved in future oriented feelings: 1) the insula; 2) the ventromedial prefrontal cortex (vmPFC); and 3) the amygdala. Moreover, these brain regions were confirmed by a meta-analysis, using a platform for large-scale, automated synthesis of fMRI data. Finally, by adopting a neurolinguistic and a big data approach, we illustrate how AF are expressed in language.

Targeted Stimulation of Human Orbitofrontal Networks Disrupts Outcome-Guided Behavior

Outcome-guided behavior requires knowledge about the current value of expected outcomes. Such behavior can be isolated in the reinforcer devaluation task, which assesses the ability to infer the current value of specific rewards after devaluation. Animal lesion studies demonstrate that orbitofrontal cortex (OFC) is necessary for normal behavior in this task, but a causal role for human OFC in outcome-guided behavior has not been established. Here, we used sham-controlled, non-invasive, continuous theta-burst stimulation (cTBS) to temporarily disrupt human OFC network activity by stimulating a site in the lateral prefrontal cortex that is strongly connected to OFC prior to devaluation of food odor rewards. Subjects in the sham group appropriately avoided Pavlovian cues associated with devalued food odors. However, subjects in the stimulation group persistently chose those cues, even though devaluation of food odors themselves was unaffected by cTBS. This behavioral impairment was mirrored in changes in resting-state functional magnetic resonance imaging (rs-fMRI) activity such that subjects in the stimulation group exhibited reduced OFC network connectivity after cTBS, and the magnitude of this reduction was correlated with choices after devaluation. These findings demonstrate the feasibility of indirectly targeting the human OFC with non-invasive cTBS and indicate that OFC is specifically required for inferring the value of expected outcomes.

Organization of afferents to the orbitofrontal cortex in the rat

The prefrontal cortex (PFC) is usually defined as the frontal cortical area receiving a mediodorsal thalamic (MD) innervation. Certain areas in the medial wall of the rat frontal area receive a MD innervation. A second frontal area that is the target of MD projections is located dorsal to the rhinal sulcus and often referred to as the orbitofrontal cortex (OFC). Both the medial PFC and OFC are comprised of a large number of cytoarchitectonic regions. We assessed the afferent innervation of the different areas of the OFC, with a focus on projections arising from the mediodorsal thalamic nucleus, the basolateral nucleus of the amygdala, and the midbrain dopamine neurons. Although there are specific inputs to various OFC areas, a simplified organizational scheme could be defined, with the medial areas of the OFC receiving thalamic inputs, the lateral areas of the OFC being the recipient of amygdala afferents, and a central zone that was the target of midbrain dopamine neurons. Anterograde tracer data were consistent with this organization of afferents, and revealed that the OFC inputs from these three subcortical sites were largely spatially segregated. This spatial segregation suggests that the central portion of the OFC (pregenual agranular insular cortex) is the only OFC region that is a prefrontal cortical area, analogous to the prelimbic cortex in the medial prefrontal cortex. These findings highlight the heterogeneity of the OFC, and suggest possible functional attributes of the three different OFC areas.

Robust Encoding of Spatial Information in Orbitofrontal Cortex and Striatum

Knowing whether core reward regions carry information about the positions of relevant objects is crucial for adjudicating between choice models. One limitation of previous studies, including our own, is that spatial positions can be consistently differentially associated with rewards, and thus position can be confounded with attention, motor plans, or target identity. We circumvented these problems by using a task in which value-and thus choices-was determined solely by a frequently changing rule, which was randomized relative to spatial position on each trial. We presented offers asynchronously, which allowed us to control for reward expectation, spatial attention, and motor plans in our analyses. We find robust encoding of the spatial position of both offers and choices in two core reward regions, orbitofrontal Area 13 and ventral striatum, as well as in dorsal striatum of macaques. The trial-by-trial correlation in noise in encoding of position was associated with variation in choice, an effect known as choice probability correlation, suggesting that the spatial encoding is associated with choice and is not incidental to it. Spatial information and reward information are not carried by separate sets of neurons, although the two forms of information are temporally dissociable. These results highlight the ubiquity of multiplexed information in association cortex and argue against the idea that these ostensible reward regions serve as part of a pure value domain.