Biology subjects

Seymour, B.

Publications and source records attributed to Seymour, B..

5 recordsLinked to original sources

bioRxiv2018

Endogenous controllability of closed-loop brain-machine interfaces for pain.

The ultimate aim of closed-loop brain-machine systems for pain is to directly titrate the ongoing level of an intervention to pain-related neural activity. However pain is highly susceptible to endogenous modulation, raising the possibility that active or passive changes in neural activity provoked by the operation of the system could enhance or interfere with the signals upon which it is based. We studied healthy subjects receiving intermittent pain stimuli in a real-time fMRI-based closed-loop feedback-stimulation task. We showed that multi-voxel pattern decoding of pain intensity could be used to train a control algorithm to learn to deliver less painful stimuli (adaptive decoded neurofeedback). However, the system engaged two types of endogenous processes in the brain. First, despite the inherent incentive for subjects to enhance the neural decodability of pain, decodability was either reduced or unchanged in classic pain-processing regions, including insula, dorsolateral prefrontal, and somatosensory cortices. However, increased decodability was observed in a putative pain modulatory region - the pregenual anterior cingulate cortex (pgACC). Second, we found that pain perception itself was modulated by an endogenous computational uncertainty signal engaged as subjects learned the success rate of the system in reducing pain - an effect that also correlated with pgACC responses. The results illustrate how regionally and computationally specific co-adaptive brain-machine learning influences the efficacy of closed-loop systems for pain, and shows that pgACC acts as a key hub in the endogenous controllability of pain.

neuroscience↗

bioRxiv2017

A prediction model of working memory across health and psychiatric disease using whole-brain functional connectivity

Individual differences in cognitive function have been shown to correlate with brain-wide functional connectivity, suggesting a common foundation relating connectivity to cognitive function across healthy populations. However, it remains unknown whether this relationship is preserved in cognitive deficits seen in a range of psychiatric disorders. Using machine learning methods, we built a prediction model of working memory function from whole-brain functional connectivity among a healthy population (N = 17, age 19-24 years). We applied this normative model to a series of independently collected resting state functional connectivity datasets (N = 968), involving multiple psychiatric diagnoses, sites, ages (18-65 years), and ethnicities. We found that predicted working memory ability was correlated with actually measured working memory performance in both schizophrenia patients (partial correlation,{rho} = 0.25, P = 0.033, N = 58) and a healthy population (partial correlation,{rho} = 0.11, P = 0.0072, N = 474). Moreover, the model predicted diagnosis-specific severity of working memory impairments in schizophrenia (N = 58, with 60 controls), major depressive disorder (N = 77, with 63 controls), obsessive-compulsive disorder (N = 46, with 50 controls), and autism spectrum disorder (N = 69, with 71 controls) with effect sizes g = -0.68, -0.29, -0.19, and 0.09, respectively. According to the model, each diagnosiss working memory impairment resulted from the accumulation of distinct functional connectivity differences that characterizes each diagnosis, including both diagnosis-specific and diagnosis-invariant functional connectivity differences. Severe working memory impairment in schizophrenia was related not only with fronto-parietal, but also widespread network changes. Autism spectrum disorder showed greater negative connectivity that related to improved working memory function, suggesting that some non-normative functional connections can be behaviorally advantageous. Our results suggest that the relationship between brain connectivity and working memory function in healthy populations can be generalized across multiple psychiatric diagnoses. This approach may shed new light on behavioral variances in psychiatric disease and suggests that whole-brain functional connectivity can provide an individual quantitative behavioral profile in a range of psychiatric disorders.

neuroscience↗

bioRxiv2017

Anterior cingulate cortex connectivity is associated with suppression of behavior in a rat model of chronic pain

A cardinal feature of persistent pain that follows injury is a general suppression of behavior, in which motivation is inhibited in a way that promotes energy conservation and recuperation. Across species, the anterior cingulate cortex (ACC) is associated with the motivational aspects of phasic pain, but whether it mediates motivational functions in persistent pain is less clear. Using burrowing behavior as an marker of non-specific motivated behavior in rodents, we studied the suppression of burrowing following painful CFA or control injection into the right knee-joint of 37 rats (18 with pain), and examined associated neural connectivity with ultra-high-field resting state functional MRI. We found that connectivity between ACC and the subcortex correlated with the reduction in burrowing behavior observed following the pain manipulation. In a full replication study we confirmed these findings in a group of 44 rats (23 with pain). Across both datasets, reduced burrowing was assosciated with increased connectivity between ACC and subcortical structures including hypothalamic/preoptic nuclei and the bed nucleus of the stria terminalis. Together, the findings implicate ACC connectivity as a robust correlate of the motivational aspect of persistent pain in rodents.

neuroscience↗

bioRxiv2017

Classification and characterisation of brain network changes in chronic back pain: a multicenter study.

Chronic pain is a common and often disabling condition, and is thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Here, we investigated brain network architecture using resting state fMRI data collected from chronic back pain patients in UK and Japan (41 patients, 56 controls). Using a machine learning approach (support vector machine), we found that brain network patterns reliably classified chronic pain patients in a third, independent open data set with an accuracy of 63%, whilst 68% was attained in cross validation of all data. We then developed a deep learning classifier using a conditional variational autoencoder, which also yield yielded 63% generalisation and 68% cross-validation accuracy. Given the existence of reliable network changes, we next studied the graph topology of the network, and found consistent evidence of hub disruption based on clustering and betweenness centrality of brain nodes in pain patients. To examine this in more detail, we developed a multislice modularity algorithm to identify a consensus pattern of modular reorganisation of brain nodes across the entire data set. This revealed evidence of significant changes in the modular identity of several brain regions, most notably including broad regions of bilateral sensorimotor cortex, subregions of which also contributed to classifier performance. These results provide evidence of consistent and characteristic brain network changes in chronic pain, and highlight extensive reorganisaton of the network architecture of sensorimotor cortex.

neuroscience↗

bioRxiv2017

The Control of Tonic Pain by Active Relief Learning

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning so that the cause of the pain can be reduced if possible. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system also uses an uncertainty ( associability) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that allows reduction of ongoing pain when learning about potential relief.

neuroscience↗