Human Brain Mapping

Journal: Human Brain Mapping

Abbreviation

Hum Brain Mapp

Publisher

Wiley

ISSN

1097-0193
1065-9471

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Publications 1 - 10 of 16

Awareness of embodiment enhances enjoyment and engages sensorimotor cortices
Moffat, Ryssa; Cross, Emily S. (2024)
Human Brain Mapping
Whether in performing arts, sporting, or everyday contexts, when we watch others move, we tend to enjoy bodies moving in synchrony. Our enjoyment of body movements is further enhanced by our own prior experience with performing those movements, or our ‘embodied experience’. The relationships between movement synchrony and enjoyment, as well as embodied experience and movement enjoyment, are well known. The interaction between enjoyment of movements, synchrony, and embodiment is less well understood, and may be central for developing new approaches for enriching social interaction. To examine the interplay between movement enjoyment, synchrony, and embodiment, we asked participants to copy another person's movements as accurately as possible, thereby gaining embodied experience of movement sequences. Participants then viewed other dyads performing the same or different sequences synchronously, and we assessed participants' recognition of having performed these sequences, as well as their enjoyment of each movement sequence. We used functional near-infrared spectroscopy to measure cortical activation over frontotemporal sensorimotor regions while participants performed and viewed movements. We found that enjoyment was greatest when participants had mirrored the sequence and recognised it, suggesting that awareness of embodiment may be central to enjoyment of synchronous movements. Exploratory analyses of relationships between cortical activation and enjoyment and recognition implicated the sensorimotor cortices, which subserve action observation and aesthetic processing. These findings hold implications for clinical research and therapies seeking to foster successful social interaction.
Supraspinal nociceptive networks in neuropathic pain after spinal cord injury
Huynh, Vincent; Lütolf, Robin; Rosner, Jan; et al. (2021)
Human Brain Mapping
Neuropathic pain following spinal cord injury involves plastic changes along the whole neuroaxis. Current neuroimaging studies have identified grey matter volume (GMV) and resting-state functional connectivity changes of pain processing regions related to neuropathic pain intensity in spinal cord injury subjects. However, the relationship between the underlying neural processes and pain extent, a complementary characteristic of neuropathic pain, is unknown. We therefore aimed to reveal the neural markers of widespread neuropathic pain in spinal cord injury subjects and hypothesized that those with greater pain extent will show higher GMV and stronger connectivity within pain related regions. Thus, 29 chronic paraplegic subjects and 25 healthy controls underwent clinical and electrophysiological examinations combined with neuroimaging. Paraplegics were demarcated based on neuropathic pain and were thoroughly matched demographically. Our findings indicate that (a) spinal cord injury subjects with neuropathic pain display stronger connectivity between prefrontal cortices and regions involved with sensory integration and multimodal processing, (b) greater neuropathic pain extent, is associated with stronger connectivity between the posterior insular cortex and thalamic sub-regions which partake in the lateral pain system and (c) greater intensity of neuropathic pain is related to stronger connectivity of regions involved with multimodal integration and the affective-motivational component of pain. Overall, this study provides neuroimaging evidence that the pain phenotype of spinal cord injury subjects is related to the underlying function of their resting brain.
Regression dynamic causal modeling for resting‐state fMRI
Frässle, Stefan; Harrison, Samuel J.; Heinzle, Jakob; et al. (2021)
Human Brain Mapping
“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI—regression dynamic causal modeling (rDCM)—extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.
Frequency‐dependent functional connectivity in resting state networks
Samogin, Jessica; Marino, Marco; Porcaro, Camillo; et al. (2020)
Human Brain Mapping
Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.
The timing and precision of action prediction in the aging brain
Diersch, Nadine; Jones, Alex L.; Cross, Emily S. (2016)
Human Brain Mapping
Successful social interactions depend on the ability to anticipate other people's actions. Current conceptualizations of brain function propose that causes of sensory input are inferred through their integration with internal predictions generated in the observer's motor system during action observation. Less is known concerning how action prediction changes with age. Previously we showed that internal action representations are less specific in older compared with younger adults at behavioral and neural levels. Here, we characterize how neural activity varies while healthy older adults aged 56–71 years predict the time-course of an unfolding action as well as the relation to task performance. By using fMRI, brain activity was measured while participants observed partly occluded actions and judged the temporal coherence of the action continuation that was manipulated. We found that neural activity in frontoparietal and occipitotemporal regions increased the more an action continuation was shifted backwards in time. Action continuations that were shifted towards the future preferentially engaged early visual cortices. Increasing age was associated with neural activity that extended from posterior to anterior regions in frontal and superior temporal cortices. Lower sensitivity in action prediction resulted in activity increases in the caudate. These results imply that the neural implementation of predicting actions undergoes similar changes as the neural process of executing actions in older adults. The comparison between internal predictions and sensory input seems to become less precise with age leading to difficulties in anticipating observed actions accurately, possibly due to less specific internal action models.
Exploring the relationship between anthropomorphism and Theory-of-Mind in brain and behaviour
Hortensius, Ruud; Kent, Michaela; Darda, Kohinoor M.; et al. (2021)
Human Brain Mapping
The process of understanding the minds of other people, such as their emotions and intentions, is mimicked when individuals try to understand an artificial mind. The assumption is that anthropomorphism, attributing human-like characteristics to non-human agents and objects, is an analogue to theory-of-mind, the ability to infer mental states of other people. Here, we test to what extent these two constructs formally overlap. Specifically, using a multi-method approach, we test if and how anthropomorphism is related to theory-of-mind using brain (Experiment 1) and behavioural (Experiment 2) measures. In a first exploratory experiment, we examine the relationship between dispositional anthropomorphism and activity within the theory-of-mind brain network (n = 108). Results from a Bayesian regression analysis showed no consistent relationship between dispositional anthropomorphism and activity in regions of the theory-of-mind network. In a follow-up, pre-registered experiment, we explored the relationship between theory-of-mind and situational and dispositional anthropomorphism in more depth. Participants (n = 311) watched a short movie while simultaneously completing situational anthropomorphism and theory-of-mind ratings, as well as measures of dispositional anthropomorphism and general theory-of-mind. Only situational anthropomorphism predicted the ability to understand and predict the behaviour of the film's characters. No relationship between situational or dispositional anthropomorphism and general theory-of-mind was observed. Together, these results suggest that while the constructs of anthropomorphism and theory-of-mind might overlap in certain situations, they remain separate and possibly unrelated at the personality level. These findings point to a possible dissociation between brain and behavioural measures when considering the relationship between theory-of-mind and anthropomorphism.
Categorizing human vocal signals depends on an integrated auditory‐frontal cortical network
Roswandowitz, Claudia; Swanborough, Huw; Frühholz, Sascha (2021)
Human Brain Mapping
Voice signals are relevant for auditory communication and suggested to be processed in dedicated auditory cortex (AC) regions. While recent reports highlighted an additional role of the inferior frontal cortex (IFC), a detailed description of the integrated functioning of the AC–IFC network and its task relevance for voice processing is missing. Using neuroimaging, we tested sound categorization while human participants either focused on the higher‐order vocal‐sound dimension (voice task) or feature‐based intensity dimension (loudness task) while listening to the same sound material. We found differential involvements of the AC and IFC depending on the task performed and whether the voice dimension was of task relevance or not. First, when comparing neural vocal‐sound processing of our task‐based with previously reported passive listening designs we observed highly similar cortical activations in the AC and IFC. Second, during task‐based vocal‐sound processing we observed voice‐sensitive responses in the AC and IFC whereas intensity processing was restricted to distinct AC regions. Third, the IFC flexibly adapted to the vocal‐sounds' task relevance, being only active when the voice dimension was task relevant. Forth and finally, connectivity modeling revealed that vocal signals independent of their task relevance provided significant input to bilateral AC. However, only when attention was on the voice dimension, we found significant modulations of auditory‐frontal connections. Our findings suggest an integrated auditory‐frontal network to be essential for behaviorally relevant vocal‐sounds processing. The IFC seems to be an important hub of the extended voice network when representing higher‐order vocal objects and guiding goal‐directed behavior.
A neurocognitive model of perceptual decision-making on emotional signals
Dricu, Mihai; Frühholz, Sascha (2020)
Human Brain Mapping
Humans make various kinds of decisions about which emotions they perceive from others. Although it might seem like a split-second phenomenon, deliberating over which emotions we perceive unfolds across several stages of decisional processing. Neurocognitive models of general perception postulate that our brain first extracts sensory information about the world then integrates these data into a percept and lastly interprets it. The aim of the present study was to build an evidence-based neurocognitive model of perceptual decision-making on others' emotions. We conducted a series of meta-analyses of neuroimaging data spanning 30 years on the explicit evaluations of others' emotional expressions. We find that emotion perception is rather an umbrella term for various perception paradigms, each with distinct neural structures that underline task-related cognitive demands. Furthermore, the left amygdala was responsive across all classes of decisional paradigms, regardless of task-related demands. Based on these observations, we propose a neurocognitive model that outlines the information flow in the brain needed for a successful evaluation of and decisions on other individuals' emotions. Highlights Emotion classification involves heterogeneous perception and decision-making tasks Decision-making processes on emotions rarely covered by existing emotions theories We propose an evidence-based neuro-cognitive model of decision-making on emotions Bilateral brain processes for nonverbal decisions, left brain processes for verbal decisions Left amygdala involved in any kind of decision on emotions
Robotic movement preferentially engages the action observation network
Cross, Emily S.; Liepelt, Roman; Hamilton, Antonia F. de C.; et al. (2012)
Human Brain Mapping
Abstract: As humans, we gather a wide range of information about other people from watching them move. A network of parietal, premotor, and occipitotemporal regions within the human brain, termed the action observation network (AON), has been implicated in understanding others’ actions by means of an automatic matching process that links observed and performed actions. Current views of the AON assume a matching process biased towards familiar actions; specifically, those performed by con- specifics and present in the observer’s motor repertoire. In this study, we test how this network responds to form and motion cues when observing natural human motion compared to rigid robotic- like motion across two independent functional neuroimaging experiments. In Experiment 1, we report the surprising finding that premotor, parietal, occipitotemporal regions respond more robustly to rigid, robot-like motion than natural human motion. In Experiment 2, we replicate and extend this finding by demonstrating that the same pattern of results emerges whether the agent is a human or a robot, which suggests the preferential response to robot-like motion is independent of the agent’s form. These data challenge previous ideas about AON function by demonstrating that the core nodes of this net- work can be flexibly engaged by novel, unfamiliar actions performed by both human and non-human agents. As such, these findings suggest that the AON is sensitive to a broader range of action features beyond those that are simply familiar.
The influence of visual training on predicting complex action sequences
Cross, Emily S.; Stadler, Waltraud; Parkinson, Jim; et al. (2013)
Human Brain Mapping
Linking observed and executable actions appears to be achieved by an action observation network (AON), comprising parietal, premotor, and occipitotemporal cortical regions of the human brain. AON engagement during action observation is thought to aid in effortless, efficient prediction of ongoing movements to support action understanding. Here, we investigate how the AON responds when observing and predicting actions we cannot readily reproduce before and after visual training. During pre- and posttraining neuroimaging sessions, participants watched gymnasts and wind-up toys moving behind an occluder and pressed a button when they expected each agent to reappear. Between scanning sessions, participants visually trained to predict when a subset of stimuli would reappear. Posttraining scanning revealed activation of inferior parietal, superior temporal, and cerebellar cortices when predicting occluded actions compared to perceiving them. Greater activity emerged when predicting untrained compared to trained sequences in occipitotemporal cortices and to a lesser degree, premotor cortices. The occipitotemporal responses when predicting untrained agents showed further specialization, with greater responses within body-processing regions when predicting gymnasts' movements and in object-selective cortex when predicting toys' movements. The results suggest that (1) select portions of the AON are recruited to predict the complex movements not easily mapped onto the observer's body and (2) greater recruitment of these AON regions supports prediction of less familiar sequences. We suggest that the findings inform both the premotor model of action prediction and the predictive coding account of AON function.

Publications 1 - 10 of 16

Journal: Human Brain Mapping

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