Sex differences in the functional connectivity of the amygdalae in association with cortisol

Human amygdalae are involved in various behavioral functions such as affective and stress processing. For these behavioral functions, as well as for psychophysiological arousal including cortisol release, sex differences are reported.Here, we assessed cortisol levels and resting-state functional connectivity (rsFC) of left and right amygdalae in 81 healthy participants (42 women) to investigate potential modulation of amygdala rsFC by sex and cortisol concentration.Our analyses revealed that rsFC of the left amygdala significantly differed between women and men: Women showed stronger rsFC than men between the left amygdala and left middle temporal gyrus, inferior frontal gyrus, postcentral gyrus and hippocampus, regions involved in face processing, inner-speech, fear and pain processing. No stronger connections were detected for men and no sex difference emerged for right amygdala rsFC. Also, an interaction of sex and cortisol appeared: In women, cortisol was negatively associated with rsFC of the amygdalae with striatal regions, mid-orbital frontal gyrus, anterior cingulate gyrus, middle and superior frontal gyri, supplementary motor area and the parietal–occipital sulcus. Contrarily in men, positive associations of cortisol with rsFC of the left amygdala and these structures were observed. Functional decoding analyses revealed an association of the amygdalae and these regions with emotion, reward and memory processing, as well as action execution.Our results suggest that functional connectivity of the amygdalae as well as the regulatory effect of cortisol on brain networks differs between women and men. These sex-differences and the mediating and sex-dependent effect of cortisol on brain communication systems should be taken into account in affective and stress-related neuroimaging research. Thus, more studies including both sexes are required

Interactions between visceral afferent signaling and stimulus processing

Visceral afferent signals to the brain influence thoughts, feelings and behaviour. Here we highlight the findings of a set of empirical investigations in humans concerning body-mind interaction that focus on how feedback from states of autonomic arousal shapes cognition and emotion. There is a longstanding debate regarding the contribution of the body, to mental processes. Recent theoretical models broadly acknowledge the role of (autonomically mediated) physiological arousal to emotional, social and motivational behaviours, yet the underlying mechanisms are only partially characterized. Neuroimaging is overcoming this shortfall; first, by demonstrating correlations between autonomic change and discrete patterns of evoked, and task- independent, neural activity; second, by mapping the central consequences of clinical perturbations in autonomic response and; third, by probing how dynamic fluctuations in peripheral autonomic state are integrated with perceptual, cognitive and emotional processes. Building on the notion that an important source of the brain’s representation of physiological arousal is derived from afferent information from arterial baroreceptors, we have exploited the phasic nature of these signals to show their differential contribution to the processing of emotionally-salient stimuli. This recent work highlights the facilitation at neural and behavioral levels of fear and threat processing that contrasts with the more established observations of the inhibition of central pain processing during baroreceptors activation. The implications of this body-brain-mind axis are discussed

Oppositional COMT Val158Met effects on resting state functional connectivity in adolescents and adults

© 2014, The Author(s).Prefrontal dopamine levels are relatively increased in adolescence compared to adulthood. Genetic variation of COMT (COMT Val158Met) results in lower enzymatic activity and higher dopamine availability in Met carriers. Given the dramatic changes of synaptic dopamine during adolescence, it has been suggested that effects of COMT Val158Met genotypes might have oppositional effects in adolescents and adults. The present study aims to identify such oppositional COMT Val158Met effects in adolescents and adults in prefrontal brain networks at rest. Resting state functional connectivity data were collected from cross-sectional and multicenter study sites involving 106 healthy young adults (mean age 24 ± 2.6 years), gender matched to 106 randomly chosen 14-year-olds. We selected the anterior medial prefrontal cortex (amPFC) as seed due to its important role as nexus of the executive control and default mode network. We observed a significant age-dependent reversal of COMT Val158Met effects on resting state functional connectivity between amPFC and ventrolateral as well as dorsolateral prefrontal cortex, and parahippocampal gyrus. Val homozygous adults exhibited increased and adolescents decreased connectivity compared to Met homozygotes for all reported regions. Network analyses underscored the importance of the parahippocampal gyrus as mediator of observed effects. Results of this study demonstrate that adolescent and adult resting state networks are dose-dependently and diametrically affected by COMT genotypes following a hypothetical model of dopamine function that follows an inverted U-shaped curve. This study might provide cues for the understanding of disease onset or dopaminergic treatment mechanisms in major neuropsychiatric disorders such as schizophrenia and attention deficit hyperactivity disorder

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging.

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (www.cam-can.com). This dataset contained two sessions of resting-state fMRI from 214 adults aged 18-88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125-4156, 2017. © 2017 Wiley Periodicals, Inc

Simultaneous Multislice (SMS) Imaging Techniques

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Ultra-fast functional MRI of brain activity and parallelized data analysis strategies

Funktionelle Magnetresonanztomographie (fMRT), ein wesentliches nicht-invasives Werkzeug der Neurowissenschaften, basiert auf der Analyse großer Datenmengen mit verschiedenen rechenintensiven statistischen Verfahren. Neuere Entwicklungen im Bereich von MR-Sequenzen, insbesondere die auf gleichzeitiger Anregung mehrerer Bildschichten basierende Beschleunigungstechnik Simultaneous Multi-Slice imaging (SMS), generieren immer größere Datenmengen, doch der tatsächliche Erkenntnisgewinn aus diesen Techniken wird oft limitiert durch den Mangel an ausreichend performanter Software für die algorithmisch komplexeren Analysen. Zwar gibt es verschiedene Softwarepakete und -frameworks zur effizienten Analyse großer Datenmengen, doch da kaum Implementierungen von spezifischen Analysen von Neurobildgebungsdaten existieren werden diese in den Neurowissenschaften erst sehr selten verwendet. In dieser Arbeit wird ein auf der statistischen Analysesoftware R basiertes Framework zur effizienten Analyse von fMRT-Daten vorgestellt und dessen Stärken anhand mehrerer Anwendungen, in denen der Umgang mit großen Datenmengen und Parallelisierung der Berechnungen vorkommen, hervorgehoben. Insbesondere stellen die aus der Verwendung von SMS-Sequenzen resultierenden Datensätze mit hoher zeitlicher wie räumlicher Auflösung durch ihre schiere Größe eine Herausforderung dar, die mit den derzeit verbreiteten Programmen nicht verarbeitet werden können. Im Gegensatz dazu ermöglicht es das hier vorgestellten Framework, auch komplexe Analysen wie zeitliche Independent Component Analysis (tICA) auf diesen Daten durchzuführen. Durch die Anwendung von tICA auf fMRT-Datensätze mit hinreichend hoher zeitlicher Auflösung um auch den Frequenzbereich des Herzschlags und damit zusammenhängenden Pulsationen im Hirn kritisch abzutasten wird gezeigt, dass die Signalfluktuationen in Ruhezustandsnetzwerken des Hirns einen breiteren Frequenzbereich umfassen als bisher angenommen, bis hin zu Frequenzen über 0.25Hz gehend, die mit typischen fMRT-Sequenzen mit einer zeitlichen Auflösung von 2s (oder langsamer) nicht gemessen werden können. Die kritische Abtastung von Signalen physiologischen Ursprungs erlaubt es darüberhinaus, den Einfluss zerebraler Gefäße auf fMRT-Experimente durch eine Separierung von arteriellen Pulsationen und venösen signalen besser zu berücksichtigen. Die bessere Identifikation venöser Signale kann dazu genutzt werden, die langjährige Brain-or-Vein-Debatte, ob die im fMRT gemessenen Signale räumlich den tatsächlich aktiven Hirnregionen oder dem venösen Abfluss zuzuordnen sind, mit neuen Erkenntnissen voranzutreiben: Im Fall von Aktivierungen in der Amygdalaregion bei einem emotionalen fMRT-Paradigma konnte gezeigt werden, dass die mit fMRT gemessenen Signalveränderungen nicht in der Amygdala selbst, sondern einer naheliegenden Vene entstehen.Functional Magnetic Resonance imaging (fMRI) is an important tool for advanced neuroscientific research involving large amounts of data and many computationally complex analysis approaches. While recent developments in MR sequences, including the acceleration technique of simultaneous multi-slice (SMS) imaging, tend to yield even larger datasets, the practical benefits from these techniques have been limited by a lack of efficient computational tools to perform some of the more complex analyses on these datasets limiting the information gained. Many high-performance computing tools for the analysis of large datasets have been developed that could potentially cope with the amounts of data involved here, but the lack of specific implementations of neuroimaging analysis tools using these advances means that they are not yet widely used in neuroimaging research. In this thesis, a computational framework for efficient analysis of fMRI data is presented based on the statistical computing language R, and in several applications of this framework, including large data handling and parallelization of computations, show how its strengths can lead to deeper insights. Most importantly, SMS sequences for fMRI have been employed to gain datasets with a high temporal as well as spatial resolution, but whose sheer size makes them difficult to handle with most established tools. Using the efficient computing framework, however, even complex analyses of these data become possible, including temporal Independent Component Analysis (tICA). Using tICA on fMRI datasets with a very high temporal resolution, critically sampling cardiac frequencies and thus cardiac related pulsations in the brain, it could be shown that the range of signal fluctuations in resting-state networks of the brain spans a much wider frequency range than previously believed, even beyond the frequencies detectable in fMRI experiments using a sampling rate of 2s (or slower). The critical sampling of signals of physiological origin further allows to better account for the influence of brain vasculature on fMRI experiments by controlling for arterial pulsations as well as better identifying venous signals. The latter can finally lead to new insights in the long-standing brain-or-vein-debate, exemplified in a demonstration that signals typically measured in emotional paradigms in the amygdala region originate largely in veins rather than in the amygdala itself.submitted by Roland N. BoubelaAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheWien, Med. Univ., Diss., 2014OeBB(VLID)171615

Improved quantification of cerebral hemodynamics using individualized time thresholds for assessment of peak enhancement parameters derived from dynamic susceptibility contrast enhanced magnetic resonance imaging.

Assessment of cerebral ischemia often employs dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC-MRI) with evaluation of various peak enhancement time parameters. All of these parameters use a single time threshold to judge the maximum tolerable peak enhancement delay that is supposed to reliably differentiate sufficient from critical perfusion. As the validity of this single threshold approach still remains unclear, in this study, (1) the definition of a threshold on an individual patient-basis, nevertheless (2) preserving the comparability of the data, was investigated.The histogram of time-to-peak (TTP) values derived from DSC-MRI, the so-called TTP-distribution curve (TDC), was modeled using a double-Gaussian model in 61 patients without severe cerebrovascular disease. Particular model-based zf-scores were used to describe the arterial, parenchymal and venous bolus-transit phase as time intervals Ia,p,v. Their durations (delta Ia,p,v), were then considered as maximum TTP-delays of each phase.Mean-R2 for the model-fit was 0.967. Based on the generic zf-scores the proposed bolus transit phases could be differentiated. The Ip-interval reliably depicted the parenchymal bolus-transit phase with durations of 3.4 s-10.1 s (median = 4.3s), where an increase with age was noted (∼30 ms/year).Individual threshold-adjustment seems rational since regular bolus-transit durations in brain parenchyma obtained from the TDC overlap considerably with recommended critical TTP-thresholds of 4 s-8 s. The parenchymal transit time derived from the proposed model may be utilized to individually correct TTP-thresholds, thereby potentially improving the detection of critical perfusion

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Clustering

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6 ± 5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remainingdifferences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needingany additional data beyond what is usually acquired (and exported) in standard fMRI experiments

Big Data approaches for the analysis of large-scale fMRI data using Apache Spark and GPU processing: A demonstration on resting-state fMRI data from the Human Connectome Project

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still only rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets