Using fMRI to decode true thoughts independent of intention to conceal

Disruptions of salience network during uncertain anticipation of conflict control in anxiety

BACKGROUND

Anxiety has been characterized by disrupted processing of conflict control, while little is known about anticipatory processing of conflicts in anxiety. Anticipation is the key factor in both anxiety and cognitive control, especially under uncertain conditions. The current study therefore examined neurocomputational mechanisms of uncertain anticipation of conflict control in anxiety.

METHODS

Twenty-six participants with high-trait anxiety and twenty-nine low-trait anxiety participants completed a cue-flanker task with functional magnetic resonance imaging. The hierarchical drift diffusion model (HDDM) was used to measure the cognitive computations during the task. To identify the neurocomputational mechanism of anticipatory control in anxiety, mediation analysis and dynamic causal modelling (DCM) analysis were conducted to examine the relationship between functional connectivity of brain networks and the parameters of HDDM.

RESULTS

We found influences of regulatory signals from the dorsolateral prefrontal cortex to dorsal anterior cingulate cortex on decision threshold in low-trait anxiety (LTA), but not in high-trait anxiety (HTA), especially for the condition with uncertain cues. The results indicate deficient top-down anticipatory control of upcoming conflicts in anxious individuals. DCM and HDDM analyses revealed that lower decision threshold was associated with higher intrinsic connectivity of salience network (SN) in anxious individuals, suggesting that dysfunctional SN disrupts anticipation of conflict control under uncertainty in anxiety.

CONCLUSIONS

Our results suggest hyperfunction of the SN underlies the deficient information accumulation during uncertain anticipation of upcoming conflicts in anxiety. Our findings shed new light on the mechanisms of anticipation processing and the psychopathology of anxiety.

Dynamic targeting enables domain-general inhibitory control over action and thought by the prefrontal cortex

Over the last two decades, inhibitory control has featured prominently in accounts of how humans and other organisms regulate their behaviour and thought. Previous work on how the brain stops actions and thoughts, however, has emphasised distinct prefrontal regions supporting these functions, suggesting domain-specific mechanisms. Here we show that stopping actions and thoughts recruits common regions in the right dorsolateral and ventrolateral prefrontal cortex to suppress diverse content, via dynamic targeting. Within each region, classifiers trained to distinguish action-stopping from action-execution also identify when people are suppressing their thoughts (and vice versa). Effective connectivity analysis reveals that both prefrontal regions contribute to action and thought stopping by targeting the motor cortex or the hippocampus, depending on the goal, to suppress their task-specific activity. These findings support the existence of a domain-general system that underlies inhibitory control and establish Dynamic Targeting as a mechanism enabling this ability.

Long short-term memory-based neural decoding of object categories evoked by natural images

Visual perceptual decoding is one of the important and challenging topics in cognitive neuroscience. Building a mapping model between visual response signals and visual contents is the key point of decoding. Most previous studies used peak response signals to decode object categories. However, brain activities measured by functional magnetic resonance imaging are a dynamic process with time dependence, so peak signals cannot fully represent the whole process, which may affect the performance of decoding. Here, we propose a decoding model based on long short‐term memory (LSTM) network to decode five object categories from multitime response signals evoked by natural images. Experimental results show that the average decoding accuracy using the multitime (2–6 s) response signals is 0.540 from the five subjects, which is significantly higher than that using the peak ones (6 s; accuracy: 0.492; p < .05). In addition, from the perspective of different durations, methods and visual areas, the decoding performances of the five object categories are deeply and comprehensively explored. The analysis of different durations and decoding methods reveals that the LSTM‐based decoding model with sequence simulation ability can fit the time dependence of the multitime visual response signals to achieve higher decoding performance. The comparative analysis of different visual areas demonstrates that the higher visual cortex (VC) contains more semantic category information needed for visual perceptual decoding than lower VC.

Neural correlates of anxiety under interrogation in guilt or innocence contexts

Interrogation elicits anxiety in individuals under scrutiny regardless of their innocence, and thus, anxious responses to interrogation should be differentiated from deceptive behavior in practical lie detection settings. Despite its importance, not many empirical studies have yet been done to separate the effects of interrogation from the acts of lying or guilt state. The present fMRI study attempted to identify neural substrates of anxious responses under interrogation in either innocent or guilt contexts by developing a modified "Doubt" game. Participants in the guilt condition showed higher brain activations in the right central-executive network and bilateral basal ganglia. Regardless of the person's innocence, we observed higher activation of the salience, theory of mind and sensory-motor networks-areas associated with anxiety-related responses in the interrogative condition, compared to the waived conditions. We further explored two different types of anxious responses under interrogation-true detection anxiety in the guilty (true positive) and false detection anxiety in the innocent (false positive). Differential neural responses across these two conditions were captured at the caudate, thalamus, ventral anterior cingulate and ventromedial prefrontal cortex. We conclude that anxiety is a common neural response to interrogation, regardless of an individual's innocence, and that there are detectable differences in neural responses for true positive and false positive anxious responses under interrogation. The results of our study highlight a need to isolate complex cognitive processes involved in the deceptive acts from the emotional and regulatory responses to interrogation in lie detection schemes.

I lie, why don't you: Neural mechanisms of individual differences in self-serving lying

People tend to lie in varying degrees. To advance our understanding of the underlying neural mechanisms of this heterogeneity, we investigated individual differences in self-serving lying. We performed a functional magnetic resonance imaging study in 37 participants and introduced a color-reporting game where lying about the color would in general lead to higher monetary payoffs but would also be punished if get caught. At the behavioral level, individuals lied to different extents. Besides, individuals who are more dishonest showed shorter lying response time, whereas no significant correlation was found between truth-telling response time and the degree of dishonesty. At the neural level, the left caudate, ventromedial prefrontal cortex (vmPFC), right inferior frontal gyrus (IFG), and left dorsolateral prefrontal cortex (dlPFC) were key regions reflecting individual differences in making dishonest decisions. The dishonesty associated activity in these regions decreased with increased dishonesty. Subsequent generalized psychophysiological interaction analyses showed that individual differences in self-serving lying were associated with the functional connectivity among the caudate, vmPFC, IFG, and dlPFC. More importantly, regardless of the decision types, the neural patterns of the left caudate and vmPFC during the decision-making phase could be used to predict individual degrees of dishonesty. The present study demonstrated that lying decisions differ substantially from person to person in the functional connectivity and neural activation patterns which can be used to predict individual degrees of dishonesty.

On the Stability of BOLD fMRI Correlations

Measurement of correlations between brain regions (functional connectivity) using blood oxygen level dependent (BOLD) fMRI has proven to be a powerful tool for studying the functional organization of the brain. Recently, dynamic functional connectivity has emerged as a major topic in the resting-state BOLD fMRI literature. Here, using simulations and multiple sets of empirical observations, we confirm that imposed task states can alter the correlation structure of BOLD activity. However, we find that observations of "dynamic" BOLD correlations during the resting state are largely explained by sampling variability. Beyond sampling variability, the largest part of observed "dynamics" during rest is attributable to head motion. An additional component of dynamic variability during rest is attributable to fluctuating sleep state. Thus, aside from the preceding explanatory factors, a single correlation structure-as opposed to a sequence of distinct correlation structures-may adequately describe the resting state as measured by BOLD fMRI. These results suggest that resting-state BOLD correlations do not primarily reflect moment-to-moment changes in cognitive content. Rather, resting-state BOLD correlations may predominantly reflect processes concerned with the maintenance of the long-term stability of the brain's functional organization.

Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.

Remembered or Forgotten?-An EEG-Based Computational Prediction Approach

Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)—the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events—have been found. This finding indicates that EEG signals convey the information relevant to memory performance. In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. We devise a convolutional neural network for EEG, called ConvEEGNN, to predict subsequently remembered and forgotten events from EEG recorded during memory process. With the ConvEEGNN, prediction of memory performance can be achieved by integrating two main stages: feature extraction and classification. To verify the proposed approach, we employ an auditory memory task to collect EEG signals from scalp electrodes. For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. It was observed that signals from pre-stimulus period and those from during-stimulus period had comparable contributions to memory performance. Furthermore, the connection weights of ConvEEGNN network can reveal prominent channels, which are consistent with the distribution of SME studied previously.

Cognitive neuroscience of honesty and deception: A signaling framework

Understanding the neural basis of human honesty and deception has enormous potential scientific and practical value. However, past approaches, largely developed out of studies with forensic applications in mind, are increasingly recognized as having serious methodological and conceptual shortcomings. Here we propose to address these challenges by drawing on so-called signaling games widely used in game theory and ethology to study behavioral and evolutionary consequences of information transmission and distortion. In particular, by separating and capturing distinct adaptive problems facing signal senders and receivers, signaling games provide a framework to organize the complex set of cognitive processes associated with honest and deceptive behavior. Furthermore, this framework provides novel insights into feasibility and practical challenges of neuroimaging-based lie detection.

Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification

Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., "the month you were born is an odd number"), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6 to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.

Social re-orientation and brain development: An expanded and updated view

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We expand our adolescent re-orientation model to include other developmental periods.

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We review neuroimaging literature on social information processing.

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We combine human and animal based approaches to social behavior.

Social development has been the focus of a great deal of neuroscience based research over the past decade. In this review, we focus on providing a framework for understanding how changes in facets of social development may correspond with changes in brain function. We argue that (1) distinct phases of social behavior emerge based on whether the organizing social force is the mother, peer play, peer integration, or romantic intimacy; (2) each phase is marked by a high degree of affect-driven motivation that elicits a distinct response in subcortical structures; (3) activity generated by these structures interacts with circuits in prefrontal cortex that guide executive functions, and occipital and temporal lobe circuits, which generate specific sensory and perceptual social representations. We propose that the direction, magnitude and duration of interaction among these affective, executive, and perceptual systems may relate to distinct sensitive periods across development that contribute to establishing long-term patterns of brain function and behavior.

Correcting dynamic distortions in 7T echo planar imaging using a jittered echo time sequence

Purpose

To develop a distortion correction method for echo planar imaging (EPI) that is able to measure dynamic changes in B₀.

Theory and Methods

The approach we propose is based on single‐echo EPI with a jittering of the echo time between two values for alternate time points. Field maps are calculated between phase images from adjacent volumes and are used to remove distortion from corresponding magnitude images. The performance of our approach was optimized using an analytical model and by comparison with field maps from dual‐echo EPI. The method was tested in functional MRI experiments at 7T with motor tasks and compared with the conventional static approach.

Results

Unwarping using our method was accurate even for head rotations up to 8.2°, where the static approach introduced errors up to 8.2 mm. Jittering the echo time between 19 and 25 ms had no measurable effect on blood oxygenation level–dependent (BOLD) sensitivity. Our approach reduced the distortions in activated regions to <1 mm and repositioned active voxels correctly.

Conclusion

This method yields accurate distortion correction in the presence of motion. No reduction in BOLD sensitivity was observed. As such, it is suitable for application in a wide range of functional MRI experiments. Magn Reson Med 76:1388–1399, 2016. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.