Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

AbstractIndividual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

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Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

10.1101/790709 ◽

2019 ◽

Author(s):

Tamas Spisak ◽

Balint Kincses ◽

Frederik Schlitt ◽

Matthias Zunhammer ◽

Tobias Schmidt-Wilcke ◽

...

Keyword(s):

Resting State ◽

Pain Sensitivity ◽

Pain Perception ◽

Brain Connectivity ◽

Brain Activity ◽

Treatment Strategies ◽

Functional Brain ◽

Individual Susceptibility ◽

Brain Connectome ◽

The Individual

AbstractIndividual differences in pain perception are of key interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual susceptibility to pain is reflected in the pain-free resting-state brain activity and functional connectivity.Here, we identified and validated a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature (https://spisakt.github.io/RPN-signature) allows assessing the individual susceptibility to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have broad implications for translational research and the development and assessment of analgesic treatment strategies.

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Effect of Deafferentation from Spinal Anesthesia on Pain Sensitivity and Resting-State Functional Brain Connectivity in Healthy Male Volunteers

Brain Connectivity ◽

10.1089/brain.2014.0247 ◽

2014 ◽

Vol 4 (6) ◽

pp. 404-416 ◽

Cited By ~ 7

Author(s):

Marieke Niesters ◽

Elske Sitsen ◽

Linda Oudejans ◽

Jaap Vuyk ◽

Leon P.H.J. Aarts ◽

...

Keyword(s):

Spinal Anesthesia ◽

Resting State ◽

Pain Sensitivity ◽

Brain Connectivity ◽

Healthy Male ◽

Functional Brain ◽

Healthy Male Volunteers ◽

Male Volunteers

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Association between metabolic syndrome and resting-state functional brain connectivity

Neurobiology of Aging ◽

10.1016/j.neurobiolaging.2021.03.012 ◽

2021 ◽

Author(s):

Barnaly Rashid ◽

Victoria N. Poole ◽

Francesca C. Fortenbaugh ◽

Michael Esterman ◽

William P. Milberg ◽

...

Keyword(s):

Metabolic Syndrome ◽

Resting State ◽

Brain Connectivity ◽

Functional Brain

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Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children

NeuroImage ◽

10.1016/j.neuroimage.2021.118368 ◽

2021 ◽

pp. 118368

Author(s):

Dorine Van Dyck ◽

Nicolas Deconinck ◽

Alec Aeby ◽

Simon Baijot ◽

Nicolas Coquelet ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Procedural Learning ◽

Learning Skills ◽

Functional Brain ◽

School Aged Children

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736 FUNCTIONAL DYSPEPSIA SHOWS ALTERED POST-MEAL RESTING-STATE FUNCTIONAL BRAIN CONNECTIVITY BETWEEN NUCLEUS TRACTUS SOLITARII AND CORTICAL NETWORKS

Gastroenterology ◽

10.1016/s0016-5085(21)01109-4 ◽

2021 ◽

Vol 160 (6) ◽

pp. S-148-S-149

Author(s):

Harrison P. Fisher ◽

Roberta Sclocco ◽

Rowan Staley ◽

Kyungsun Han ◽

April Mendez ◽

...

Keyword(s):

Functional Dyspepsia ◽

Resting State ◽

Brain Connectivity ◽

Nucleus Tractus Solitarii ◽

Cortical Networks ◽

Functional Brain

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Error-related Persistence of Motor Activity in Resting-state Networks

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01323 ◽

2018 ◽

Vol 30 (12) ◽

pp. 1883-1901 ◽

Cited By ~ 2

Author(s):

Nicolò F. Bernardi ◽

Floris T. Van Vugt ◽

Ricardo Ruy Valle-Mena ◽

Shahabeddin Vahdat ◽

David J. Ostry

Keyword(s):

Motor Learning ◽

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Brain Networks ◽

Neural Activation ◽

Resting State Functional Connectivity ◽

Movement Training ◽

Movement Error ◽

Human Participants

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

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Treatment of age-related hearing loss alters audiovisual integration and resting-state functional connectivity: A randomized controlled pilot trial

10.31219/osf.io/48bpt ◽

2021 ◽

Author(s):

Stephanie Rosemann ◽

Anja Gieseler ◽

Maike Tahden ◽

Hans Colonius ◽

Christiane Thiel

Keyword(s):

Hearing Loss ◽

Resting State ◽

Hearing Aid ◽

Brain Connectivity ◽

Audiovisual Integration ◽

Resting State Functional Connectivity ◽

Functional Brain ◽

Hearing Aid Use ◽

Age Related ◽

Age Related Hearing Loss

Untreated age-related hearing loss increases audiovisual integration and impacts resting state functional brain connectivity. It is unclear whether compensation with hearing aids is able to alter audiovisual integration and resting state functional brain connectivity. We conducted a randomized controlled pilot study to investigate how the McGurk illusion, a common measure for audiovisual integration, and resting state functional brain connectivity of the auditory cortex are altered by six-month hearing aid use. Thirty-two older participants with slight-to-moderate, symmetric, age-related hearing loss were allocated to a treatment or waiting control group and measured one week before and six months after hearing aid fitting with functional magnetic resonance imaging. Our results showed that a hearing aid use of six months was associated with a decrease in resting state functional connectivity between the auditory cortex and the fusiform gyrus and that this decrease was related to an increase of perceived McGurk illusions. Our study, therefore, suggests that even short-term hearing aid use alters audiovisual integration and functional brain connectivity between auditory and visual cortices.

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Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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From static to temporal network theory: Applications to functional brain connectivity

Network Neuroscience ◽

10.1162/netn_a_00011 ◽

2017 ◽

Vol 1 (2) ◽

pp. 69-99 ◽

Cited By ~ 28

Author(s):

William Hedley Thompson ◽

Per Brantefors ◽

Peter Fransson

Keyword(s):

Network Theory ◽

Temporal Dynamics ◽

Brain Connectivity ◽

Resting State Fmri ◽

Network Activity ◽

Temporal Network ◽

Functional Brain ◽

Brain Connectome ◽

The Brain ◽

Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

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