scholarly journals Inter-individual differences in pain anticipation and pain perception in migraine: Neural correlates of migraine frequency and cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261570
Author(s):  
Gyöngyi Kökönyei ◽  
Attila Galambos ◽  
Natália Kocsel ◽  
Edina Szabó ◽  
Andrea Edit Édes ◽  
...  
Keyword(s):  
Individual Differences ◽  
Allostatic Load ◽  
Pain Perception ◽  
Dehydroepiandrosterone Sulfate ◽  
Brain Regions ◽  
Episodic Migraine ◽  
Neural Activation ◽  
Migraine Frequency ◽  
Two Phases ◽  
The Brain

Previous studies targeting inter-individual differences in pain processing in migraine mainly focused on the perception of pain. Our main aim was to disentangle pain anticipation and perception using a classical fear conditioning task, and investigate how migraine frequency and pre-scan cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio as an index of neurobiological stress response would relate to neural activation in these two phases. Functional Magnetic Resonance Imaging (fMRI) data of 23 participants (18 females; mean age: 27.61± 5.36) with episodic migraine without aura were analysed. We found that migraine frequency was significantly associated with pain anticipation in brain regions comprising the midcingulate and caudate, whereas pre-scan cortisol-to DHEA-S ratio was related to pain perception in the pre-supplementary motor area (pre-SMA). Both results suggest exaggerated preparatory responses to pain or more general to stressors, which may contribute to the allostatic load caused by stressors and migraine attacks on the brain.

scholarly journals The Neural Basis of Shared Preference Learning

2019 ◽  
Author(s):  
Harry Farmer ◽  
Uri Hertz ◽  
Antonia Hamilton
Keyword(s):  
Social Interactions ◽  
Computational Modelling ◽  
Brain Regions ◽  
The Self ◽  
Neural Activation ◽  
Neural Basis ◽  
Daily Lives ◽  
Neural Processes ◽  
Specific Manner ◽  
The Brain

AbstractDuring our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each confederate in terms of consistency of the similarity to the self, with respect to the choices they have made before (consistency account). Our study combined fMRI and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one confederate who usually shared their preference and another who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Data showed more brain regions whose activity pattern fits with the consistency account, specifically, areas linked to reward and social cognition. Our findings suggest that impressions of other people can be calculated in a person-specific manner which assumes that each individual behaves consistently with their past choices.

scholarly journals Migraine in Multiple Sclerosis Patients Affects Functional Connectivity of the Brain Circuitry Involved in Pain Processing

2021 ◽  
Vol 12 ◽  
Author(s):  
Emanuele Pravatà ◽  
Gianna C. Riccitelli ◽  
Carlo Sestieri ◽  
Rosaria Sacco ◽  
Alessandro Cianfoni ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Pain Perception ◽  
Brain Regions ◽  
Daily Activities ◽  
Control Group ◽  
Resting State Functional Connectivity ◽  
Brain Circuitry ◽  
Multiple Sclerosis Patients ◽  
The Brain

Migraine is particularly common in patients with multiple sclerosis (MS) and has been linked to the dysfunction of the brain circuitry modulating the peripheral nociceptive stimuli. Using MRI, we explored whether changes in the resting state-functional connectivity (RS-FC) may characterize the occurrence of migraine in patients with MS. The RS-FC characteristics in concerned brain regions were explored in 20 MS patients with migraine (MS+M) during the interictal phase, and compared with 19 MS patients without migraine (MS-M), which served as a control group. Functional differences were correlated to the frequency and severity of previous migraine attacks, and with the resulting impact on daily activities. In MS+M, the loss of periaqueductal gray matter (PAG) positive connectivity with the default mode network and the left posterior cranial pons was associated with an increase of migraine attacks frequency. In contrast, the loss of PAG negative connectivity with sensorimotor and visual network was linked to migraine symptom severity and related daily activities impact. Finally, a PAG negative connection was established with the prefrontal executive control network. Migraine in MS+M patients and its impact on daily activities, underlies RS-FC rearrangements between brain regions involved in pain perception and modulation.

scholarly journals The PICO Study: Impact of Phenylalanine on Cognitive, Cerebral and Neurometabolic Parameters in Adult Patients with Phenylketonuria – a Randomized, Placebo-Controlled, Crossover, Non-inferiority Trial

2019 ◽  
Author(s):  
Roman Trepp ◽  
Raphaela Muri ◽  
Lenka Bosanska ◽  
Stephanie Abgottspon ◽  
Michel Hochuli ◽  
...  
Keyword(s):  
Working Memory ◽  
Memory Task ◽  
Brain Regions ◽  
Primary Objective ◽  
Adult Patients ◽  
Target Range ◽  
Neural Activation ◽  
Level Of Evidence ◽  
Double Blind ◽  
The Brain

Abstract Background The population of adult patients with early-treated phenylketonuria (PKU) following newborn screening is growing substantially. The ideal target range of blood Phe levels in adults outside pregnancy is discussed controversially. Therefore, prospective intervention studies are needed to evaluate the effects of an elevated Phe concentration on cognition and structural, functional and neurometabolic parameters of the brain. Methods The PICO (Phenylalanine and Its Impact on Cognition) Study evaluates the effect of a 4-week phenylalanine (Phe) load on cognition and cerebral parameters in 30 adults with early-treated PKU in a double-blind, randomized, placebo-controlled, crossover, non-inferiority trial. The primary objective of the PICO Study is to prospectively assess whether a temporarily elevated Phe level influences cognitive performance in adults with early-treated PKU. As secondary objective, the PICO Study will elucidate cerebral and neurometabolic mechanisms, which accompany changes in Phe concentration using advanced neuroimaging methods. In addition to the intervention study, cognition, structural and functional parameters of the brain of adult patients with early-treated PKU will be cross-sectionally compared to healthy controls, who will be comparable with regard to age, gender and education level. Advanced MR-techniques will be used to investigate intensity of neural activation during the working memory task (fMRI), strength of functional connectivity between brain regions related to performance in working memory (rsfMRI), white matter integrity (DTI), cerebral blood flow (ASL) and brain Phe concentrations (MRS). Discussion Using a combination of neuropsychological and neuroimaging data, the PICO study will considerably contribute to improve the currently insufficient level of evidence on how adult patients with early-treated PKU should be managed.

scholarly journals Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns

2020 ◽  
Vol 14 ◽  
Author(s):  
Richard Huskey ◽  
Benjamin O. Turner ◽  
René Weber
Keyword(s):  
Individual Differences ◽  
Brain Activity ◽  
Brain Regions ◽  
Future Research ◽  
Message Processing ◽  
Response Patterns ◽  
Message Tailoring ◽  
Brain Responses ◽  
And Behavior ◽  
The Brain

Prevention neuroscience investigates the brain basis of attitude and behavior change. Over the years, an increasingly structurally and functionally resolved “persuasion network” has emerged. However, current studies have only identified a small handful of neural structures that are commonly recruited during persuasive message processing, and the extent to which these (and other) structures are sensitive to numerous individual difference factors remains largely unknown. In this project we apply a multi-dimensional similarity-based individual differences analysis to explore which individual factors—including characteristics of messages and target audiences—drive patterns of brain activity to be more or less similar across individuals encountering the same anti-drug public service announcements (PSAs). We demonstrate that several ensembles of brain regions show response patterns that are driven by a variety of unique factors. These results are discussed in terms of their implications for neural models of persuasion, prevention neuroscience and message tailoring, and methodological implications for future research.

scholarly journals A graph neural network framework for causal inference in brain networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
S. Wein ◽  
W. M. Malloni ◽  
A. M. Tomé ◽  
S. M. Frank ◽  
G. -I. Henze ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Structural Information ◽  
Brain Networks ◽  
Scale Up ◽  
Brain Regions ◽  
Neural Activation ◽  
Dynamic Interactions ◽  
The Brain

AbstractA central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

scholarly journals Resting state EEG in youth with ASD: age, sex, and relation to phenotype

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Emily Neuhaus ◽  
Sarah J. Lowry ◽  
Megha Santhosh ◽  
Anna Kresse ◽  
Laura A. Edwards ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Repetitive Behavior ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Neural Activation ◽  
Alpha Power ◽  
Linear Regression Models ◽  
Biological Sex ◽  
Research Risks

Abstract Background Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy. Methods We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations. Results Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD. Conclusions Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

scholarly journals Phox2a defines a developmental origin of the anterolateral system in mice and humans

2020 ◽  
Author(s):  
R. Brian Roome ◽  
Farin B. Bourojeni ◽  
Bishakha Mona ◽  
Shima Rastegar-Pouyani ◽  
Raphael Blain ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Essential Role ◽  
Pain Perception ◽  
Brain Regions ◽  
Developmental Expression ◽  
Spinal Neurons ◽  
Molecular Identity ◽  
Nervous System Function ◽  
The Brain ◽  
Specific Contribution

SummaryAnterolateral system neurons relay pain, itch and temperature information from the spinal cord to pain-related brain regions, but the differentiation of these neurons and their specific contribution to pain perception remain poorly defined. Here, we show that virtually all mouse spinal neurons that embryonically express the autonomic system-associated Paired-like homeobox 2A (Phox2a) transcription factor innervate nociceptive brain targets, including the parabrachial nucleus and the thalamus. We define Phox2a anterolateral system neuron birth order, migration and differentiation, and uncover an essential role for Phox2a in the development of relay of nociceptive signals from the spinal cord to the brain. Finally, we also demonstrate that the molecular identity of Phox2a neurons is conserved in the human foetal spinal cord. The developmental expression of Phox2a as a uniting feature of anterolateral system neurons suggests a link between nociception and autonomic nervous system function.

Individual Differences in the Effects of Perceived Controllability on Pain Perception: Critical Role of the Prefrontal Cortex

2007 ◽  
Vol 19 (6) ◽  
pp. 993-1003 ◽  
Author(s):  
Tim V. Salomons ◽  
Tom Johnstone ◽  
Misha-Miroslav Backonja ◽  
Alexander J. Shackman ◽  
Richard J. Davidson
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
Pain Perception ◽  
Critical Role ◽  
Anterior Cingulate ◽  
Emotional Impact ◽  
Neural Activation ◽  
Posterior Insula ◽  
Cognitive Modulation ◽  
The Impact

The degree to which perceived controllability alters the way a stressor is experienced varies greatly among individuals. We used functional magnetic resonance imaging to examine the neural activation associated with individual differences in the impact of perceived controllability on self-reported pain perception. Subjects with greater activation in response to uncontrollable (UC) rather than controllable (C) pain in the pregenual anterior cingulate cortex (pACC), periaqueductal gray (PAG), and posterior insula/SII reported higher levels of pain during the UC versus C conditions. Conversely, subjects with greater activation in the ventral lateral prefrontal cortex (VLPFC) in anticipation of pain in the UC versus C conditions reported less pain in response to UC versus C pain. Activation in the VLPFC was significantly correlated with the acceptance and denial subscales of the COPE inventory [Carver, C. S., Scheier, M. F., & Weintraub, J. K. Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56, 267–283, 1989], supporting the interpretation that this anticipatory activation was associated with an attempt to cope with the emotional impact of uncontrollable pain. A regression model containing the two prefrontal clusters (VLPFC and pACC) predicted 64% of the variance in pain rating difference, with activation in the two additional regions (PAG and insula/SII) predicting almost no additional variance. In addition to supporting the conclusion that the impact of perceived controllability on pain perception varies highly between individuals, these findings suggest that these effects are primarily top-down, driven by processes in regions of the prefrontal cortex previously associated with cognitive modulation of pain and emotion regulation.

scholarly journals Mapping individual differences across brain network structure to function and behavior with connectome embedding

2021 ◽  
Author(s):  
Gidon Levakov ◽  
Joshua Faskowitz ◽  
Galia Avidan ◽  
Olaf Sporns
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Predictive Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Global Network ◽  
Age Related ◽  
And Behavior ◽  
The Brain

AbstractThe connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

scholarly journals An fMRI-based brain marker predicts individual differences in delay discounting

2021 ◽  
Author(s):  
Leonie Koban ◽  
Sangil Lee ◽  
Daniela S. Schelski ◽  
Marie-Christine Simon ◽  
Caryn Lerman ◽  
...  
Keyword(s):  
Individual Differences ◽  
Delay Discounting ◽  
Cross Validation ◽  
Intertemporal Choice ◽  
Brain Regions ◽  
Life Outcomes ◽  
Data Set ◽  
Future Studies ◽  
Validation Set ◽  
The Brain

Individual differences in impatience - how much we discount future compared to immediate rewards - are associated with general life outcomes and related to substance use, psychiatric diseases, and obesity. Here, we use machine-learning on fMRI activity during an intertemporal choice task to develop a brain marker of individual differences in delay discounting. Study 1 (N=110) was used as a training and cross-validation set, resulting in significant prediction accuracy (r = 0.49) and suggesting an interplay between brain regions associated with affect, value, and cognitive control. The validity of the brain marker was replicated in an independent data set (Study 2, N=145, r = 0.45). In both studies, responses of the marker significantly differed between overweight and lean individuals. This pattern is a first step towards a generalizable neuromarker of delay discounting and a potentially transdiagnostic phenotype, which can be used as a brain-based target measure in future studies.

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