-
Hurst Fisher posted an update 6 months ago
We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.Rhythmic brain activity may reflect a functional mechanism that facilitates cortical processing and dynamic interareal interactions and thereby give rise to complex behavior. Using magnetoencephalography (MEG), we investigated rhythmic brain activity in a brain-wide network and their relation to behavior, while human subjects executed a variant of the Simon task, a simple stimulus-response task with well-studied behavioral effects. We hypothesized that the faster reaction times (RT) on stimulus-response congruent versus incongruent trials are associated with oscillatory power changes, reflecting a change in local cortical activation. Additionally, we hypothesized that the faster reaction times for trials following instances with the same stimulus-response contingency (the so-called Gratton effect) is related to contingency-induced changes in the state of the network, as measured by differences in local spectral power and interareal phase coherence. This would be achieved by temporarily upregulating the connectivity strength between behaviorally relevant network nodes. We identified regions-of-interest that differed in local synchrony during the response phase of the Simon task. Within this network, spectral power in none of the nodes in either of the studied frequencies was significantly different in the pre-cue window of the subsequent trial. Nor was there a significant difference in coherence between the task-relevant nodes that could explain the superior behavioral performance after compatible consecutive trials.Since the publication of the first neuroscience study investigating emotion with music about two decades ago, the number of functional neuroimaging studies published on this topic has increased each year. This research interest is in part due to the ubiquity of music across cultures, and to music’s power to evoke a diverse range of intensely felt emotions. To support a better understanding of the brain correlates of music-evoked emotions this article reports a coordinate-based meta-analysis of neuroimaging studies (n = 47 studies with n = 944 subjects). The studies employed a range of diverse experimental approaches (e.g., using music to evoke joy, sadness, fear, tension, frissons, surprise, unpleasantness, or feelings of beauty). The results of an activation likelihood estimation (ALE) indicate large clusters in a range of structures, including amygdala, anterior hippocampus, auditory cortex, and numerous structures of the reward network (ventral and dorsal striatum, anterior cingulate cortex, orbitofrontal cortex, secondary somatosensory cortex). The results underline the rewarding nature of music, the role of the auditory cortex as an emotional hub, and the role of the hippocampus in attachment-related emotions and social bonding.Psychopathic individuals are notorious for their callous disregard for others’ emotions. Prior research has linked psychopathy to deficits in affective mechanisms underlying empathy (e.g., affective sharing), yet research relating psychopathy to cognitive mechanisms underlying empathy (e.g., affective perspective-taking and Theory of Mind) requires further clarification. To elucidate the neurobiology of cognitive mechanisms of empathy in psychopathy, we administered an fMRI task and tested for global as well as emotion-specific deficits in affective perspective-taking. Adult male incarcerated offenders (N = 94) viewed images of two people interacting, with one individual’s face obscured by a shape. Participants were cued to either identify the emotion of the obscured individual or identify the shape from one of two emotion or shape choices presented on each trial. Target emotions included anger, fear, happiness, sadness, and neutral. Contrary to predictions, psychopathy was unrelated to neural activity in the Affective Perspective-taking > Shape contrast. In line with predictions, psychopathy was negatively related to task accuracy during affective perspective-taking for fear, happiness, and sadness. Psychopathy was related to reduced hemodynamic activity exclusively during fear perspective-taking in several areas left anterior insula extending into posterior orbitofrontal cortex, right precuneus, left superior parietal lobule, and left superior occipital cortex. Although much prior research has emphasized psychopathy-related abnormalities in affective mechanisms mediating empathy, current results add to growing evidence of psychopathy-related abnormalities in a cognitive mechanism related to empathy. These findings highlight brain regions that are hypoactive in psychopathy when explicitly processing another’s fear.To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. UBCS039 cell line To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our “ground truth” for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity.
Please follow & like us :)
All content contained on CatsWannaBeCats.Com, unless otherwise acknowledged,is the property of CatsWannaBeCats.Com and subject to copyright.