Neural Mechanisms of Facial Emotion Recognition in Autism: Distinct Roles for Anterior Cingulate and dlPFC.

OBJECTIVE: The present study sought to measure and internally validate neural markers of facial emotion recognition (FER) in adolescents and young adults with ASD to inform targeted intervention. METHOD: We utilized fMRI to measure patterns of brain activity among individuals with ASD (N = 21) and matched controls (CON; N = 20) 2 s prior to judgments about the identity of six distinct facial emotions (happy, sad, angry, surprised, fearful, disgust). RESULTS: Predictive modeling of fMRI data (support vector classification; SVC) identified mechanistic roles for brain regions that forecasted correct and incorrect identification of facial emotion as well as sources of errors over these decisions. BOLD signal activation in bilateral insula, anterior cingulate (ACC) and right dorsolateral prefrontal cortex (dlPFC) preceded accurate FER in both controls and ASD. Predictive modeling utilizing SVC confirmed the utility of ACC in forecasting correct decisions in controls but not ASD, and further indicated that a region within the right dlPFC was the source of a type 1 error signal in ASD (i.e. neural marker reflecting an impending correct judgment followed by an incorrect behavioral response) approximately two seconds prior to emotion judgments during fMRI. CONCLUSIONS: ACC forecasted correct decisions only among control participants. Right dlPFC was the source of a false-positive signal immediately prior to an error about the nature of a facial emotion in adolescents and young adults with ASD, potentially consistent with prior work indicating that dlPFC may play a role in attention to and regulation of emotional experience.

MRI brain templates of the male Yucatan minipig.

The pig is growing in popularity as an experimental animal because its gyrencephalic brain is similar to humans. Currently, however, there is a lack of appropriate brain templates to support functional and structural neuroimaging pipelines. The primary contribution of this work is an average volume from an iterative, non-linear registration of 70 five- to seven-month-old male Yucatan minipigs. In addition, several aspects of this study are unique, including the comparison of linear and non-linear template generation, the characterization of a large and homogeneous cohort, an analysis of effective resolution after averaging, and the evaluation of potential in-template bias as well as a comparison with a template from another minipig species using a "left-out" validation set. We found that within our highly homogeneous cohort, non-linear registration produced better templates, but only marginally so. Although our T1-weighted data were resolution limited, we preserved effective resolution across the multi-subject average, produced templates that have high gray-white matter contrast and demonstrate superior registration accuracy compared to an alternative minipig template.

The Cerebellum and Implicit Sequencing: Evidence from Cerebellar Ataxia.

The cerebellum recognizes sequences from prior experiences and uses this information to generate internal models that predict future outcomes in a feedforward manner [Front Hum Neurosci 8: 475, 2014; Cortex 47: 137-44, 2011; Cerebellum 7: 611-5, 2008; J Neurosci 26: 9107-16, 2006]. This process has been well documented in the motor domain, but the cerebellum's role in cognitive sequencing, within the context of implicit versus explicit processes, is not well characterized. In this study, we tested individuals with cerebellar ataxia and healthy controls to clarify the role of the cerebellum sequencing using variations on implicit versus explicit and motor versus cognitive demands across five experiments. Converging results across these studies suggest that cerebellar feedforward mechanisms may be necessary for sequencing in the implicit domain only. In the ataxia group, rhythmic tapping, rate of motor learning, and implicit sequence learning were impaired. However, for cognitive sequencing that could be accomplished using explicit strategies, the cerebellar group performed normally, as though they shifted to extra-cerebellar mechanisms to compensate. For example, when cognitive and motor functions relied on cerebellar function simultaneously, the ataxia group's motor function was unaffected, in contrast to that of controls whose motor performance declined as a function of cognitive load. These findings indicated that the cerebellum is not critical for all forms of sequencing per se. Instead, it plays a fundamental role for sequencing within the implicit domain, whether functions are motor or cognitive. Moreover, individuals with cerebellar ataxia are generally able to compensate for cognitive sequencing when explicit strategies are available in order to preserve resources for motor function.

Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing.

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye's orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method's ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

Reinforcer pathology: Common neural substrates for delay discounting and snack purchasing in prediabetics.

Reinforcer pathology theory stipulates that individuals with both (a) high preference for smaller, immediate over larger, delayed rewards; and (b) high demand for unhealthy commodities are uniquely susceptible to poor health outcomes. Specifically, two behavioral economic tasks (delay discounting, assessing preference for smaller, immediate or larger, delayed rewards; and purchasing, assessing purchases of commodities over changes in price) have been independently associated with conditions such as overweight/obesity and problem substance use. In the present study, we examined possible shared neural regions involved in the processes of delay discounting and demand for snack foods in a prediabetic sample. Fifty-four participants completed both of these tasks. Conjunction between delay discounting and purchasing task results indicates substantial common neural substrates recruited during these two tasks, consistent with interpretations of executive control, interoception, and attention, in the prefrontal cortex, insula, and frontoparietal cortex (superior/middle frontal cortex and superior/inferior parietal lobules), respectively. Collectively, these results suggest possible neural substrates in which the two behavioral risk factors of reinforcer pathology may interact during real-world decision-making in prediabetes.

An adaptive, individualized fMRI delay discounting procedure to increase flexibility and optimize scanner time.

Research on the rate at which people discount the value of future rewards has become increasingly prevalent as discount rate has been shown to be associated with many unhealthy patterns of behavior such as drug abuse, gambling, and overeating. fMRI research points to a fronto-parietal-limbic pathway that is active during decisions between smaller amounts of money now and larger amounts available after a delay. Researchers in this area have used different variants of delay discounting tasks and reported various contrasts between choice trials of different types from these tasks. For instance, researchers have compared 1) choices of delayed monetary amounts to choices of the immediate monetary amounts, 2) 'hard' choices made near one's point of indifference to 'easy' choices that require little thought, and 3) trials where an immediate choice is available versus trials where one is unavailable, regardless of actual eventual choice. These differences in procedure and analysis make comparison of results across studies difficult. In the present experiment, we designed a delay discounting task with the intended capability of being able to construct contrasts of all three comparisons listed above while optimizing scanning time to reduce costs and avoid participant fatigue. This was accomplished with an algorithm that customized the choice trials presented to each participant with the goal of equalizing choice trials of each type. We compared this task, which we refer to here as the individualized discounting task (IDT), to two other delay discounting tasks previously reported in the literature (McClure et al., 2004; Amlung et al., 2014) in 18 participants. Results show that the IDT can examine each of the three contrasts mentioned above, while yielding a similar degree of activation as the reference tasks. This suggests that this new task could be used in delay discounting fMRI studies to allow researchers to more easily compare their results to a majority of previous research while minimizing scanning duration.

The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository.

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

Brain-computer interfaces increase whole-brain signal to noise.

Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

Regulation of craving for real-time fMRI neurofeedback based on individual classification.

In previous real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) studies on smoking craving, the focus has been on within-region activity or between-region connectivity, neglecting the potential predictive utility of broader network activity. Moreover, there is debate over the use and relative predictive power of individual-specific and group-level classifiers. This study aims to further advance rtfMRI-NF for substance use disorders by using whole-brain rtfMRI-NF to assess smoking craving-related brain patterns, evaluate the performance of group-level or individual-level classification (n = 31) and evaluate the performance of an optimized classifier across repeated NF runs. Using real-time individual-level classifiers derived from whole-brain support vector machines, we found that classification accuracy between crave and no-crave conditions and between repeated NF runs increased across repeated runs at both individual and group levels. In addition, individual-level accuracy was significantly greater than group-level accuracy, highlighting the potential increased utility of an individually trained whole-brain classifier for volitional control over brain patterns to regulate smoking craving. This study provides evidence supporting the feasibility of using whole-brain rtfMRI-NF to modulate smoking craving-related brain responses and the potential for learning individual strategies through optimization across repeated feedback runs. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Connectivity Changes Following Episodic Future Thinking in Alcohol Use Disorder.

[Please note that in order to respond to reviewers request we had exceed the 300 word limit. The following is NOT revised from the first submission, please see the actual revised manuscript file for the reviewer-driven changes]. INTRODUCTION: Recent addiction and obesity-related research suggest that episodic future thinking (EFT) can serve as a promising intervention to promote healthy decision making. This study investigated the neural effects of EFT in alcohol use disorder (AUD). METHODS: Participants received either a brief EFT or control intervention to examine differences in resting-state connectivity. We then used these findings to characterize psychophysiological interaction (PPI) differences during a delay discounting (DD) fMRI task. In addition, we used a second control group of AUD participants without any intervention to reproduce and aid in interpreting our key findings. RESULTS: EFT participants, but not controls, showed statistically improved discounting rates - a behavioral marker for addiction. Resting state analyses of the left hippocampus revealed connectivity differences in the frontal poles. The directionality of this difference suggested that EFT reduced a hypoconnectivity relationship between these regions in AUD. We also found resting state connectivity differences between the salience network and the right dorsolateral prefrontal cortex (R DLPFC), which then led us to discover R-to-L DLPFC PPI differences during DD. Moreover, the resting state salience-to-DLPFC functional connectivity showed an inverse relationship to discounting rate while hyperconnectivity between left and right DLPFC reflected slower reaction times during difficult DD trials. DISCUSSION: These findings suggest that EFT produces beneficial changes in neural connectivity patterns in AUD. The alterations in connectivity highlight potential mechanisms underlying the effectiveness of EFT in improving decision-making in AUD. Understanding these neural effects may contribute to the further development of targeted interventions for AUD and related disorders.

Reinforcement learning processes as forecasters of depression remission.

BACKGROUND: Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness. METHODS: We applied support vector machines to investigate whether blood­oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit. RESULTS: Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status. LIMITATIONS: Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling). CONCLUSIONS: Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

Decoding the Brain's Surface to Track Deeper Activity.

Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex's connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.

Quantitative susceptibility mapping of basal ganglia iron is associated with cognitive and motor functions that distinguish spinocerebellar ataxia type 6 and type 3.

Background: In spinocerebellar ataxia type 3 (SCA3), volume loss has been reported in the basal ganglia, an iron-rich brain region, but iron content has not been examined. Recent studies have reported that patients with SCA6 have markedly decreased iron content in the cerebellar dentate, coupled with severe volume loss. Changing brain iron levels can disrupt cognitive and motor functions, yet this has not been examined in the SCAs, a disease in which iron-rich regions are affected. Methods: In the present study, we used quantitative susceptibility mapping (QSM) to measure tissue magnetic susceptibility (indicating iron concentration), structural volume, and normalized susceptibility mass (indicating iron content) in the cerebellar dentate and basal ganglia in people with SCA3 (n = 10) and SCA6 (n = 6) and healthy controls (n = 9). Data were acquired using a 7T Philips MRI scanner. Supplemental measures assessed motor, cognitive, and mood domains. Results: Putamen volume was lower in both SCA groups relative to controls, replicating prior findings. Dentate susceptibility mass and volume in SCA6 was lower than in SCA3 or controls, also replicating prior findings. The novel finding was that higher basal ganglia susceptibility mass in SCA6 correlated with lower cognitive performance and greater motor impairment, an association that was not observed in SCA3. Cerebellar dentate susceptibility mass, however, had the opposite relationship with cognition and motor function in SCA6, suggesting that, as dentate iron is depleted, it relocated to the basal ganglia, which contributed to cognitive and motor decline. By contrast, basal ganglia volume loss, rather than iron content, appeared to drive changes in motor function in SCA3. Conclusion: The associations of higher basal ganglia iron with lower motor and cognitive function in SCA6 but not in SCA3 suggest the potential for using brain iron deposition profiles beyond the cerebellar dentate to assess disease states within the cerebellar ataxias. Moreover, the role of the basal ganglia deserves greater attention as a contributor to pathologic and phenotypic changes associated with SCA.

RF encoding using a multielement parallel transmit system.

Radiofrequency (RF) encoding using spatially variant RF transmission fields represents an alternative to the conventional signal-encoding techniques applied in MRI, which are based on main field gradients. Thus, RF encoding might allow omitting the use of all main field gradients, alleviating acoustic noise and other main field gradient-related problems. This study investigates the potential of RF transmit encoding using spatially nonlinear RF fields generated by an eight-channel parallel transmit system equipped with the corresponding transmit array. Appropriate spatial encoding functions are determined iteratively by randomized superposition of the array element sensitivities and judging their performance. Besides RF amplitude-based signal encoding, the possibility of RF phase-dominated encoding is investigated to allow future fast imaging applications. The theoretical background is given and experimental phantom results are compared with predictions from corresponding simulations to prove the basic feasibility of the approach for low-resolution MRI. Deviations of roughly 10-15% between main field and RF encoded images were found for an in-plane resolution of 5 x 5 mm(2). The approach of RF encode MRI does not seem to be able to replace standard main field gradient encoding completely, but a suitable combination of both concepts could find promising applications.

Working Memory Training Improves Alcohol Users' Episodic Future Thinking: A Rate-Dependent Analysis.

BACKGROUND: Episodic thinking, whether past or future, uses similar neural machinery, and individuals with alcohol dependence have clear challenges with both. Moreover, alcohol-dependent individuals' narrowed temporal window likely gives rise to greater valuation of immediate rewards. We aimed to strengthen working memory (WM) in alcohol-dependent individuals and measure performance on near-transfer (novel WM) and far-transfer delay discounting (DD) tasks, including episodic future thinking (EFT) performance. Importantly, heterogeneous intervention responses could obscure a treatment effect due to individuals' baseline differences. Therefore, we considered WM, DD, and EFT DD scores using rate-dependent analyses. METHODS: A total of 50 alcohol-dependent individuals received either 20 active (Trained) or sham (Control) WM training sessions using the Cogmed adaptive WM training program. Participants completed a near-transfer novel WM task and far-transfer DD and EFT DD tasks before and after training. RESULTS: Active WM training improved performance on the near-transfer task. As determined by Oldham's correlation [rmean(x,y),y-x], initially low near-transfer task scores improved more than initially high scores (i.e., rate dependence) in the Trained group only. Moreover, Trained group individuals with the highest rates of EFT DD at baseline rate-dependently decreased following training, whereas WM training had no effect on DD alone. CONCLUSIONS: These data support the notion that WM training improves near-transfer task performance and may enhance the effects of EFT DD in a subset of alcohol-dependent individuals trapped within the narrowest temporal window. Rate-dependent changes highlight that we should attend to baseline performance to better identify individuals who would most benefit from an intervention.

Early childhood investment impacts social decision-making four decades later.

Early childhood educational investment produces positive effects on cognitive and non-cognitive skills, health, and socio-economic success. However, the effects of such interventions on social decision-making later in life are unknown. We recalled participants from one of the oldest randomized controlled studies of early childhood investment-the Abecedarian Project (ABC)-to participate in well-validated interactive economic games that probe social norm enforcement and planning. We show that in a repeated-play ultimatum game, ABC participants who received high-quality early interventions strongly reject unequal division of money across players (disadvantageous or advantageous) even at significant cost to themselves. Using a multi-round trust game and computational modeling of social exchange, we show that the same intervention participants also plan further into the future. These findings suggest that high quality early childhood investment can result in long-term changes in social decision-making and promote social norm enforcement in order to reap future benefits.

Support vector machine classification of complex fMRI data.

This work examines support vector machine (SVM) classification of complex fMRI data, both in the image domain and in the acquired k-space data. We achieve high classification accuracy using the magnitude data in both domains. Additionally, we maintain high classification accuracy even when using only partial k-space data. Thus we demonstrate the feasibility of using kspace data for classification, enabling rapid realtime acquisition and classification.