Biology subjects

Breakspear, M.

Publications and source records attributed to Breakspear, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

A snapshot of brain and cognition in healthy mid-life and older adults

Deficits in memory are seen as a canonical sign of ageing and a prodrome to dementia in older adults. However, the nature of cognitive and brain changes across a wider aperture of adulthood is not well known. We quantify the relationship between cognitive function and brain morphology from mid-life to older adulthood, and the influence of age, sex, amyloid and genetic risk for dementia. We analyzed three observational cohorts (PISA, AIBL, ADNI) with cognitive, genetic and neuroimaging measures comprising a total of 1570 healthy mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment or Alzheimers disease. Among healthy adults, we find robust modes of co-variation between regional sulcal width and multidomain cognitive function that change from mid-life to the older age range. The most prominent cortical changes in mid-life are predominantly associated with changes in executive functions, whereas they are most strongly associated with poorer memory function in older age. These cognitive changes are accompanied by an age-dependent pattern of sulcal widening. Amyloid exerts a weak, but significant, influence on cognition, but not on sulcal width. The APOE {varepsilon}4 allele also exerts a weak influence on cognition, but only significantly in the (larger and older) AIBL cohort. These findings provide new insights into brain and cognition in mid-life and older adults, suggesting that cognitive screening in mid-life cohorts should encompass executive functions as well as memory.

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bioRxiv2022

Critical-like bistable dynamics in the resting-state human brain

Brain activity exhibits scale-free avalanche dynamics and power-law long-range temporal correlations (LRTCs) across the nervous system. This has been thought to reflect "brain criticality", i.e., brains operating near a critical phase transition between disorder and excessive order. Neuronal activity is, however, metabolically costly and may be constrained by activity-limiting mechanisms and resource depletion, which could make the phase transition discontinuous and bistable. Observations of bistability in awake human brain activity have nonetheless remained scarce and its functional significance unclear. First, using computational modelling where bistable synchronization dynamics emerged through local positive feedback, we found bistability to occur exclusively in a regime of critical-like dynamics. We then assessed bistability in vivo with resting-state magnetoencephalography and stereo-encephalography. Bistability was a robust characteristic of cortical oscillations throughout frequency bands from {delta} (3-7 Hz) to high-{gamma} (100-225 Hz). As predicted by modelling, bistability and LRTCs were positively correlated. Importantly, while moderate levels of bistability were positively correlated with executive functioning, excessive bistability was associated with epileptic pathophysiology and predictive of local epileptogenicity. Critical bistability is thus a salient feature of spontaneous human brain dynamics in awake resting-state and is both functionally and clinically significant. These findings expand the framework of brain criticality and show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.

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bioRxiv2020

Insula cortex gates the interplay of action observation and preparation for controlled imitation

Perceiving, anticipating and responding to the actions of another person are fundamentally entwined processes such that seeing anothers movement can prompt automatic imitation, as in social mimicry and contagious yawning. Yet the direct-matching of others movements is not always appropriate, so this tendency must be controlled. This necessitates the hierarchical integration of the systems for action mirroring with domain-general control networks. Here we use functional magnetic resonance imaging (fMRI) and computational modelling to examine the top-down and context-dependent modulation of mirror representations and their influence on motor planning. Participants performed actions that either intentionally or incidentally imitated, or counter-imitated, an observed action. Analyses of these fMRI data revealed a region in the mid-occipital gyrus (MOG) where activity differed between imitation versus counter-imitation in a manner that depended on whether this was intentional or incidental. To identify broader cortical network mechanisms underlying this interaction between intention and imitativeness, we used dynamic causal modelling to pose specific hypotheses which embody assumptions about inter-areal interactions and contextual modulations. These models each incorporated four regions - medial temporal V5 (early motion perception), MOG (action-observation), supplementary motor area (action planning), and anterior insula (executive control) - but differ in their interactions and hierarchical structure. The best model of our data afforded a crucial role for the anterior insula, gating the interaction of supplementary motor area and MOG activity. This provides a novel brain network-based account of task-dependent control over the integration of motor planning and mirror systems, with mirror responses suppressed for intentional counter-imitation.

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bioRxiv2020

Data-driven analysis of facial thermal responses to an emotional movie reveals consistent stimulus-locked physiological changes

Facial infra-red imaging (IRI) is a contact-free technique complimenting the traditional psychophysiological measures to characterize physiological profile. However, its full potential in affective research is arguably unmet due to the analytical challenges it poses. Here we acquired facial IRI data, facial expressions and traditional physiological recordings (heart rate and skin conductance) from healthy human subjects whilst they viewed a 20-minute-long unedited emotional movie. We present a novel application of motion correction and the results of spatial independent component analysis of the thermal data. Three distinct spatial components are recovered associated with the nose, the cheeks and a respiratory component. We first benchmark this methodology against a traditional region-of-interest based technique. We then show significant correlation of all the physiological responses across subjects, including the thermal signals, suggesting common dynamic shifts in emotional state induced by the movie. Finally, we show that thermal responses were significantly anti-correlated with the positive emotional content of the movie thus an index of emotionally-driven physiological response. In sum, this study introduces an innovative approach to analyse facial IRI data and highlights the potential of thermal imaging to robustly capture emotion-related changes in ecological contexts.

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bioRxiv2020

Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

Brain network modeling studies are often limited with respect to the number of data features fitted, although capturing multiple empirical features is important to validate the models overall biological plausibility. Here we construct personalized models from multimodal data of 50 healthy individuals (18-80 years) with The Virtual Brain and demonstrate that an individuals brain has its own converging optimal working point in the parameter space that predicts multiple empirical features in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We further show that bimodality in the alpha band power - as an explored novel feature - arises as a function of global coupling and exhibits inter-regional differences depending on the degree. Reliable inter-individual differences with respect to these optimal working points were found that seem to be driven by the individual structural rather than by the functional connectivity. Our results provide the groundwork for future multimodal brain modeling studies.

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bioRxiv2020

Intracranial-EEG evidence for medial temporal pole driving amygdala activity in multi-modal emotional stimuli

The temporal pole (TP) is an associative cortical region required for complex cognitive functions such as social and emotional cognition. However, functional mapping of the TP with functional magnetic resonance imaging is technically challenging and thus understanding of its interaction with other key emotional circuitry, such as the amygdala, remain elusive. We exploited the unique advantages of stereo-electroencephalography (SEEG) to assess the responses of the TP and the amygdala during the perception of emotionally salient stimuli of pictures, music and movies. These stimuli consistently elicited high gamma responses (70-140 Hz) in both the TP and the amygdala, accompanied by functional connectivity in the low frequency range (2-12 Hz). Computational analyses suggested the TP driving this effect in the theta-alpha frequency range and which was modulated by the emotional salience of the stimuli. Of note, cross-frequency analysis indicated the phase of theta-alpha oscillations in the TP modulated the amplitude of high gamma activity in the amygdala. These results were reproducible with three types of stimuli including naturalistic stimuli suggesting a hierarchical influence of the TP over the amygdala in non-threatening stimuli.

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bioRxiv2020

Hierarchical organization of the human subcortex unveiled with functional connectivity gradients

Understanding the topographic organization of the human brain remains a major goal in neuroscience. Brain atlases are fundamental to this goal, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We revealed the complex topographic organization of the human subcortex by disambiguating smooth connectivity gradients from discrete areal boundaries in resting-state fMRI data acquired from more than 1000 healthy adults. This unveiled four scales of subcortical organization, recapitulating well-known anatomical nuclei at the coarsest scale and delineating 27 new bilateral regions at the finest. Ultra-high field strength fMRI corroborated and extended this organizational structure, enabling delineation of finer subdivisions of hippocampus and amygdala, while task-evoked fMRI revealed a subtle reorganization of subcortical topography in response to changing cognitive demands. A new subcortical atlas was delineated, personalized to account for individual connectivity differences and utilized to uncover reproducible relationships between subcortical connectivity and individual variation in human behaviors. Linking cortical networks to subcortical regions recapitulated a task-positive to task-negative organizational axis. The new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.

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bioRxiv2019

Automated cot-side tracking of functional brain age in preterm infants with routine EEG monitoring

ObjectiveA major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely-available neurological monitoring with electroencephalography (EEG). MethodsWe used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome. ResultsThe FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well-defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome. InterpretationThe FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.

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bioRxiv2019

Extensive functional repertoire underpins complex behaviours: insights from Parkinson’s disease

Rapid reconfigurations of brain activity support efficient neuronal communication and flexible behaviour. Suboptimal brain dynamics impair this adaptability, possibly leading to functional deficiencies. We hypothesize that impaired flexibility in brain activity can lead to motor and cognitive symptoms of Parkinsons disease (PD). To test this hypothesis, we studied the functional repertoire - the number of distinct configurations of neural activity - using source-reconstructed magnetoencephalography in PD patients and controls. We found stereotyped brain dynamics and reduced flexibility in PD. The intensity of this reduction was proportional to symptoms severity, which can be explained by beta-band hyper-synchronization. Moreover, the basal ganglia were prominently involved in the abnormal patterns of brain activity. Our findings support the hypotheses that: symptoms in PD reflect impaired brain flexibility, this impairment preferentially involves the basal ganglia, and beta-band hypersynchronization is associated with reduced brain flexibility. These findings highlight the importance of extensive functional repertoires for behaviour and motor.

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bioRxiv2019

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

A key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a critical transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.\n\nAuthor summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition.

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bioRxiv2018

Subjective estimates of uncertainty and volatility during gambling predict impulsivity after subthalamic deep brain stimulation for Parkinson’s disease

1Subthalamic deep brain stimulation (STN-DBS) for Parkinsons disease may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether STN-DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, thirty-eight participants with Parkinsons disease played a virtual casino prior to subthalamic DBS (whilst on medication) and again, three-months postoperatively (whilst on stimulation). At the group level, there was a small but statistically significant postoperative decrease in impulsivity, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participants estimate of the tendency of the slot machines winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in persons with Parkinsons disease may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.

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bioRxiv2018

Prediction-error signals to violated expectations about person identity and head orientation are doubly-dissociated across the dorsal and ventral visual streams

Predictive coding theories of perception suggest the importance of constantly updated internal models of the world in predicting future sensory inputs. One implication of such models is that cortical regions whose function is to resolve particular stimulus attributes should also signal prediction violations with respect to those same stimulus attributes. Previously, through carefully designed experiments, we have demonstrated early-mid latency EEG/MEG prediction-error signals in the dorsal visual stream to violated expectations about stimulus orientation/trajectory, with localisations consistent with cortical areas processing motion and orientation. Here we extend those methods to simultaneously investigate the predictive processes in both dorsal and ventral visual streams. In this MEG study we employed a contextual trajectory paradigm that builds expectations using a series of image presentations. We created expectations about both face orientation and identity, either of which can subsequently be violated. Crucially this paradigm allows us to parametrically test double dissociations between these different types of violations. The study identified double dissociations across the type of violation in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater evidence of prediction-error signals to Identity violations than to Orientation violations, whereas the left angular gyrus and postcentral gyrus showed the opposite pattern of results. Our results suggest comparable processes for error checking and context updating in high-level expectations instantiated across both perceptual streams. Perceptual prediction-error signalling is initiated in regions associated with the processing of different stimulus properties.\n\nSignificance StatementVisual processing occurs along what and where information streams that run, respectively along the ventral and dorsal surface of the posterior brain. Predictive coding models of perception imply prediction-error detection processes that are instantiated at the level where particular stimulus attributes are parsed. This implies that, for instance, when considering face stimuli, signals arising through violated expectations about the person identity of the stimulus should localise to the ventral stream, whereas signals arising through violated expectations about head orientation should localise to the dorsal stream. We test this in a magnetoencephalography source localisation study. The analysis confirmed that prediction-error signals to identity versus head-orientation occur with similar latency, but activate doubly-dissociated brain regions along ventral and dorsal processing streams.

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bioRxiv2018

Metastable brain waves

Traveling patterns of neuronal activity - brain waves - have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

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bioRxiv2018

Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models

Alzheimers disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, \"The Virtual Brain (TVB)\", based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioral spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

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bioRxiv2018

The low dimensional dynamic and integrative core of cognition in the human brain

The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.\n\nOne Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

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bioRxiv2017

Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR (n=84, mean age 22.4) individuals, patients with BD (n=38, mean age 23.9), and age- and gender-matched controls (n=96, mean age 22.6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippocampal, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula (p=0.004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus (p=0.008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions.\n\nHighlightsO_LIControl theory estimates how neuronal fluctuations spread from local networks.\nC_LIO_LIWe compare brain controllability in bipolar disorder and their high-risk relatives.\nC_LIO_LIThese groups have impaired controllability in networks supporting cognitive and emotional control.\nC_LIO_LIWeaker connectivity as well as topological alterations contribute to these changes.\nC_LI

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bioRxiv2017

The Brain Dynamics Toolbox for Matlab

The Brain Dynamics Toolbox provides a graphical tool for simulating user-defined dynamical systems in O_SCPLOWMATLABC_SCPLOW. It supports three classes of differential equations commonly used in computational neuroscience: Ordinary Differential Equations, Delay Differential Equations and Stochastic Differential Equations. The design of the graphical interface fosters intuitive exploration of the dynamics, yet there is no barrier to scripting large-scale simulations and parameter explorations. System variables and parameters may range in size from simple scalars to large vectors or matrices. The toolbox is intended for dynamical models in computational neuroscience but it can be applied to continuous dynamical systems from any domain.

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bioRxiv2017

Brain-behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder

BackgroundChildhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD.\n\nMethodsWe first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naive adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the individual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype.\n\nResultsAdults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain-symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD.\n\nConclusionsOverall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain-symptom mediators.

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