Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient’s subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state—which results in a loss of this anticorrelation—is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).

On 21 August 2017, North America witnessed a total solar eclipse, with the path of totality passing across the United States from coast to coast. The major public interest in the event inspired the Global Learning and Observations to Benefit the Environment (GLOBE) Observer to organize a citizen science observing campaign to record the meteorological effects of the eclipse. Participants at 17 585 observing sites collected 68 620 temperature observations and 15 978 cloud observations. With 7194 sites positioned in the path of totality, participants provide a nearly unbroken record of the cloud and temperature effects of the eclipse across the contiguous United States. The collection of both temperature and cloud observations provides an opportunity to quantify the cloud–temperature relationship. The unique character of citizen science, which provides data from a large number of observations with limited quality control, requires a method that leverages the large number of observations. By grouping observing sites along the path of totality by 1° longitude bins, the errors from individual sites are averaged out and the meteorological effects of the eclipse can be determined robustly. The data reveal a distinct relationship between prevailing cloud cover and the eclipse-induced temperature depression, in which overcast conditions reduces the temperature depression by about one-half of the value from clear conditions. A comparison of the GLOBE results with mesonet data allows a test of the robustness of the citizen science results. The results also show the great benefit that research using citizen science data receives from increased numbers of participants and observations.

Patients with posttraumatic stress disorder (PTSD) appear to manifest two opposing tendencies in their attentional biases and symptoms. However, whether common neural mechanisms account for their opposing attentional biases and symptoms remains unknown. We here propose a model in which reciprocal inhibition between the amygdala and ventromedial prefrontal cortex (vmPFC) predicts synchronized alternations between emotional under-and overmodulatory states at the neural, behavioral, and symptom levels within the same patients. This reciprocal inhibition model predicts that when the amygdala is dominant, patients enter an emotional undermodulatory state where they show attentional bias toward threat and manifest re-experiencing symptoms. In contrast, when the vmPFC is dominant, patients are predicted to enter an emotional overmodulatory state where they show attentional bias away from threat and avoidance symptoms. To test the model, we performed a behavioral meta-analysis (total N = 491), analyses of own behavioral study (N = 20), and a neuroimaging metaanalysis (total N = 316). Supporting the model, we found the distributions of behavioral attentional measurements to be bimodal, suggesting alternations between the states within patients. Moreover, attentional bias toward threat was related to re-experiencing symptoms, whereas attentional bias away from threat was related with avoidance symptoms. We also found that the increase and decrease of activity in the left amygdala activity was related with re-experiencing and avoidance symptoms, respectively. Our model may help elucidate the neural mechanisms differentiating nondissociative and dissociative subtypes of PTSD, which usually show differential emotional modulatory levels. It may thus provide a new venue for therapies targeting each subtype.

In a complex and uncertain world, how do we select appropriate behavior? One possibility is that we choose actions that are highly reinforced by their probabilistic consequences (model-free processing). However, we may instead plan actions prior to their actual execution by predicting their consequences (model-based processing). It has been suggested that the brain contains multiple yet distinct systems involved in reward prediction. Several studies have tried to allocate model-free and model-based systems to the striatum and the lateral prefrontal cortex (LPFC), respectively. Although there is much support for this hypothesis, recent research has revealed discrepancies. To understand the nature of the reward prediction systems in the LPFC and the striatum, a series of single-unit recording experiments were conducted. LPFC neurons were found to infer the reward associated with the stimuli even when the monkeys had not yet learned the stimulus-reward (SR) associations directly. Striatal neurons seemed to predict the reward for each stimulus only after directly experiencing the SR contingency. However, the one exception was “Exclusive Or” situations in which striatal neurons could predict the reward without direct experience. Previous single-unit studies in monkeys have reported that neurons in the LPFC encode category information, and represent reward information specific to a group of stimuli. Here, as an extension of these, we review recent evidence that a group of LPFC neurons can predict reward specific to a category of visual stimuli defined by relevant behavioral responses. We suggest that the functional difference in reward prediction between the LPFC and the striatum is that while LPFC neurons can utilize abstract code, striatal neurons can code individual associations between stimuli and reward but cannot utilize abstract code.

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