112 research outputs found
The neural architecture of emotional intelligence.
Emotional Intelligence (EI) is a nebulous concept that permeates daily interpersonal communication. Despite prolific research into its benefits, EI subjective measurement is difficult, contributing to an enigmatic definition of its core constructs. However, neuroimaging research probing socioaffective brain mechanisms underlying putative EI constructs can add an objective perspective to existing models, thereby illuminating the nature of EI. Therefore, the primary aim of this dissertation is to identify brain networks underlying EI and examine how EI arises from the brain’s functional and structural neuroarchitecture. EI is first defined according to behavioral data, which suggests EI is made up of two core constructs: Empathy and Emotion Regulation (ER). The interaction of brain networks underlying Empathy and ER is then investigated using a novel neuroimaging analysis method: dynamic functional connectivity (dynFC). The results suggest efficient communication and (re)configuration between the CEN, DMN, SN underlie both ER and RME task dynamics, and that these temporal patterns relate to trait empathy and ER tendency. Given the demonstrated behavioral and neurobiological relationship between empathy and ER, our second aim is to examine each of these constructs individually through detailed experiments using a variety of neuroimaging methodologies. The dissertation concludes by proposing EI is an ability that arises from the effective, yet flexible communication between brain networks underlying Empathy and ER. The dissertation is divided into five chapters. Chapter I describes the foundational concept of EI as originally described by a variety of psychological figures and the lacuna that exists in terms of its neural correlates. Chapter II presents behavioral data that proposes EI is best predicted by Empathy and ER. Chapter III explores the dynamic relationship between brain networks underlying Empathy and ER, with the aim of elucidating their neurobiological associations, and investigate how such associations may combine to create EI. Chapter IV examines Empathy closely, by probing its neurobiological relationship to interoception and anxiety. Chapter V examines ER closely, by investigating whether gender plays a role in ER, and its neurobiological relationship to hormones. Chapter VI links the general findings from Chapters III, IV and V, and proposes an integrative neurocognitive EI model. The dissertation concludes by providing clinical and non-clinical applications for the model
Predictive cognition in dementia: the case of music
The clinical complexity and pathological diversity of neurodegenerative diseases impose immense challenges for diagnosis and the design of rational interventions. To address these challenges, there is a need to identify new paradigms and biomarkers that capture shared pathophysiological processes and can be applied across a range of diseases. One core paradigm of brain function is predictive coding: the processes by which the brain establishes predictions and uses them to minimise prediction errors represented as the difference between predictions and actual sensory inputs. The processes involved in processing unexpected events and responding appropriately are vulnerable in common dementias but difficult to characterise. In my PhD work, I have exploited key properties of music – its universality, ecological relevance and structural regularity – to model and assess predictive cognition in patients representing major syndromes of frontotemporal dementia – non-fluent variant PPA (nfvPPA), semantic-variant PPA (svPPA) and behavioural-variant FTD (bvFTD) - and Alzheimer’s disease relative to healthy older individuals. In my first experiment, I presented patients with well-known melodies containing no deviants or one of three types of deviant - acoustic (white-noise burst), syntactic (key-violating pitch change) or semantic (key-preserving pitch change). I assessed accuracy detecting melodic deviants and simultaneously-recorded pupillary responses to these deviants. I used voxel-based morphometry to define neuroanatomical substrates for the behavioural and autonomic processing of these different types of deviants, and identified a posterior temporo-parietal network for detection of basic acoustic deviants and a more anterior fronto-temporo-striatal network for detection of syntactic pitch deviants. In my second chapter, I investigated the ability of patients to track the statistical structure of the same musical stimuli, using a computational model of the information dynamics of music to calculate the information-content of deviants (unexpectedness) and entropy of melodies (uncertainty). I related these information-theoretic metrics to performance for detection of deviants and to ‘evoked’ and ‘integrative’ pupil reactivity to deviants and melodies respectively and found neuroanatomical correlates in bilateral dorsal and ventral striatum, hippocampus, superior temporal gyri, right temporal pole and left inferior frontal gyrus. Together, chapters 3 and 4 revealed new hypotheses about the way FTD and AD pathologies disrupt the integration of predictive errors with predictions: a retained ability of AD patients to detect deviants at all levels of the hierarchy with a preserved autonomic sensitivity to information-theoretic properties of musical stimuli; a generalized impairment of surprise detection and statistical tracking of musical information at both a cognitive and autonomic levels for svPPA patients underlying a diminished precision of predictions; the exact mirror profile of svPPA patients in nfvPPA patients with an abnormally high rate of false-alarms with up-regulated pupillary reactivity to deviants, interpreted as over-precise or inflexible predictions accompanied with normal cognitive and autonomic probabilistic tracking of information; an impaired behavioural and autonomic reactivity to unexpected events with a retained reactivity to environmental uncertainty in bvFTD patients. Chapters 5 and 6 assessed the status of reward prediction error processing and updating via actions in bvFTD. I created pleasant and aversive musical stimuli by manipulating chord progressions and used a classic reinforcement-learning paradigm which asked participants to choose the visual cue with the highest probability of obtaining a musical ‘reward’. bvFTD patients showed reduced sensitivity to the consequence of an action and lower learning rate in response to aversive stimuli compared to reward. These results correlated with neuroanatomical substrates in ventral and dorsal attention networks, dorsal striatum, parahippocampal gyrus and temporo-parietal junction. Deficits were governed by the level of environmental uncertainty with normal learning dynamics in a structured and binarized environment but exacerbated deficits in noisier environments. Impaired choice accuracy in noisy environments correlated with measures of ritualistic and compulsive behavioural changes and abnormally reduced learning dynamics correlated with behavioural changes related to empathy and theory-of-mind. Together, these experiments represent the most comprehensive attempt to date to define the way neurodegenerative pathologies disrupts the perceptual, behavioural and physiological encoding of unexpected events in predictive coding terms
Towards a better understanding of the impact of heart rate on the BOLD signal: a new method for physiological noise correction and its applications
Functional magnetic resonance imaging (fMRI) based on blood oxygenation level-dependent (BOLD) contrast allows non-invasive examination of brain activity and is widely used in the neuroimaging field. The BOLD contrast mechanism reflects hemodynamic changes resulting from a complex interplay of blood flow, blood volume, and oxygen consumption. Heart rate (HR) variations are the most intriguing and less understood physiological processes affecting the BOLD signal, as they are the result of a wide variety of interacting factors. The use of the response function that best models HR-induced signal changes, called cardiac response function (CRF), is an effective method to reduce HR noise in fMRI. However, current models of physiological noise correction based on CRF, i.e. canonical and individual, either do not take into account variations in HR between subjects, and are thus inadequate for cohorts with varying HR, or require time-consuming quality control of individual physiological recordings and derived CRFs. By analyzing a large cohort of healthy individuals, the results presented in this thesis show that different HRs influence the BOLD signal and their corresponding spectra differently. A further finding is that HR plays an essential role in determining the shape of the CRF. Slower HRs produce a smoothed CRF with a single well-defined maximum, while faster HRs cause a second maximum. Taking advantage of this dependence of the CRF on HR, a novel method is proposed to model HR-induced fluctuations in the BOLD signal more accurately than current approaches of physiological noise correction. This method, called HR-based CRF, consists of two CRFs: one for HRs below 68 bpm and one for HRs above this value. HR-based CRFs can be directly applied to the fMRI data without the time-consuming task of deriving a CRF for each subject while accounting for inter-subject variability in HR response
Mechanisms and outcomes of Autonomous Sensory Meridian Response
People who experience autonomous sensory meridian response (ASMR) report a complex emotional response of calming, tingling sensations that originate around the crown of the head in response to a specific subset of somatosensory and/or audio-visual triggers. Recently, the authenticity of these experiences has been established. This thesis aimed to build on prior work to further characterise both state and trait ASMR in terms of classification, empathic abilities and electrophysiological neural correlates. In Chapter 1 a brief review of the current literature is described, followed by an introductory methodology chapter.
Chapter 3 introduces a novel data-driven tool that is able to capture both state and trait ASMR, whilst also identifying potential respondents who report experiencing ASMR but who would otherwise fail a follow-up confirmation (e.g., negative associated affect). Using this data-driven approach in respondent classification allows a more comprehensive profiling of how participants respond to ASMR stimuli. This raises the potential to better understand mechanisms and broader traits associated with sub-groups of ASMR-responders in the future.
I further unpack the relationship between ASMR and empathy in Chapter 4. Results show that ASMR responders perform better at tasks designed to measure emotion identification capabilities. These findings systematically delineate the relationship between ASMR and empathy and show the importance of investigating subcomponents of the empathic process in order to fully explain the nature of individual differences in empathic response.
In Chapter 5 I sought to provide source-level signatures of oscillatory changes induced by this phenomenon and investigate potential decay effects — oscillatory changes in the absence of self-reported ASMR. Altogether, I showed the robust changes in the patterns of dynamical brain oscillations associated with an ASMR tingling experience. Further, I demonstrated the longlasting effects of ASMR across a wide range of brain regions and oscillatory powers.
Together, I propose a neural model of ASMR based on the principles of stochastic resonance and synchronisation in Chapter 6. Using testable hypotheses, I hope this model builds on prior work and progresses our understanding of the neurological basis of ASMR and the role neural noise in sensory processing in general
Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder.
Although sensory processing challenges have been noted since the first clinical descriptions of autism, it has taken until the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 for sensory problems to be included as part of the core symptoms of autism spectrum disorder (ASD) in the diagnostic profile. Because sensory information forms the building blocks for higher-order social and cognitive functions, we argue that sensory processing is not only an additional piece of the puzzle, but rather a critical cornerstone for characterizing and understanding ASD. In this review we discuss what is currently known about sensory processing in ASD, how sensory function fits within contemporary models of ASD, and what is understood about the differences in the underlying neural processing of sensory and social communication observed between individuals with and without ASD. In addition to highlighting the sensory features associated with ASD, we also emphasize the importance of multisensory processing in building perceptual and cognitive representations, and how deficits in multisensory integration may also be a core characteristic of ASD
Social and Affective Neuroscience of Everyday Human Interaction
This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers
Frontiers in psychodynamic neuroscience
he term psychodynamics was introduced in 1874 by Ernst von Brücke, the renowned German physiologist and Freud’s research supervisor at the University of Vienna. Together with Helmholtz and others, Brücke proposed that all living organisms are energy systems, regulated by the same thermodynamic laws. Since Freud was a student of Brücke and a deep admirer of Helmholtz, he adopted this view, thus laying the foundations for his metapsychology.
The discovery of the Default Network and the birth of Neuropsychoanalysis, twenty years ago, facilitated a deep return to this classical conception of the brain as an energy system, and therefore a return to Freud's early ambition to establish psychology as natural science. Our current investigations of neural networks and applications of the Free Energy Principle are equally ‘psychodynamic’ in Brücke’s original sense of the term.
Some branches of contemporary neuroscience still eschew subjective data and therefore exclude the brain’s most remarkable property – its selfhood – from the field, and many neuroscientists remain skeptical about psychoanalytic methods, theories, and concepts. Likewise, some psychoanalysts continue to reject any consideration of the structure and functions of the brain from their conceptualization of the mind in health and disease. Both cases seem to perpetuate a Cartesian attitude in which the mind is linked to the brain in some equivocal relationship and an attitude that detaches the brain from the body -- rather than considering it an integral part of the complex and dynamic living organism as a whole.
Evidence from psychodynamic neuroscience suggests that Freudian constructs can now be realized neurobiologically. For example, Freud’s notion of primary and secondary processes is consistent with the hierarchical organization of self-organized cortical and subcortical systems, and his description of the ego is consistent with the functions of the Default Network and its reciprocal exchanges with subordinate brain systems. Moreover, thanks to new methods of measuring brain entropy, we can now operationalize the primary and secondary processes and therefore test predictions arising from these Freudian constructs.
All of this makes it possible to deepen the dialogue between neuroscience and psychoanalysis, in ways and to a degree that was unimaginable in Freud's time, and even compared to twenty years ago. Many psychoanalytical hypotheses are now well integrated with contemporary neuroscience. Other Freudian and post-Freudian hypotheses about the structure and function of the mind seem ripe for the detailed and sophisticated development that modern psychodynamic neuroscience can offer.
This Research Topic aims to provide comprehensive coverage of the latest advances in psychodynamic neuroscience and neuropsychoanalysis. Potential authors are invited to submit papers (original research, case reports, review articles, commentaries) that deploy, review, compare or develop the methods and theories of psychodynamic neuroscience and neuropsychoanalysis.
Potential authors include researchers, psychoanalysts, and neuroscientists
The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging
In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species
Neurocomputational Accounts of Choice Variability and Affect during Decision-making
Humans exhibit surprising variability in behaviour, often making different choices under identical conditions. While the outcomes of these choices typically lead to explicit rewards that have been shown to influence subsequent affective states, less well understood is how the brain represents rewards that are intrinsically meaningful to an individual. The first part of this thesis examines the contributions of endogenous fluctuations in brain activity to behaviour. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes but it remains unknown whether such fluctuations also influence high-level cognitive processes including decision making. Using a novel application of real-time functional magnetic resonance imaging, I find that low pre-stimulus brain activity lead to increased occurrences of risky choice. Using computational modeling, I show that greater risk taking is explained by enhanced phasic responses to offers in a decision network. These findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex behaviour. I then examine how the neuroanatomy of the brain in the form of tissue microstructure relates to risk preferences by leveraging on in vivo histology using magnetic resonance imaging. The second part of this thesis investigates how experienced events, such as rewards received following choice, are aggregated into affective states. Despite their relevance to ideas like goal-setting and well-being, little is known about the impact of intrinsic rewards on affective states and their representation in the brain. A reinforcement learning task incorporating a skilled performance component that did not influence payment was developed to examine this. Computational modeling revealed that momentary happiness depended on past extrinsic rewards and also intrinsic rewards related to the experience of successful skilled performance. Individuals for whom intrinsic rewards more strongly influence momentary happiness exhibit stronger ventromedial prefrontal cortex responses for successful skilled performance. These findings show that the ventromedial prefrontal cortex represents the subjective value of intrinsic rewards, and that computational models of mood dynamics provide a tool that can be used to measure implicit values of abstract goods and experiences
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