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Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which “couple” the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC. [ABSTRACT FROM AUTHOR]

Multi-variate pattern analysis (MVPA) applied to BOLD-fMRI has proven successful at decoding complicated fMRI signal patterns associated with a variety of cognitive processes. One cognitive process, not yet investigated, is the mental representation of "Yes/No" thoughts that precede the actual overt response to a binary "Yes/No" question. In this study, we focus on examining: (1) whether spatial patterns of the hemodynamic response carry sufficient information to allow reliable decoding of "Yes/No" thoughts; and (2) whether decoding of "Yes/No" thoughts is independent of the intention to respond honestly or dishonestly. To achieve this goal, we conducted two separate experiments. Experiment 1, collected on a 3T scanner, examined the whole brain to identify regions that carry sufficient information to permit significantly above-chance prediction of "Yes/No" thoughts at the group level. In Experiment 2, collected on a 7T scanner, we focused on the regions identified in Experiment 1 to examine the capability of achieving high decoding accuracy at the single subject level. A set of regions - namely right superior temporal gyrus, left supra-marginal gyrus, and left middle frontal gyrus - exhibited high decoding power. Decoding accuracy for these regions increased with trial averaging. When 18 trials were averaged, the median accuracies were 82.5%, 77.5%, and 79.5%, respectively. When trials were separated according to deceptive intentions (set via experimental cues), and classifiers were trained on honest trials, but tested on trials where subjects were asked to deceive, the median accuracies of these regions still reached 66%, 75%, and 78.5%. These results provide evidence that concealed "Yes/No" thoughts are encoded in the BOLD signal, retaining some level of independence from the subject's intentions to answer honestly or dishonestly. These findings also suggest the theoretical possibility for more efficient brain-computer interfaces where subjects only need to think their answers to communicate. [ABSTRACT FROM AUTHOR]

Abstract: The insula has been identified as a key region involved in interoceptive awareness. Whilst imaging studies have investigated the neural activation patterns in this region involved in intero- and exteroceptive awareness, the underlying biochemical mechanisms still remain unclear. In order to investigate these, a well-established fMRI task targeting interoceptive awareness (heartbeat counting) and exteroceptive awareness (tone counting) was combined with magnetic resonance spectroscopy (MRS). Controlling for physiological noise, neural activity in the insula during intero- and exteroceptive awareness was confirmed in an independent data sample using the same fMRI design. Focussing on MRS values from the left insula and combining them with neural activity during intero- and exteroceptive awareness in the same healthy individuals, we demonstrated that GABA concentration in a region highly involved in interoceptive processing is correlated with neural responses to interoceptive stimuli, as opposed to exteroceptive stimuli. In addition, both GABA and interoceptive signal changes in the insula predicted the degree of depressed affect, as measured by the Beck Hopelessness Scale. On the one hand, the association between GABA concentration and neural activity during interoceptive awareness provides novel insight into the biochemical underpinnings of insula function and interoception. On the other, through the additional association of both GABA and neural activity during interoception with depressed affect, these data also bear potentially important implications for psychiatric disorders like depression and anxiety, where GABAergic deficits, altered insula function and abnormal affect coincide. [Copyright &y& Elsevier]

Abstract: Interdependent cultures (such as the Chinese) and independent cultures (such as the German) differ in their attitude towards harmony that is more valued in interdependent cultures. Interdependent and independent cultures also differ in their appreciation of anger — an emotion that implies the disruption of harmony. The present study investigated if interdependent and independent cultures foster distinct brain activity associated with empathic processing of familiar angry, familiar neutral, and unfamiliar neutral faces. Using functional MRI, we scanned Chinese and German healthy subjects during an intentional empathy task, a control task (the evaluation of skin color), and a baseline condition. The subject groups were matched with regard to age, gender, and education. Behaviorally, Chinese subjects described themselves as significantly more interdependent compared to German subjects. The contrast ‘intentional empathy for familiar angry’>‘baseline’ revealed several regions, including the left inferior frontal cortex, the left supplementary motor area, and the left insula, that showed comparable hemodynamic responses in both groups. However, the left dorsolateral prefrontal cortex had stronger hemodynamic responses in Chinese subjects in the contrast ‘intentional empathy for familiar angry’>‘baseline’. Germans, in contrast, showed stronger hemodynamic responses in the right temporo-parietal junction, right inferior and superior temporal gyrus, and left middle insula for the same contrast. Hemodynamic responses in the latter three brain regions correlated with interdependences scores over all subjects. Our results suggest that enhanced emotion regulation during empathy with anger in the interdependent lifestyle is mediated by the left dorsolateral prefrontal cortex. Increased tolerance towards the expression of anger in the independent lifestyle, in contrast, is associated with increased activity of the right inferior and superior temporal gyrus and the left middle insula. [Copyright &y& Elsevier]

Abstract: Self-referential processing involves a complex set of cognitive functions, posing challenges to delineating its independent neural correlates. While self-referential processing has been considered functionally intertwined with episodic memory, the present study explores their overlap and dissociability. Standard tasks for self-referential processing and episodic memory were combined into a single fMRI experiment. Contrasting the effects of self-relatedness and retrieval success allowed for the two processes to be delineated. Stimuli judged as self-referential specifically activated the posterior cingulate/anterior precuneus, the medial prefrontal cortex, and an inferior division of the inferior parietal lobule. In contrast, episodic memory retrieval specifically involved the posterior precuneus, the right anterior prefrontal cortex, and a superior division of the inferior parietal lobule (extending into superior parietal lobule). Overlapping activations were found in intermediate zones in the precuneus and the inferior parietal lobule, but not in the prefrontal cortex. While our data show common networks for both processes in the medial and lateral parietal cortex, three functional differentiations were also observed: (1) an anterior–posterior differentiation within the medial parietal cortex; (2) a medial–anterolateral differentiation within the prefrontal cortex; and, (3) an inferior–superior differentiation within the lateral parietal cortex for self-referential processing versus episodic memory retrieval. [Copyright &y& Elsevier]

Abstract: Emotions are frequently characterized by distinct dimensions such as valence, intensity, and recognition. However, the exact neural representation of these dimensions in different prefrontal cortical regions remains unclear. One of the problems in revealing prefrontal cortical representation is that the very same regions are also involved in cognitive functions associated with emotion processing. We therefore conducted an fMRI study involving the viewing of emotional pictures (using the International Affective Picture System; IAPS) and controlled for associated cognitive processing like judgment and preceding attention. Functional activation was correlated with subjective post-scanning ratings of valence, intensity, and recognition. Valence significantly correlated with the functional response in ventromedial prefrontal cortex (VMPFC) and dorsolateral prefrontal cortex (DLPFC), intensity with activation in ventrolateral prefrontal cortex (VLPFC) and dorsomedial prefrontal cortex (DMPFC), and recognition with the functional response in perigenual anterior cingulate cortex (PACC). In conclusion, our results indicate segregated neural representation of the different emotion dimensions in different prefrontal cortical regions. [Copyright &y& Elsevier]

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees , artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients. • Anesthetic-induced unconsciousness informs pathological unconsciousness. • Rs-fMRI-based machine learning is applied to distinguish conscious from unconscious subjects. • Machine learning models achieve reliable performance in cross-dataset generalizations. • Hyperparameter optimization significantly improves model performance. • Network-level functional connectivity features are most informative for classification. [ABSTRACT FROM AUTHOR]