The default network of the human brain is associated with perceived social isolation.

Humans survive and thrive through social exchange. Yet, social dependency also comes at a cost. Perceived social isolation, or loneliness, affects physical and mental health, cognitive performance, overall life expectancy, and increases vulnerability to Alzheimer's disease-related dementias. Despite severe consequences on behavior and health, the neural basis of loneliness remains elusive. Using the UK Biobank population imaging-genetics cohort (n = ~40,000, aged 40-69 years when recruited, mean age = 54.9), we test for signatures of loneliness in grey matter morphology, intrinsic functional coupling, and fiber tract microstructure. The loneliness-linked neurobiological profiles converge on a collection of brain regions known as the 'default network'. This higher associative network shows more consistent loneliness associations in grey matter volume than other cortical brain networks. Lonely individuals display stronger functional communication in the default network, and greater microstructural integrity of its fornix pathway. The findings fit with the possibility that the up-regulation of these neural circuits supports mentalizing, reminiscence and imagination to fill the social void.

10,000 social brains: Sex differentiation in human brain anatomy.

In human and nonhuman primates, sex differences typically explain much interindividual variability. Male and female behaviors may have played unique roles in the likely coevolution of increasing brain volume and more complex social dynamics. To explore possible divergence in social brain morphology between men and women living in different social environments, we applied probabilistic generative modeling to ~10,000 UK Biobank participants. We observed strong volume effects especially in the limbic system but also in regions of the sensory, intermediate, and higher association networks. Sex-specific brain volume effects in the limbic system were linked to the frequency and intensity of social contact, such as indexed by loneliness, household size, and social support. Across the processing hierarchy of neural networks, different conditions for social interplay may resonate in and be influenced by brain anatomy in sex-dependent ways.

Dissecting the impact of depression on decision-making.

BACKGROUND: Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression. METHODS: Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters. RESULTS: RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced-and decision thresholds were wider-in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did. CONCLUSIONS: Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making-not slowed perception or response execution-caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models.

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

A Computational Cognitive Biomarker for Early-Stage Huntington's Disease.

Huntington's disease (HD) is genetically determined but with variability in symptom onset, leading to uncertainty as to when pharmacological intervention should be initiated. Here we take a computational approach based on neurocognitive phenotyping, computational modeling, and classification, in an effort to provide quantitative predictors of HD before symptom onset. A large sample of subjects-consisting of both pre-manifest individuals carrying the HD mutation (pre-HD), and early symptomatic-as well as healthy controls performed the antisaccade conflict task, which requires executive control and response inhibition. While symptomatic HD subjects differed substantially from controls in behavioral measures [reaction time (RT) and error rates], there was no such clear behavioral differences in pre-HD. RT distributions and error rates were fit with an accumulator-based model which summarizes the computational processes involved and which are related to identified mechanisms in more detailed neural models of prefrontal cortex and basal ganglia. Classification based on fitted model parameters revealed a key parameter related to executive control differentiated pre-HD from controls, whereas the response inhibition parameter declined only after symptom onset. These findings demonstrate the utility of computational approaches for classification and prediction of brain disorders, and provide clues as to the underlying neural mechanisms.

fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning.

What are the neural dynamics of choice processes during reinforcement learning? Two largely separate literatures have examined dynamics of reinforcement learning (RL) as a function of experience but assuming a static choice process, or conversely, the dynamics of choice processes in decision making but based on static decision values. Here we show that human choice processes during RL are well described by a drift diffusion model (DDM) of decision making in which the learned trial-by-trial reward values are sequentially sampled, with a choice made when the value signal crosses a decision threshold. Moreover, simultaneous fMRI and EEG recordings revealed that this decision threshold is not fixed across trials but varies as a function of activity in the subthalamic nucleus (STN) and is further modulated by trial-by-trial measures of decision conflict and activity in the dorsomedial frontal cortex (pre-SMA BOLD and mediofrontal theta in EEG). These findings provide converging multimodal evidence for a model in which decision threshold in reward-based tasks is adjusted as a function of communication from pre-SMA to STN when choices differ subtly in reward values, allowing more time to choose the statistically more rewarding option.

Eye tracking and pupillometry are indicators of dissociable latent decision processes.

Can you predict what people are going to do just by watching them? This is certainly difficult: it would require a clear mapping between observable indicators and unobservable cognitive states. In this report, we demonstrate how this is possible by monitoring eye gaze and pupil dilation, which predict dissociable biases during decision making. We quantified decision making using the drift diffusion model (DDM), which provides an algorithmic account of how evidence accumulation and response caution contribute to decisions through separate latent parameters of drift rate and decision threshold, respectively. We used a hierarchical Bayesian estimation approach to assess the single trial influence of observable physiological signals on these latent DDM parameters. Increased eye gaze dwell time specifically predicted an increased drift rate toward the fixated option, irrespective of the value of the option. In contrast, greater pupil dilation specifically predicted an increase in decision threshold during difficult decisions. These findings suggest that eye tracking and pupillometry reflect the operations of dissociated latent decision processes.

Release the BEESTS: Bayesian Estimation of Ex-Gaussian STop-Signal reaction time distributions.

The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time (RT) task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their response. The primary goal is to estimate the latency of the unobservable stop response (stop signal reaction time or SSRT). Recently, Matzke et al. (2013) have developed a Bayesian parametric approach (BPA) that allows for the estimation of the entire distribution of SSRTs. The BPA assumes that SSRTs are ex-Gaussian distributed and uses Markov chain Monte Carlo sampling to estimate the parameters of the SSRT distribution. Here we present an efficient and user-friendly software implementation of the BPA-BEESTS-that can be applied to individual as well as hierarchical stop-signal data. BEESTS comes with an easy-to-use graphical user interface and provides users with summary statistics of the posterior distribution of the parameters as well various diagnostic tools to assess the quality of the parameter estimates. The software is open source and runs on Windows and OS X operating systems. In sum, BEESTS allows experimental and clinical psychologists to estimate entire distributions of SSRTs and hence facilitates the more rigorous analysis of stop-signal data.

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

A computational model of inhibitory control in frontal cortex and basal ganglia.

Planning and executing volitional actions in the face of conflicting habitual responses is a critical aspect of human behavior. At the core of the interplay between these 2 control systems lies an override mechanism that can suppress the habitual action selection process and allow executive control to take over. Here, we construct a neural circuit model informed by behavioral and electrophysiological data collected on various response inhibition paradigms. This model extends a well-established model of action selection in the basal ganglia by including a frontal executive control network that integrates information about sensory input and task rules to facilitate well-informed decision making via the oculomotor system. Our simulations of the anti-saccade, Simon, and saccade-override tasks ensue in conflict between a prepotent and controlled response that causes the network to pause action selection via projections to the subthalamic nucleus. Our model reproduces key behavioral and electrophysiological patterns and their sensitivity to lesions and pharmacological manipulations. Finally, we show how this network can be extended to include the inferior frontal cortex to simulate key qualitative patterns of global response inhibition demands as required in the stop-signal task.

Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold.

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4-8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5-5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico-basal ganglia communication regulating decision processes.

Altered probabilistic learning and response biases in schizophrenia: behavioral evidence and neurocomputational modeling.

OBJECTIVE: Patients with schizophrenia (SZ) show reinforcement learning impairments related to both the gradual/procedural acquisition of reward contingencies, and the ability to use trial-to-trial feedback to make rapid behavioral adjustments. METHOD: We used neurocomputational modeling to develop plausible mechanistic hypotheses explaining reinforcement learning impairments in individuals with SZ. We tested the model with a novel Go/NoGo learning task in which subjects had to learn to respond or withhold responses when presented with different stimuli associated with different probabilities of gains or losses in points. We analyzed data from 34 patients and 23 matched controls, characterizing positive- and negative-feedback-driven learning in both a training phase and a test phase. RESULTS: Consistent with simulations from a computational model of aberrant dopamine input to the basal ganglia patients, patients with SZ showed an overall increased rate of responding in the training phase, together with reduced response-time acceleration to frequently rewarded stimuli across training blocks, and a reduced relative preference for frequently rewarded training stimuli in the test phase. Patients did not differ from controls on measures of procedural negative-feedback-driven learning, although patients with SZ exhibited deficits in trial-to-trial adjustments to negative feedback, with these measures correlating with negative symptom severity. CONCLUSIONS: These findings support the hypothesis that patients with SZ have a deficit in procedural "Go" learning, linked to abnormalities in DA transmission at D1-type receptors, despite a "Go bias" (increased response rate), potentially related to excessive tonic dopamine. Deficits in trial-to-trial reinforcement learning were limited to a subset of patients with SZ with severe negative symptoms, putatively stemming from prefrontal cortical dysfunction.

Neurocomputational models of motor and cognitive deficits in Parkinson's disease.

We review the contributions of biologically constrained computational models to our understanding of motor and cognitive deficits in Parkinson's disease (PD). The loss of dopaminergic neurons innervating the striatum in PD, and the well-established role of dopamine (DA) in reinforcement learning (RL), enable neural network models of the basal ganglia (BG) to derive concrete and testable predictions. We focus in this review on one simple underlying principle - the notion that reduced DA increases activity and causes long-term potentiation in the indirect pathway of the BG. We show how this theory can provide a unified account of diverse and seemingly unrelated phenomena in PD including progressive motor degeneration as well as cognitive deficits in RL, decision making and working memory. DA replacement therapy and deep brain stimulation can alleviate some aspects of these impairments, but can actually introduce negative effects such as motor dyskinesias and cognitive impulsivity. We discuss these treatment effects in terms of modulation of specific mechanisms within the computational framework. In addition, we review neurocomputational interpretations of increased impulsivity in the face of response conflict in patients with deep-brain-stimulation.

A neurocomputational account of catalepsy sensitization induced by D2 receptor blockade in rats: context dependency, extinction, and renewal.

RATIONALE: Repeated haloperidol treatment in rodents results in a day-to-day intensification of catalepsy (i.e., sensitization). Prior experiments suggest that this sensitization is context-dependent and resistant to extinction training. OBJECTIVES: The aim of this study was to provide a neurobiological mechanistic explanation for these findings. MATERIALS AND METHODS: We use a neurocomputational model of the basal ganglia and simulate two alternative models based on the reward prediction error and novelty hypotheses of dopamine function. We also conducted a behavioral rat experiment to adjudicate between these models. Twenty male Sprague-Dawley rats were challenged with 0.25 mg/kg haloperidol across multiple days and were subsequently tested in either a familiar or novel context. RESULTS: Simulation results show that catalepsy sensitization, and its context dependency, can be explained by "NoGo" learning via simulated D2 receptor antagonism in striatopallidal neurons, leading to increasingly slowed response latencies. The model further exhibits a non-extinguishable component of catalepsy sensitization due to latent NoGo representations that are prevented from being expressed, and therefore from being unlearned, during extinction. In the rat experiment, context dependency effects were not dependent on the novelty of the context, ruling out the novelty model's account of context dependency. CONCLUSIONS: Simulations lend insight into potential complex mechanisms leading to context-dependent catalepsy sensitization, extinction, and renewal.

Material properties determine how force and position signals combine in haptic shape perception.

When integrating estimates from redundant sensory signals, humans seem to weight these estimates according to their reliabilities. In the present study, human observers used active touch to judge the curvature of a shape. The curvature was specified by positional and force signals: When a finger slides across a surface, the finger's position follows the surface geometry (position signal). At the same time, it is exposed to patterns of forces depending on the gradient of the surface (force signal; Robles-de-la-Torre, G., & Hayward, V. (2001). Force can overcome object geometry in the perception of shape through active touch. Nature, 412, 445-448). We show that variations in the surface's material properties (compliance, friction) influence the sensorily available position and force signals, as well as the noise associated with these signals. Along with this, material properties affect the weights given to the position and force signals for curvature judgements. Our findings are consistent with the notion of an observer who weights signal estimates according to their reliabilities. That is, signal weights shifted with the signal noise, which in the present case resulted from active exploration.