51 results found
Turkheimer FE, Hellyer P, Kehagia AA, et al., 2019, Conflicting emergences. Weak vs. strong emergence for the modelling of brain function., Neurosci Biobehav Rev, Vol: 99, Pages: 3-10
The concept of "emergence" has become commonplace in the modelling of complex systems, both natural and man-made; a functional property" emerges" from a system when it cannot be readily explained by the properties of the system's sub-units. A bewildering array of adaptive and sophisticated behaviours can be observed from large ensembles of elementary agents such as ant colonies, bird flocks or by the interactions of elementary material units such as molecules or weather elements. Ultimately, emergence has been adopted as the ontological support of a number of attempts to model brain function. This manuscript aims to clarify the ontology of emergence and delve into its many facets, particularly into its "strong" and "weak" versions that underpin two different approaches to the modelling of behaviour. The first group of models is here represented by the "free energy" principle of brain function and the "integrated information theory" of consciousness. The second group is instead represented by computational models such as oscillatory networks that use mathematical scalable representations to generate emergent behaviours and are then able to bridge neurobiology with higher mental functions. Drawing on the epistemological literature, we observe that due to their loose mechanistic links with the underlying biology, models based on strong forms of emergence are at risk of metaphysical implausibility. This, in practical terms, translates into the over determination that occurs when the proposed model becomes only one of a large set of possible explanations for the observable phenomena. On the other hand, computational models that start from biologically plausible elementary units, hence are weakly emergent, are not limited by ontological faults and, if scalable and able to realistically simulate the hierarchies of brain output, represent a powerful vehicle for future neuroscientific research programmes.
Barry EF, Vanes LD, Andrews DS, et al., 2019, Mapping cortical surface features in treatment resistant schizophrenia with in vivo structural MRI., Psychiatry Res, Vol: 274, Pages: 335-344
Decreases in cortical volume (CV), thickness (CT) and surface area (SA) have been reported in individuals with schizophrenia by in vivo MRI studies. However, there are few studies that examine these cortical measures as potential biomarkers of treatment resistance (TR) and treatment response (NTR) in schizophrenia. This study used structural MRI to examine differences in CV, CT, and SA in 42 adults with schizophrenia (TR = 21, NTR = 21) and 23 healthy controls (HC) to test the hypothesis that individuals with TR schizophrenia have significantly greater reductions in these cortical measures compared to individuals with NTR schizophrenia. We found that individuals with TR schizophrenia showed significant reductions in CV and CT compared to individuals with NTR schizophrenia in right frontal and precentral regions, right parietal and occipital cortex, left temporal cortex and bilateral cingulate cortex. In line with previous literature, the temporal lobe and cingulate gyrus in both patient groups showed significant reductions of all three measures when compared to healthy controls. Taken together these results suggest that regional changes in CV and CT may index mechanisms specific to TR schizophrenia and potentially identify patients with TR schizophrenia for earlier treatment.
De Simoni S, Jenkins PO, Bourke NJ, et al., 2018, Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury, BRAIN, Vol: 141, Pages: 148-164, ISSN: 0006-8950
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
Hellyer PJ, Clopath C, Kehagia AA, 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
Feeney C, Sharp DJ, Hellyer PJ, et al., 2017, Serum Insulin-like Growth Factor-I Levels are Associated with Improved White Matter Recovery after Traumatic Brain Injury, ANNALS OF NEUROLOGY, Vol: 82, Pages: 30-43, ISSN: 0364-5134
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
Ghajari M, Hellyer PJ, Sharp DJ, 2017, Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology, BRAIN, Vol: 140, Pages: 333-343, ISSN: 0006-8950
Braga RM, Hellyer PJ, Wise RJS, et al., 2017, Auditory and visual connectivity gradients in frontoparietal cortex, HUMAN BRAIN MAPPING, Vol: 38, Pages: 255-270, ISSN: 1065-9471
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: 0027-8424
Scott G, Ramlackhansingh AF, Edison P, et al., 2016, Amyloid pathology and axonal injury after brain trauma, NEUROLOGY, Vol: 86, Pages: 821-828, ISSN: 0028-3878
Hellyer PJ, Jachs B, Clopath C, et al., 2016, Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks, NEUROIMAGE, Vol: 124, Pages: 85-95, ISSN: 1053-8119
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, Publisher: TAYLOR & FRANCIS INC, Pages: 662-662, ISSN: 0269-9052
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
Vasa F, Shanahan M, Hellyer PJ, et al., 2015, Effects of lesions on synchrony and metastability in cortical networks, NEUROIMAGE, Vol: 118, Pages: 456-467, ISSN: 1053-8119
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
Hellyer PJ, Scott G, Shanahan M, 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
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
Parkin BL, Hellyer PJ, Leech R, et al., 2015, Dynamic Network Mechanisms of Relational Integration, JOURNAL OF NEUROSCIENCE, Vol: 35, Pages: 7660-7673, ISSN: 0270-6474
Scott G, Hellyer PJ, Hampshire A, et al., 2015, Exploring Spatiotemporal Network Transitions in Task Functional MRI, HUMAN BRAIN MAPPING, Vol: 36, Pages: 1348-1364, ISSN: 1065-9471
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: 0270-6474
Nigmatullina Y, Hellyer PJ, Nachev P, et al., 2015, The Neuroanatomical Correlates of Training-Related Perceptuo-Reflex Uncoupling in Dancers, CEREBRAL CORTEX, Vol: 25, Pages: 554-562, ISSN: 1047-3211
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
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
Nigmatullina Y, Hellyer PM, Nachev P, et al., 2014, The neuroanatomical correlates of vestibular adaptation in ballet dancers, Joint Congress of European Neurology, Publisher: SPRINGER HEIDELBERG, Pages: S190-S191, ISSN: 0340-5354
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