Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.

Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.

Blocked designs in functional magnetic resonance imaging (fMRI) are useful to localize functional brain areas. A blocked design consists of different blocks of trials of the same stimulus type and is characterized by three factors: the length of blocks, i.e., number of trials per blocks, the ordering of task and rest blocks, and the time between trials within one block. Optimal design theory was applied to find the optimal combination of these three design factors. Furthermore, different error structures were used within a general linear model for the analysis of fMRI data, and the maximin criterion was applied to find designs which are robust against misspecification of model parameters.

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Attention has been shown to modulate the visual evoked potential (VEP) recorded to reversing achromatic patterns. However, the chromatic onset VEP appears to be robust to attentional shifts. Functional magnetic resonance imaging (fMRI) responses to both chromatic and achromatic reversing patterns are also affected by attention. Resolution and comparison of these results is problematic due to differences in presentation mode, stimulus parameters, and the source of the response. Here, we report the results of experiments using comparable perceptual contrasts, pattern reversals, and a co-extensive and highly demanding multiple object tracking (MOT) task while exploring the effects of attentional modulation across both the chromatic (L − M) and (S − (L + M)) and the achromatic visual pathways. Our findings indicate that although achromatic VEPs are modulated by attention, chromatic VEPs are more robust to attentional modulation, even when using comparable stimulus presentation modes and in the presence of a highly demanding distractor task. In addition, we found that the majority of the modulation appears to be from a relative decrease in response due to the distractor task rather than a relative increase in response during heightened attention to the stimulus.

Similar to adults with posttraumatic stress disorder, children with early life adversity show bias in memory for negative emotional stimuli. However, it is not well understood how childhood adversity impacts mechanisms underlying emotional memory. N = 56 children (8–14 years, 48% female) reported on adverse experiences including potentially traumatic events and underwent fMRI while attending to emotionally pleasant, neutral, or negative images. Post-scan, participants completed a cued recall test to assess memory for these images. Emotional difference-in-memory (DM) scores were computed by subtracting negative or positive from neutral recall performance. All children showed enhancing effects of emotion on recall, with no effect of trauma load. However, children with less trauma showed a larger emotional DM for both positive and negative stimuli when amygdala or anterior hippocampal activity was higher. In contrast, highly trauma-exposed children demonstrated a lower emotional DM with greater amygdala or hippocampal activity. This suggested that alternative neural mechanisms might support emotional enhancement of encoding in children with greater trauma load. Whole-brain analyses revealed that right fusiform activity during encoding positively correlated with both trauma load and successful later recall of positive images. Therefore, highly trauma-exposed children may use alternative, potentially adaptive neural pathways via the ventral visual stream to encode positive emotional events.

Recent theories suggest that for youth highly sensitive to incentives, perceiving more social threat may contribute to social anxiety (SA) symptoms. In 129 girls (ages 11–13) oversampled for shy/fearful temperament, we thus examined how interactions between neural responses to social reward (vs. neutral) cues (measured during anticipation of peer feedback) and perceived social threat in daily peer interactions (measured using ecological momentary assessment) predict SA symptoms two years later. No significant interactions emerged when neural reward function was modeled as a latent factor. Secondary analyses showed that higher perceived social threat was associated with more severe SA symptoms two years later only for girls with higher basolateral amygdala (BLA) activation to social reward cues at baseline. Interaction effects were specific to BLA activation to social reward (not threat) cues, though a main effect of BLA activation to social threat (vs. neutral) cues on SA emerged. Unexpectedly, interactions between social threat and BLA activation to social reward cues also predicted generalized anxiety and depression symptoms two years later, suggesting possible transdiagnostic risk pathways. Perceiving high social threat may be particularly detrimental for youth highly sensitive to reward incentives, potentially due to mediating reward learning processes, though this remains to be tested.

Major depressive disorder (MDD) is characterized by deficient reward functions in the brain. However, existing findings on functional alterations during reward anticipation, reward processing, and learning among MDD patients are inconsistent, and it was unclear whether a common reward system implicated in multiple reward functions is altered in MDD. Here we meta-analyzed 18 past studies that compared brain reward functions between adult MDD patients (N = 477, mean age = 26.50 years, female = 59.40%) and healthy controls (N = 506, mean age = 28.11 years, females = 55.58%), and particularly examined group differences across multiple reward functions. Jack-knife sensitivity and subgroup meta-analyses were conducted to test robustness of findings across patient comorbidity, task paradigm, and reward nature. Meta-regression analyses assessed the moderating effect of patient symptom severity and anhedonia scores. We found during reward anticipation, MDD patients showed lower activities in the lateral prefrontal-thalamus circuitry. During reward processing, patients displayed reduced activities in the right striatum and prefrontal cortex, but increased activities in the left temporal cortex. During reward learning, patients showed reduced activity in the lateral prefrontal–thalamic–striatal circuitry and the right parahippocampal–occipital circuitry but higher activities in bilateral cerebellum and the left visual cortex. MDD patients showed decreased activity in the right thalamus during both reward anticipation and learning, and in the right caudate during both reward processing and learning. Larger functional changes in MDD were observed among patients with more severe symptoms and higher anhedonia levels. The thalamic-striatal circuitry functional alterations could be the key neural mechanism underlying MDD patients overarching reward function deficiencies.

Bilinguals may choose to speak a language either at their own will or in response to an external demand, but the underlying neural mechanisms in the two contexts is poorly understood. In the present study, Chinese–English bilinguals named pairs of pictures in three conditions: during forced-switch, the naming language altered between pictures 1 and 2. During non-switch, the naming language used was the same. During free-naming, either the same or different languages were used at participants' own will. While behavioural switching costs were observed during free-naming and forced-switching, neuroimaging results showed that forced language selection (i.e., forced-switch and non-switch) is associated with left-lateralized frontal activations, which have been implicated in inhibitory control. Free language selection (i.e., free-naming), however, was associated with fronto-parietal activations, which have been implicated in self-initiated behaviours. These findings offer new insights into the neural differentiation of language control in forced and free language selection contexts.