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Abstract: Edges are important cues defining coherent auditory objects. As a model of auditory edges, sound on- and offset are particularly suitable to study their neural underpinnings because they contrast a specific physical input against no physical input. Change from silence to sound, that is onset, has extensively been studied and elicits transient neural responses bilaterally in auditory cortex. However, neural activity associated with sound onset is not only related to edge detection but also to novel afferent inputs. Edges at the change from sound to silence, that is offset, are not confounded by novel physical input and thus allow to examine neural activity associated with sound edges per se. In the first experiment, we used silent acquisition functional magnetic resonance imaging and found that the offset of pulsed sound activates planum temporale, superior temporal sulcus and planum polare of the right hemisphere. In the planum temporale and the superior temporal sulcus, offset response amplitudes were related to the pulse repetition rate of the preceding stimulation. In the second experiment, we found that these offset-responsive regions were also activated by single sound pulses, onset of sound pulse sequences and single sound pulse omissions within sound pulse sequences. However, they were not active during sustained sound presentation. Thus, our data show that circumscribed areas in right temporal cortex are specifically involved in identifying auditory edges. This operation is crucial for translating acoustic signal time series into coherent auditory objects. [Copyright &y& Elsevier]

Abstract: Among other auditory operations, the analysis of different sound levels received at both ears is fundamental for the localization of a sound source. These so-called interaural level differences, in animals, are coded by excitatory–inhibitory neurons yielding asymmetric hemispheric activity patterns with acoustic stimuli having maximal interaural level differences. In human auditory cortex, the temporal blood oxygen level-dependent (BOLD) response to auditory inputs, as measured by functional magnetic resonance imaging (fMRI), consists of at least two independent components: an initial transient and a subsequent sustained signal, which, on a different time scale, are consistent with electrophysiological human and animal response patterns. However, their specific functional role remains unclear. Animal studies suggest these temporal components being based on different neural networks and having specific roles in representing the external acoustic environment. Here we hypothesized that the transient and sustained response constituents are differentially involved in coding interaural level differences and therefore play different roles in spatial information processing. Healthy subjects underwent monaural and binaural acoustic stimulation and BOLD responses were measured using high signal-to-noise-ratio fMRI. In the anatomically segmented Heschl’s gyrus the transient response was bilaterally balanced, independent of the side of stimulation, while in opposite the sustained response was contralateralized. This dissociation suggests a differential role at these two independent temporal response components, with an initial bilateral transient signal subserving rapid sound detection and a subsequent lateralized sustained signal subserving detailed sound characterization. [Copyright &y& Elsevier]

Abstract: The auditory cortex is anatomically segregated into a central core and a peripheral belt region, which exhibit differences in preference to bandpassed noise and in temporal patterns of response to acoustic stimuli. While it has been shown that visual stimuli can modify response magnitude in auditory cortex, little is known about differential patterns of multisensory interactions in core and belt. Here, we used functional magnetic resonance imaging and examined the influence of a short visual stimulus presented prior to acoustic stimulation on the spatial pattern of blood oxygen level-dependent signal response in auditory cortex. Consistent with crossmodal inhibition, the light produced a suppression of signal response in a cortical region corresponding to the core. In the surrounding areas corresponding to the belt regions, however, we found an inverse modulation with an increasing signal in centrifugal direction. Our data suggest that crossmodal effects are differentially modulated according to the hierarchical core-belt organization of auditory cortex. [Copyright &y& Elsevier]

• Amygdalar neurometabolites were measured reliably with high anatomical specificity. • Glutamate and other neurometabolites did not differ between smokers and non-smokers. • Glutamine levels in smokers were decreased in satiation compared to baseline. • Glx levels were negatively associated with nicotine quantity and duration. • More severe nicotine addiction may be associated with altered glutamate homeostasis. Tobacco smoking is one of the main causes of premature death worldwide and quitting success remains low, highlighting the need to understand the neurobiological mechanisms underlying relapse. Preclinical models have shown that the amygdala and glutamate play an important role in nicotine addiction. The aims of this study were to compare glutamate and other metabolites in the amygdala between smokers and controls, and between different smoking states. Furthermore, associations between amygdalar metabolite levels and smoking characteristics were explored. A novel non-water-suppressed proton magnetic resonance spectroscopy protocol was applied to quantify neurometabolites in 28 male smokers (≥15 cigarettes/day) and 21 non-smoking controls, matched in age, education, verbal IQ, and weekly alcohol consumption. Controls were measured once (baseline) and smokers were measured in a baseline state (1–3 h abstinence), during withdrawal (24 h abstinence) and in a satiation state (directly after smoking). Baseline spectroscopy data were compared between groups by independent t -tests or Mann-Whitney-U tests. Smoking state differences were investigated by repeated-measures analyses of variance (ANOVAs). Associations between spectroscopy data and smoking characteristics were explored using Spearman correlations. Good spectral quality, high anatomical specificity (98% mean gray matter) and reliable quantification of most metabolites of interest were achieved in the amygdala. Metabolite levels did not differ between groups, but smokers showed significantly higher glutamine levels at baseline than satiation. Glx levels were negatively associated with pack-years and smoking duration. In summary, this study provides first insights into the neurometabolic profile of the amygdala in smokers with high anatomical specificity. By applying proton magnetic resonance spectroscopy, neurometabolites in smokers during different smoking states and non-smoking controls were quantified reliably. A significant shift in glutamine levels between smoking states was detected, with lower concentrations in satiation than baseline. The negative association between Glx levels and smoking quantity and duration may imply altered glutamate homeostasis with more severe nicotine addiction. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: In visual perception of emotional stimuli, low- and high-level appraisal processes have been found to engage different neural structures. Beyond emotional facial expression, emotional prosody is an important auditory cue for social interaction. Neuroimaging studies have proposed a network for emotional prosody processing that involves a right temporal input region and explicit evaluation in bilateral prefrontal areas. However, the comparison of different appraisal levels has so far relied upon using linguistic instructions during low-level processing, which might confound effects of processing level and linguistic task. In order to circumvent this problem, we examined processing of emotional prosody in meaningless speech during gender labelling (implicit, low-level appraisal) and emotion labelling (explicit, high-level appraisal). While bilateral amygdala, left superior temporal sulcus and right parietal areas showed stronger blood oxygen level-dependent (BOLD) responses during implicit processing, areas with stronger BOLD responses during explicit processing included the left inferior frontal gyrus, bilateral parietal, anterior cingulate and supplemental motor cortex. Emotional versus neutral prosody evoked BOLD responses in right superior temporal gyrus, bilateral anterior cingulate, left inferior frontal gyrus, insula and bilateral putamen. Basal ganglia and right anterior cingulate responses to emotional versus neutral prosody were particularly pronounced during explicit processing. These results are in line with an amygdala-prefrontal-cingulate network controlling different appraisal levels, and suggest a specific role of the left inferior frontal gyrus in explicit evaluation of emotional prosody. In addition to brain areas commonly related to prosody processing, our results suggest specific functions of anterior cingulate and basal ganglia in detecting emotional prosody, particularly when explicit identification is necessary. [Copyright &y& Elsevier]

Abstract: Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition. [Copyright &y& Elsevier]

Abstract: Auditory neuroscience has not tapped fMRI''s full potential because of acoustic scanner noise emitted by the gradient switches of conventional echoplanar fMRI sequences. The scanner noise is pulsed, and auditory cortex is particularly sensitive to pulsed sounds. Current fMRI approaches to avoid stimulus–noise interactions are temporally inefficient. Since the sustained BOLD response to pulsed sounds decreases with repetition rate and becomes minimal with unpulsed sounds, we developed an fMRI sequence emitting continuous rather than pulsed gradient sound by implementing a novel quasi-continuous gradient switch pattern. Compared to conventional fMRI, continuous-sound fMRI reduced auditory cortex BOLD baseline and increased BOLD amplitude with graded sound stimuli, short sound events, and sounds as complex as orchestra music with preserved temporal resolution. Response in subcortical auditory nuclei was enhanced, but not the response to light in visual cortex. Finally, tonotopic mapping using continuous-sound fMRI demonstrates that enhanced functional signal-to-noise in BOLD response translates into improved spatial separability of specific sound representations. [Copyright &y& Elsevier]

• First machine learning mega-analysis to investigate predictors of real-time fMRI neurofeedback success. • Inclusion of a pre-training no feedback was associated with higher neurofeedback performance. • Patients were associated with higher neurofeedback performance than healthy individuals. • More data (sharing) in the future will allow for design optimization and a better understanding of neurofeedback learning. Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing. [ABSTRACT FROM AUTHOR]