55 results found
Hughes SW, Hellyer PJ, Sharp DJ, et al., 2020, Diffusion tensor imaging of lumbar spinal nerves reveals changes in microstructural integrity following decompression surgery associated with improvements in clinical symptoms: A case report, Magnetic Resonance Imaging, Vol: 69, Pages: 65-70, ISSN: 0730-725X
The outcomes from spinal nerve decompression surgery are highly variable with a sizable proportion of elderly foraminal stenosis patients not regaining good pain relief. A better understanding of nerve root compression before and following decompression surgery and whether these changes are mirrored by improvements in symptoms may help to improve clinical decision-making processes. This case study used a combination of diffusion tensor imaging (DTI), clinical questionnaires and motor neurophysiology assessments before and up to 3 months following spinal decompression surgery. In this case report, a 70-year-old women with compression of the left L5 spinal nerve root in the L5-S1 exit foramina was recruited to the study. At 3 months following surgery, DTI revealed marked improvements in left L5 microstructural integrity to a similar level to that seen in the intact right L5 nerve root. This was accompanied by a gradual improvement in pain-related symptoms, mood and disability score by 3 months. Using this novel multimodal approach, it may be possible to track concurrent improvements in pain-related symptoms, function and microstructural integrity of compressed nerves in elderly foraminal stenosis patients undergoing decompression surgery.
Dafflon J, Pinaya WHL, Turkheimer F, et al., 2020, An automated machine learning approach to predict brain age from cortical anatomical measures, HUMAN BRAIN MAPPING, Vol: 41, Pages: 3555-3566, ISSN: 1065-9471
Barry EF, Vanes LD, Andrews DS, et al., 2019, Mapping cortical surface features in treatment resistant schizophrenia with in vivo structural MRI, PSYCHIATRY RESEARCH, Vol: 274, Pages: 335-344, ISSN: 0165-1781
Turkheimer FE, Hellyer P, Kehagia AA, et al., 2019, Conflicting emergences. Weak vs. strong emergence for the modelling of brain function, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 99, Pages: 3-10, ISSN: 0149-7634
Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimensional dynamics remains elusive. Here, we consider Wilson-Cowan networks and demonstrate through numerical and analytical work that homeostatic regulation of the network firing rates can paradoxically lead to a rich dynamical repertoire. The dynamics include mixed-mode oscillations, mixed-mode chaos, and chaotic synchronization when the homeostatic plasticity operates on a moderately slower time scale than the firing rates. This is true for a single recurrently coupled node, pairs of reciprocally coupled nodes without self-coupling, and networks coupled through experimentally determined weights derived from functional magnetic resonance imaging data. In all cases, the stability of the homeostatic set point is analytically determined or approximated. The dynamics at the network level are directly determined by the behavior of a single node system through synchronization in both oscillatory and non-oscillatory states. Our results demonstrate that rich dynamics can be preserved under homeostatic regulation or even be caused by homeostatic regulation.When recordings from the brain are analyzed, rich dynamics such as oscillations or low-dimensional chaos are often present. However, a general mechanism for how these dynamics emerge remains unresolved. Here, we explore the potential that these dynamics are caused by an interaction between synaptic homeostasis, and the connectivity between distinct populations of neurons. Using both analytical and numerical approaches, we analyze how data derived connection weights interact with inhibitory synaptic homeostasis to create rich dynamics such chaos and oscillations operating on multiple time scales. We demonstrate that these rich dynamical states are present in simple systems such as single population of neurons
arora H, nila A, Vitharana K, et al., 2017, Microstructural consequences of blast lung injury characterised with digital volume correlation, Frontiers in Materials, Vol: 4, ISSN: 2296-8016
This study focuses on microstructural changes that occur within the mammalian lung when subject to blast and how these changes influence strain distributions within the tissue. Shock tube experiments were performed to generate the blast injured specimens (cadaveric Sprague-Dawley rats). Blast overpressures of 100 and 180 kPa were studied. Synchrotron tomography imaging was used to capture volumetric image data of lungs. Specimens were ventilated using a custom-built system to study multiple inflation pressures during each tomography scan. These data enabled the first digital volume correlation (DVC) measurements in lung tissue to be performed. Quantitative analysis was performed to describe the damaged architecture of the lung. No clear changes in the microstructure of the tissue morphology were observed due to controlled low- to moderate-level blast exposure. However, significant focal sites of injury were observed using DVC, which allowed the detection of bias and concentration in the patterns of strain level. Morphological analysis corroborated the findings, illustrating that the focal damage caused by a blast can give rise to diffuse influence across the tissue. It is important to characterize the non-instantly fatal doses of blast, given the transient nature of blast lung in the clinical setting. This research has highlighted the need for better understanding of focal injury and its zone of influence (alveolar interdependency and neighboring tissue burden as a result of focal injury). DVC techniques show great promise as a tool to advance this endeavor, providing a new perspective on lung mechanics after blast.
De Simoni S, Jenkins PO, Bourke N, et al., 2017, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, Brain, Vol: 141, Pages: 148-164, ISSN: 1460-2156
Traumatic brain injury often produces executive dysfunction. This characteristic cognitive impairment often causes long-term problems with behaviour and personality. Frontal lobe injuries are associated with executive dysfunction, but it is unclear how these injuries relate to corticostriatal interactions that are known to play an important role in behavioural control. We hypothesized that executive dysfunction after traumatic brain injury would be associated with abnormal corticostriatal interactions, a question that has not previously been investigated. We used structural and functional MRI measures of connectivity to investigate this. Corticostriatal functional connectivity in healthy individuals was initially defined using a data-driven approach. A constrained independent component analysis approach was applied in 100 healthy adult dataset from the Human Connectome Project. Diffusion tractography was also performed to generate white matter tracts. The output of this analysis was used to compare corticostriatal functional connectivity and structural integrity between groups of 42 patients with traumatic brain injury and 21 age-matched controls. Subdivisions of the caudate and putamen had distinct patterns of functional connectivity. Traumatic brain injury patients showed disruption to functional connectivity between the caudate and a distributed set of cortical regions, including the anterior cingulate cortex. Cognitive impairments in the patients were mainly seen in processing speed and executive function, as well as increased levels of apathy and fatigue. Abnormalities of caudate functional connectivity correlated with these cognitive impairments, with reductions in right caudate connectivity associated with increased executive dysfunction, information processing speed and memory impairment. Structural connectivity, measured using diffusion tensor imaging between the caudate and anterior cingulate cortex was impaired and this also correlated with measures of ex
Hellyer P, Clopath C, Kehagia A, et al., 2017, From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction, PLoS Computational Biology, Vol: 13, ISSN: 1553-734X
In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional “task negative” activity that compensates for “task positive”, sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic “task-negative” patterns of activity (e.g., the default mode network).
Feeney C, Sharp DJ, Hellyer PJ, et al., 2017, Serum IGF-I levels are associated with improved white matter recovery after TBI., Annals of Neurology, Vol: 82, Pages: 30-43, ISSN: 0364-5134
OBJECTIVE: Traumatic brain injury (TBI) is a common disabling condition with limited treatment options. Diffusion tensor imaging (DTI) measures recovery of axonal injury in white matter (WM) tracts after TBI. Growth hormone deficiency (GHD) after TBI may impair axonal and neuropsychological recovery, and serum IGF-I may mediate this effect. We conducted a longitudinal study to determine the effects of baseline serum IGF-I concentrations on WM tract and neuropsychological recovery after TBI. METHODS: Thirty-nine adults after TBI (84.6% male; age median 30.5y; 87.2% moderate-severe; time since TBI median 16.3 months, n=4 with GHD) were scanned twice, 13.3 months (12.1-14.9) apart, and 35 healthy controls scanned once. Symptom and quality of life questionnaires and cognitive assessments were completed at both visits (n=33). Our main outcome measure was fractional anisotropy (FA), a measure of WM tract integrity, in a priori regions of interest: splenium of corpus callosum (SPCC), and posterior limb of internal capsule (PLIC). RESULTS: At baseline, FA was reduced in many WM tracts including SPCC and PLIC following TBI compared to controls, indicating axonal injury, with longitudinal increases indicating axonal recovery. There was a significantly greater increase in SPCC FA over time in patients with serum IGF-I above vs. below the median-for-age. Only the higher IGF-I group had significant improvements in immediate verbal memory recall over time. INTERPRETATION: WM recovery and memory improvements after TBI were greater in patients with higher serum IGF-I at baseline. These findings suggest that GH/IGF-I system may be a potential therapeutic target following TBI. This article is protected by copyright. All rights reserved.
Chiou SY, Hellyer PJ, Sharp DJ, et al., 2017, Relationships between the integrity and function of lumbar nerve roots as assessed by diffusion tensor imaging and neurophysiology, NEURORADIOLOGY, Vol: 59, Pages: 893-903, ISSN: 0028-3940
PurposeDiffusion tensor imaging (DTI) has shown promise in the measurement of peripheral nerve integrity, although the optimal way to apply the technique for the study of lumbar spinal nerves is unclear. The aims of this study are to use an improved DTI acquisition to investigate lumbar nerve root integrity and correlate this with functional measures using neurophysiology.MethodsTwenty healthy volunteers underwent 3 T DTI of the L5/S1 area. Regions of interest were applied to L5 and S1 nerve roots, and DTI metrics (fractional anisotropy, mean, axial and radial diffusivity) were derived. Neurophysiological measures were obtained from muscles innervated by L5/S1 nerves; these included the slope of motor-evoked potential input-output curves, F-wave latency, maximal motor response, and central and peripheral motor conduction times.ResultsDTI metrics were similar between the left and right sides and between vertebral levels. Conversely, significant differences in DTI measures were seen along the course of the nerves. Regression analyses revealed that DTI metrics of the L5 nerve correlated with neurophysiological measures from the muscle innervated by it.ConclusionThe current findings suggest that DTI has the potential to be used for assessing lumbar spinal nerve integrity and that parameters derived from DTI provide quantitative information which reflects their function.
Hellyer PJ, Barry EF, Pellizzon A, et al., 2017, Protein synthesis is associated with high-speed dynamics and broad-band stability of functional hubs in the brain., Neuroimage, Vol: 155, Pages: 209-216
L-[1-(11)C]leucine PET can be used to measure in vivo protein synthesis in the brain. However, the relationship between regional protein synthesis and on-going neural dynamics is unclear. We use a graph theoretical approach to examine the relationship between cerebral protein synthesis (rCPS) and both static and dynamical measures of functional connectivity (measured using resting state functional MRI, R-fMRI). Our graph theoretical analysis demonstrates a significant positive relationship between protein turnover and static measures of functional connectivity. We compared these results to simple measures of metabolism in the cortex using [(18)F]FDG PET). Whilst some relationships between [(18)F]FDG binding and graph theoretical measures was present, there remained a significant relationship between protein turnover and graph theoretical measures, which were more robustly explained by L-[1-(11)C]Leucine than [(18)F]FDG PET. This relationship was stronger in dynamics at a faster temporal resolution relative to dynamics measured over a longer epoch. Using a Dynamic connectivity approach, we also demonstrate that broad-band dynamic measures of Functional Connectivity (FC), are inversely correlated with protein turnover, suggesting greater stability of FC in highly interconnected hub regions is supported by protein synthesis. Overall, we demonstrate that cerebral protein synthesis has a strong relationship independent of tissue metabolism to neural dynamics at the macroscopic scale.
Monti RP, Lorenz R, Hellyer P, et al., 2017, Decoding time-varying functional connectivity networks via linear graph embedding methods, Frontiers in Computational Neuroscience, Vol: 11, ISSN: 1662-5188
An exciting avenue of neuroscientific research involves quantifying the time-varying prop-erties of functional connectivity networks. As a result, many methods have been proposed toestimate the dynamic properties of such networks. However, one of the challenges associatedwith such methods involves the interpretation and visualization of high-dimensional, dynamicnetworks. In this work, we employ graph embedding algorithms to provide low-dimensionalvector representations of networks, thus facilitating traditional objectives such as visualization,interpretation and classification. We focus on linear graph embedding methods based on prin-cipal component analysis and regularized linear discriminant analysis. The proposed graphembedding methods are validated through a series of simulations and applied to fMRI datafrom the Human Connectome Project.
Ghajari M, Hellyer P, Sharp D, 2016, Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology, Brain, Vol: 140, Pages: 333-343, ISSN: 0006-8950
Traumatic brain injury can lead to the neurodegenerative disease chronic traumatic encephalopathy. This condition has a clear neuropathological definition but the relationship between the initial head impact and the pattern of progressive brain pathology is poorly understood. We test the hypothesis that mechanical strain and strain rate are greatest in sulci, where neuropathology is prominently seen in chronic traumatic encephalopathy, and whether human neuroimaging observations converge with computational predictions. Three distinct types of injury were simulated. Chronic traumatic encephalopathy can occur after sporting injuries, so we studied a helmet-to-helmet impact in an American football game. In addition, we investigated an occipital head impact due to a fall from ground level and a helmeted head impact in a road traffic accident involving a motorcycle and a car. A high fidelity 3D computational model of brain injury biomechanics was developed and the contours of strain and strain rate at the grey matter–white matter boundary were mapped. Diffusion tensor imaging abnormalities in a cohort of 97 traumatic brain injury patients were also mapped at the grey matter–white matter boundary. Fifty-one healthy subjects served as controls. The computational models predicted large strain most prominent at the depths of sulci. The volume fraction of sulcal regions exceeding brain injury thresholds were significantly larger than that of gyral regions. Strain and strain rates were highest for the road traffic accident and sporting injury. Strain was greater in the sulci for all injury types, but strain rate was greater only in the road traffic and sporting injuries. Diffusion tensor imaging showed converging imaging abnormalities within sulcal regions with a significant decrease in fractional anisotropy in the patient group compared to controls within the sulci. Our results show that brain tissue deformation induced by head impact loading is greatest in sulcal
Braga RM, Hellyer PJ, Wise, et al., 2016, Auditory and visual connectivity gradients in frontoparietal cortex, Human Brain Mapping, Vol: 38, Pages: 255-270, ISSN: 1097-0193
A frontoparietal network of brain regions is often implicated in both auditory and visual information processing. Although it is possible that the same set of multimodal regions subserves both modalities, there is increasing evidence that there is a differentiation of sensory function within frontoparietal cortex. Magnetic resonance imaging (MRI) in humans was used to investigate whether different frontoparietal regions showed intrinsic biases in connectivity with visual or auditory modalities. Structural connectivity was assessed with diffusion tractography and functional connectivity was tested using functional MRI. A dorsal–ventral gradient of function was observed, where connectivity with visual cortex dominates dorsal frontal and parietal connections, while connectivity with auditory cortex dominates ventral frontal and parietal regions. A gradient was also observed along the posterior–anterior axis, although in opposite directions in prefrontal and parietal cortices. The results suggest that the location of neural activity within frontoparietal cortex may be influenced by these intrinsic biases toward visual and auditory processing. Thus, the location of activity in frontoparietal cortex may be influenced as much by stimulus modality as the cognitive demands of a task. It was concluded that stimulus modality was spatially encoded throughout frontal and parietal cortices, and was speculated that such an arrangement allows for top–down modulation of modality-specific information to occur within higher-order cortex. This could provide a potentially faster and more efficient pathway by which top–down selection between sensory modalities could occur, by constraining modulations to within frontal and parietal regions, rather than long-range connections to sensory cortices.
Sharp D, Hellyer P, Ghanjari M, 2016, The distribution of neuropathology seen in chronic traumatic encephalopathy can be predicted by finite element modelling of impact biomechanics and can be observed in human neuroimaging data, International Brain Injury Association’s Eleventh World Congress on Brain Injury, Publisher: Taylor & Francis, Pages: 662-662, ISSN: 1362-301X
Carhart-Harris RL, Muthukumaraswamy S, Roseman L, et al., 2016, Neural correlates of the LSD experience revealed by multimodal neuroimaging, Proceedings of the National Academy of Sciences of the United States of America, Vol: 113, Pages: 4853-4858, ISSN: 1091-6490
Lysergic acid diethylamide (LSD) is the prototypical psychedelic drug, but its effects on the human brain have never been studied before with modern neuroimaging. Here, three complementary neuroimaging techniques: arterial spin labeling (ASL), blood oxygen level-dependent (BOLD) measures, and magnetoencephalography (MEG), implemented during resting state conditions, revealed marked changes in brain activity after LSD that correlated strongly with its characteristic psychological effects. Increased visual cortex cerebral blood flow (CBF), decreased visual cortex alpha power, and a greatly expanded primary visual cortex (V1) functional connectivity profile correlated strongly with ratings of visual hallucinations, implying that intrinsic brain activity exerts greater influence on visual processing in the psychedelic state, thereby defining its hallucinatory quality. LSD’s marked effects on the visual cortex did not significantly correlate with the drug’s other characteristic effects on consciousness, however. Rather, decreased connectivity between the parahippocampus and retrosplenial cortex (RSC) correlated strongly with ratings of “ego-dissolution” and “altered meaning,” implying the importance of this particular circuit for the maintenance of “self” or “ego” and its processing of “meaning.” Strong relationships were also found between the different imaging metrics, enabling firmer inferences to be made about their functional significance. This uniquely comprehensive examination of the LSD state represents an important advance in scientific research with psychedelic drugs at a time of growing interest in their scientific and therapeutic value. The present results contribute important new insights into the characteristic hallucinatory and consciousness-altering properties of psychedelics that inform on how they can model certain pathological states and potentially treat others.
Scott GPT, Ramlackhansingh A, Edison P, et al., 2016, Amyloid pathology and axonal injury after brain trauma, Neurology, Vol: 86, Pages: 821-828, ISSN: 0028-3878
Objective: To image amyloid-β (Aβ) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and Aβ are correlated, and compare the spatial distribution of Aβ to Alzheimer’s disease.Methods: Patients 11 months to 17 years after moderate-severe TBI had 11C-Pittsburgh compound-B (PIB) PET, structural and diffusion MRI and neuropsychological examination. Healthy aged controls and AD patients had PET and structural MRI. Binding potential (BPND) images of 11C-PIB, which index Aβ plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy (FA), was estimated and correlated with 11C-PIB BPND.Results: 28 participants (9 TBI, 9 controls, 10 AD) were assessed. Increased 11C-PIB BPND was found in TBI versus controls in the posterior cingulate cortex (PCC) and cerebellum. Binding in the PCC increased with decreasing FA of associated white matter tracts, and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions, but increased in the cerebellum. Conclusions: Increased Aβ burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathological features of dementia, which may relate to axonal damage produced by the injury.
The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. Thisability relies on flexible cognitive systems that adapt in order to encode temporary programs forprocessing non-automated tasks. Previous functional imaging studies have revealed distinctroles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learningprocesses. However, the human LFCs are complex; they house multiple distinct sub-regions,each of which co-activates with a different functional network. It remains unclear how these LFCnetworks differ in their functions and how they coordinate with each other, and the ventralstriatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods todetermine how LFC networks activate and interact at different stages of two novel tasks, inwhich arbitrary stimulus-response rules are learnt either from explicit instruction or by trialand-error.We report that the networks activate en masse and in synchrony when novel rules arebeing learnt from instruction. However, these networks are not homogeneous in their functions;instead, the directed connectivities between them vary asymmetrically across the learningtimecourse and they disengage from the task sequentially along a rostro-caudal axis.Furthermore, when negative feedback indicates the need to switch to alternative stimulusresponserules, there is additional input to the LFC networks from the ventral striatum. Theseresults support the hypotheses that LFC networks interact as a hierarchical system duringintentional learning and that signals from the ventral striatum have a driving influence on thissystem when the internal program for processing the task is updated.
Scott G, Hellyer PJ, Ramlackhansingh AF, et al., 2015, Thalamic inflammation after brain trauma is associated with thalamo-cortical white matter damage, Journal of Neuroinflammation, Vol: 12, ISSN: 1742-2094
BackgroundTraumatic brain injury can trigger chronic neuroinflammation, which may predispose to neurodegeneration. Animal models and human pathological studies demonstrate persistent inflammation in the thalamus associated with axonal injury, but this relationship has never been shown in vivo.FindingsUsing [11C]-PK11195 positron emission tomography, a marker of microglial activation, we previously demonstrated thalamic inflammation up to 17 years after traumatic brain injury. Here, we use diffusion MRI to estimate axonal injury and show that thalamic inflammation is correlated with thalamo-cortical tract damage.ConclusionsThese findings support a link between axonal damage and persistent inflammation after brain injury.
Hellyer PJ, Jachs B, Clopath C, et al., 2015, Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks, Neuroimage, Vol: 124, Pages: 85-95, ISSN: 1095-9572
Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks.
Radford DR, Hellyer P, 2015, Dental students' perceptions of their experience at a residential outreach centre, BRITISH DENTAL JOURNAL, Vol: 219, Pages: 171-175, ISSN: 0007-0610
Shanahan MP, Hellyer P, Sharp DJ, et al., 2015, Cognitive flexibility through metastable neural dynamics is disrupted by damage to the structural connectome, Journal of Neuroscience, Vol: 35, Pages: 9050-9063, ISSN: 0270-6474
Current theory proposes that healthy neural dynamics operate in a metastable regime, where brain regions interact to simultaneously maximize integration and segregation. Metastability may confer important behavioral properties, such as cognitive flexibility. It is increasingly recognized that neural dynamics are constrained by the underlying structural connections between brain regions. An important challenge is, therefore, to relate structural connectivity, neural dynamics, and behavior. Traumatic brain injury (TBI) is a pre-eminent structural disconnection disorder whereby traumatic axonal injury damages large-scale connectivity, producing characteristic cognitive impairments, including slowed information processing speed and reduced cognitive flexibility, that may be a result of disrupted metastable dynamics. Therefore, TBI provides an experimental and theoretical model to examine how metastable dynamics relate to structural connectivity and cognition. Here, we use complementary empirical and computational approaches to investigate how metastability arises from the healthy structural connectome and relates to cognitive performance. We found reduced metastability in large-scale neural dynamics after TBI, measured with resting-state functional MRI. This reduction in metastability was associated with damage to the connectome, measured using diffusion MRI. Furthermore, decreased metastability was associated with reduced cognitive flexibility and information processing. A computational model, defined by empirically derived connectivity data, demonstrates how behaviorally relevant changes in neural dynamics result from structural disconnection. Our findings suggest how metastable dynamics are important for normal brain function and contingent on the structure of the human connectome.
Váša F, Shanahan M, Hellyer P, et al., 2015, Effects of lesions on synchrony and metastability in cortical networks, Neuroimage, Vol: 118, Pages: 456-467, ISSN: 1095-9572
At the macroscopic scale, the human brain can be described as a complex network of white matter tracts integrating grey matter assemblies — the human connectome. The structure of the connectome, which is often described using graph theoretic approaches, can be used to model macroscopic brain function at low computational cost. Here, we use the Kuramoto model of coupled oscillators with time-delays, calibrated with respect to empirical functional MRI data, to study the relation between the structure of the connectome and two aspects of functional brain dynamics — synchrony, a measure of general coherence, and metastability, a measure of dynamical flexibility. Specifically, we investigate the relationship between the local structure of the connectome, quantified using graph theory, and the synchrony and metastability of the model's dynamics. By removing individual nodes and all of their connections from the model, we study the effect of lesions on both global and local dynamics. Of the nine nodal graph-theoretical properties tested, two were able to predict effects of node lesion on the global dynamics. The removal of nodes with high eigenvector centrality leads to decreases in global synchrony and increases in global metastability, as does the removal of hub nodes joining topologically segregated network modules. At the level of local dynamics in the neighbourhood of the lesioned node, structural properties of the lesioned nodes hold more predictive power, as five nodal graph theoretical measures are related to changes in local dynamics following node lesions. We discuss these results in the context of empirical studies of stroke and functional brain dynamics.
Parkin BL, Hellyer PJ, Leech R, et al., 2015, Dynamic network mechanisms of relational integration, Journal of Neuroscience, Vol: 35, Pages: 7660-7673, ISSN: 1529-2401
© 2015 Parkin et al. A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifically. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domaingeneral perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.
Fagerholm ED, Hellyer PJ, Scott G, et al., 2015, Disconnection of network hubs and cognitive impairment after traumatic brain injury., Brain, Vol: 138, Pages: 1696-1709, ISSN: 0006-8950
Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These resul
Fagerholm ED, Lorenz R, Scott G, et al., 2015, Cascades and Cognitive State: Focused Attention Incurs Subcritical Dynamics, Journal of Neuroscience, Vol: 35, Pages: 4626-4634, ISSN: 1529-2401
Monti R, Lorenz R, Hellyer P, et al., 2015, Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data, 2015 International Workshop on Pattern Recognition in NeuroImaging PRNI 2015, Publisher: IEEE, Pages: 1-4
Petri G, Expert P, Turkheimer F, et al., 2014, Homological scaffolds of brain functional networks, Journal of the Royal Society Interface, Vol: 11, ISSN: 1742-5689
Networks, as efficient representations of complex systems, have appealed toscientists for a long time and now permeate many areas of science, includingneuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198.(doi:10.1038/nrn2618)). Traditionally, the structure of complex networks hasbeen studied through their statistical properties and metrics concerned withnode and link properties, e.g. degree-distribution, node centrality and modularity.Here, we study the characteristics of functional brain networks at themesoscopic level from a novel perspective that highlights the role of inhomogeneitiesin the fabric of functional connections. This can be done by focusingon the features of a set of topological objects—homological cycles—associatedwith the weighted functional network. We leverage the detected topologicalinformation to define the homological scaffolds, a new set of objects designed torepresent compactly the homological features of the correlation network andsimultaneously make their homological properties amenable to networks theoreticalmethods. As a proof of principle, we apply these tools to compare restingstatefunctional brain activity in 15 healthy volunteers after intravenous infusionof placebo and psilocybin—the main psychoactive component of magic mushrooms.The results show that the homological structure of the brain’s functionalpatterns undergoes a dramatic change post-psilocybin, characterized by theappearance of many transient structures of low stability and of a smallnumber of persistent ones that are not observed in the case of placebo.
Scott G, Hellyer PJ, Hampshire A, et al., 2014, Exploring spatiotemporal network transitions in task functional MRI, Hum. Brain Mapp., Pages: n/a-n/a, ISSN: 1097-0193
Monti RP, Hellyer P, Sharp D, et al., 2014, Estimating time-varying brain connectivity networks from functional MRI time series, NEUROIMAGE, Vol: 103, Pages: 427-443, ISSN: 1053-8119
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