Publications by authors named "Marcus Kaiser"

83 Publications

Understanding Neural Flexibility From a Multifaceted Definition.

Neuroimage 2021 Apr 6:118027. Epub 2021 Apr 6.

School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK; School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK; Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. Electronic address:

Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118027DOI Listing
April 2021

Creative Destruction: A Basic Computational Model of Cortical Layer Formation.

Cereb Cortex 2021 Feb 24. Epub 2021 Feb 24.

School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.

One of the most characteristic properties of many vertebrate neural systems is the layered organization of different cell types. This cytoarchitecture exists in the cortex, the retina, the hippocampus, and many other parts of the central nervous system. The developmental mechanisms of neural layer formation have been subject to substantial experimental efforts. Here, we provide a general computational model for cortical layer formation in 3D physical space. We show that this multiscale, agent-based model, comprising two distinct stages of apoptosis, can account for the wide range of neuronal numbers encountered in different cortical areas and species. Our results demonstrate the phenotypic richness of a basic state diagram structure. Importantly, apoptosis allows for changing the thickness of one layer without automatically affecting other layers. Therefore, apoptosis increases the flexibility for evolutionary change in layer architecture. Notably, slightly changed gene regulatory dynamics recapitulate the characteristic properties observed in neurodevelopmental diseases. Overall, we propose a novel computational model using gene-type rules, exhibiting many characteristics of normal and pathological cortical development.
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http://dx.doi.org/10.1093/cercor/bhab003DOI Listing
February 2021

Brain network analysis reveals that amyloidopathy affects comorbid cognitive dysfunction in older adults with depression.

Sci Rep 2021 Feb 22;11(1):4299. Epub 2021 Feb 22.

Department of Biomedical Sciences, Korea University Graduate School, Seoul, Republic of Korea.

Late-life depression (LLD) may increase the risk of Alzheimer's dementia (AD). While amyloidopathy accelerates AD progression, its role in such patients has not yet been elucidated. We hypothesized that cerebral amyloidopathy distinctly affects the alteration of brain network topology and may be associated with distinct cognitive symptoms. We recruited 26 and 27 depressed mild cognitive impairment (MCI) patients with (LLD-MCI-A(+)) and without amyloid accumulation (LLD-MCI-A(-)), respectively, and 21 normal controls. We extracted structural brain networks using their diffusion-weighted images. We aimed to compare the distinct network deterioration in LLD-MCI with and without amyloid accumulation and the relationship with their distinct cognitive decline. Thus, we performed a group comparison of the network topological measures and investigated any correlations with neurocognitive testing scores. Topological features of brain networks were different according to the presence of amyloid accumulation. Disrupted network connectivity was highly associated with impaired recall and recognition in LLD-MCI-A(+) patients. Inattention and dysexecutive function were more influenced by the altered networks involved in fronto-limbic circuitry dysfunction in LLD-MCI-A(-) patients. Our results show that alterations in brain network topology may reflect different cognitive dysfunction depending on amyloid accumulation in depressed older adults with MCI.
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http://dx.doi.org/10.1038/s41598-021-83739-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900108PMC
February 2021

Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions.

Neurocomputing 2020 Nov;416:38-44

Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom.

Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.
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http://dx.doi.org/10.1016/j.neucom.2020.01.050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598092PMC
November 2020

Reliability and comparability of human brain structural covariance networks.

Neuroimage 2020 10 2;220:117104. Epub 2020 Jul 2.

CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK. Electronic address:

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117104DOI Listing
October 2020

NIHBA: a network interdiction approach for metabolic engineering design.

Bioinformatics 2020 06;36(11):3482-3492

School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK.

Motivation: Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run.

Results: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article).

Availability And Implementation: Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA.

Contact: math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa163DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267835PMC
June 2020

Computational modelling of the long-term effects of brain stimulation on the local and global structural connectivity of epileptic patients.

PLoS One 2020 6;15(2):e0221380. Epub 2020 Feb 6.

Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, England, United Kingdom.

Computational studies of the influence of different network parameters on the dynamic and topological network effects of brain stimulation can enhance our understanding of different outcomes between individuals. In this study, a brain stimulation session along with the subsequent post-stimulation brain activity is simulated for a period of one day using a network of modified Wilson-Cowan oscillators coupled according to diffusion imaging based structural connectivity. We use this computational model to examine how differences in the inter-region connectivity and the excitability of stimulated regions at the time of stimulation can affect post-stimulation behaviours. Our findings indicate that the initial inter-region connectivity can heavily affect the changes that stimulation induces in the connectivity of the network. Moreover, differences in the excitability of the stimulated regions seem to lead to different post-stimulation connectivity changes across the model network, including on the internal connectivity of non-stimulated regions.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221380PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7004372PMC
April 2020

Divisive gain modulation enables flexible and rapid entrainment in a neocortical microcircuit model.

J Neurophysiol 2020 03 5;123(3):1133-1143. Epub 2020 Feb 5.

CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.

Neocortical circuits exhibit a rich dynamic repertoire, and their ability to achieve entrainment (adjustment of their frequency to match the input frequency) is thought to support many cognitive functions and indicate functional flexibility. Although previous studies have explored the influence of various circuit properties on this phenomenon, the role of divisive gain modulation (or divisive inhibition) is unknown. This gain control mechanism is thought to be delivered mainly by the soma-targeting interneurons in neocortical microcircuits. In this study, we use a neural mass model of the neocortical microcircuit (extended Wilson-Cowan model) featuring both soma-targeting and dendrite-targeting interneuronal subpopulations to investigate the role of divisive gain modulation in entrainment. Our results demonstrate that the presence of divisive inhibition in the microcircuit, as delivered by the soma-targeting interneurons, enables its entrainment to a wider range of input frequencies. Divisive inhibition also promotes a faster entrainment, with the microcircuit needing less time to converge to the fully entrained state. We suggest that divisive inhibition, working alongside subtractive inhibition, allows for more adaptive oscillatory responses in neocortical circuits and, thus, supports healthy brain functioning. We introduce a computational neocortical microcircuit model that features two inhibitory neural populations, with one providing subtractive and the other divisive inhibition to the excitatory population. We demonstrate that divisive inhibition widens the range of input frequencies to which the microcircuit can become entrained and diminishes the time needed to reach full entrainment. We suggest that divisive inhibition enables more adaptive oscillatory activity, with important implications for both normal and pathological brain function.
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http://dx.doi.org/10.1152/jn.00401.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099485PMC
March 2020

Weighted network measures reveal differences between dementia types: An EEG study.

Hum Brain Mapp 2020 04 9;41(6):1573-1590. Epub 2019 Dec 9.

Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.

The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non-invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age-matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior-anterior patterns within the β-band and greater network segregation within the θ-band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β- and θ-band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α-band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.
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http://dx.doi.org/10.1002/hbm.24896DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267959PMC
April 2020

Periodic Operation of a Dynamic DNA Origami Structure Utilizing the Hydrophilic-Hydrophobic Phase-Transition of Stimulus-Sensitive Polypeptides.

Small 2019 11 18;15(45):e1903541. Epub 2019 Sep 18.

Physics of Synthetic Biological Systems-E14, Physics Department and ZNN, Technische Universität München, 85748, Garching, Germany.

Dynamic DNA nanodevices are designed to perform structure-encoded motion actuated by a variety of different physicochemical stimuli. In this context, hybrid devices utilizing other components than DNA have the potential to considerably expand the library of functionalities. Here, the reversible reconfiguration of a DNA origami structure using the stimulus sensitivity of elastin-like polypeptides is reported. To this end, a rectangular sheet made using the DNA origami technique is functionalized with these peptides and by applying changes in salt concentration the hydrophilic-hydrophobic phase transition of these peptides actuate the folding of the structure. The on-demand and reversible switching of the rectangle is driven by externally imposed temperature oscillations and appears at specific transition temperatures. Using transmission electron microscopy, it is shown that the structure exhibits distinct conformational states with different occupation probabilities, which are dependent on structure-intrinsic parameters such as the local number and the arrangement of the peptides on the rectangle. It is also shown through ensemble fluorescence resonance energy transfer spectroscopy that the transition temperature and thus the thermodynamics of the rectangle-peptide system depends on the stimuli salt concentration and temperature, as well as on the intrinsic parameters.
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http://dx.doi.org/10.1002/smll.201903541DOI Listing
November 2019

Computational models and fundamental constraints can inform the design of synthetic connectomes: Comment on "What would a synthetic connectome look like?" by Ithai Rabinowitch.

Authors:
Marcus Kaiser

Phys Life Rev 2020 Jul 7;33:16-18. Epub 2019 Aug 7.

Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom; Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China. Electronic address:

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http://dx.doi.org/10.1016/j.plrev.2019.08.003DOI Listing
July 2020

Structural correlates of attention dysfunction in Lewy body dementia and Alzheimer's disease: an ex-Gaussian analysis.

J Neurol 2019 Jul 21;266(7):1716-1726. Epub 2019 Apr 21.

Institute of Neuroscience, Newcastle University, Biomedical Research Building 3rd Floor, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK.

Background: Lewy body dementia (LBD) and Alzheimer's disease (AD) are common forms of degenerative dementia. While they are characterized by different clinical profiles, attentional deficits are a common feature. The objective of this study was to investigate how attentional problems in LBD and AD differentially affect different aspects of reaction time performance and to identify possible structural neural correlates.

Methods: We studied reaction time data from an attention task comparing 39 LBD patients, 28 AD patients, and 22 age-matched healthy controls. Data were fitted to an ex-Gaussian model to characterize different facets of the reaction time distribution (mean reaction time, reaction time variability, and the subset of extremely slow responses). Correlations between ex-Gaussian parameters and grey and white matter volume were assessed by voxel-based morphometry.

Results: Both dementia groups showed an increase in extremely slow responses. While there was no difference between AD and controls with respect to mean reaction time and variability, both were significantly increased in LBD patients compared to controls and AD. There were widespread correlations between mean reaction time and variability and grey matter loss in AD, but not in LBD.

Conclusions: This study shows that different aspects of reaction time performance are differentially affected by AD and LBD, with a difference in structural neural correlates underlying the observed behavioural deficits. While impaired attentional performance is linked to brain atrophy in AD, in LBD it might be related to functional or microstructural rather than macrostructural changes.
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http://dx.doi.org/10.1007/s00415-019-09323-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586700PMC
July 2019

Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease.

Neuroimage Clin 2019 3;22:101812. Epub 2019 Apr 3.

Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, United Kingdom.

We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.
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http://dx.doi.org/10.1016/j.nicl.2019.101812DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462776PMC
January 2020

The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue.

Wellcome Open Res 2019 1;4:20. Epub 2019 Feb 1.

Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK.

Neuronal circuits can be modelled in detail allowing us to predict the effects of stimulation on individual neurons. Electrical stimulation of neuronal circuits and excites a range of neurons within the tissue and measurements of neural activity, e.g the local field potential (LFP), are again an aggregate of a large pool of cells. The previous version of our Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) allowed for the simulation of the LFP generated by a patch of brain tissue. Here, we extend VERTEX to simulate the effect of electrical stimulation through a focal electric field. We observe both direct changes in neural activity and changes in synaptic plasticity. Testing our software in a model of a rat neocortical slice, we determine the currents contributing to the LFP, the effects of paired pulse stimulation to induce short term plasticity (STP), and the effect of theta burst stimulation (TBS) to induce long term potentiation (LTP).
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http://dx.doi.org/10.12688/wellcomeopenres.15058.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6439485PMC
February 2019

Dysfunctional brain dynamics and their origin in Lewy body dementia.

Brain 2019 06;142(6):1767-1782

Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.

Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer's disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain's dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer's disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer's disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer's disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = - 0.53, P = 0.003) and thalamic networks (r = - 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer's disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics.
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http://dx.doi.org/10.1093/brain/awz069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6536851PMC
June 2019

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization.

IEEE Trans Cybern 2020 Jun 15;50(6):2814-2826. Epub 2019 Feb 15.

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more important, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.
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http://dx.doi.org/10.1109/TCYB.2019.2896021DOI Listing
June 2020

Structural connectivity centrality changes mark the path toward Alzheimer's disease.

Alzheimers Dement (Amst) 2019 Dec 18;11:98-107. Epub 2019 Jan 18.

Interdisciplinary Computing and Complex Biosystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.

Introduction: The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques.

Methods: Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression.

Results: A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages.

Discussion: Our analyses suggest that diffusion magnetic resonance imaging-based centrality measures can offer a tool for early disease detection before clinical dementia onset.
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http://dx.doi.org/10.1016/j.dadm.2018.12.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350419PMC
December 2019

Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy.

Neuroimage Clin 2019 11;21:101655. Epub 2019 Jan 11.

Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK. Electronic address:

Patients with idiopathic generalised epilepsy (IGE) typically have normal conventional magnetic resonance imaging (MRI), hence diagnosis based on MRI is challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are unclear and their relation to the pathomechanisms of epileptogenesis is poorly understood. In this study, we applied connectometry, an advanced quantitative neuroimaging technique for investigating localised changes in white-matter tissues in vivo. Analysing white matter structures of 32 subjects we incorporated our in vivo findings in a computational model of seizure dynamics to suggest a plausible mechanism of epileptogenesis. Patients with IGE have significant bilateral alterations in major white-matter fascicles. In the cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients. Combining these alterations in a logistic regression model, we computed the decision boundary that discriminated patients and controls. The computational model, informed with the findings on the tract abnormalities, specifically highlighted the importance of enhanced cortico-reticular connections along with impaired cortico-cortical connections in inducing pathological seizure-like dynamics. We emphasise taking directionality of brain connectivity into consideration towards understanding the pathological mechanisms; this is possible by combining neuroimaging and computational modelling. Our imaging evidence of structural alterations suggest the loss of cortico-cortical and enhancement of cortico-thalamic fibre integrity in IGE. We further suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit for reversing the mechanisms leading to epileptogenesis.
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http://dx.doi.org/10.1016/j.nicl.2019.101655DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6356007PMC
January 2020

Structural Brain Correlates of Attention Dysfunction in Lewy Body Dementias and Alzheimer's Disease.

Front Aging Neurosci 2018 30;10:347. Epub 2018 Oct 30.

Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.

Lewy body dementia (LBD) and Alzheimer's disease (AD) are common forms of dementia that have different clinical profiles but are both commonly associated with attentional deficits. The aim of this study was to investigate efficiency of different attentional systems in LBD and AD and its association with brain structural abnormalities. We studied reaction time (RT) data from 45 LBD, 31 AD patients and 22 healthy controls (HCs) using the Attention Network Test (ANT) to assess the efficiency of three different attentional systems: alerting, orienting and executive conflict. Voxel-based morphometry (VBM) was used to investigate relations between different attention components and cortical volume. Both dementia groups showed slower overall RTs than controls, with additional slowing in LBD relative to AD. There was a significant alerting effect in controls which was absent in the dementia groups, the executive conflict effect was greater in both dementia groups compared to controls, but the orienting effect did not differ between groups. Mean RT in AD was negatively correlated with occipital gray matter (GM) volume and in LBD orienting efficiency was negatively related to occipital white matter (WM) volume. Given that previous studies in less impaired patients suggest a maintenance of the alerting effect, the absent alerting effect in our study suggests a loss of alerting efficiency with dementia progression. While orienting was largely preserved, it might be related to occipital structural abnormalities in LBD. Executive function was markedly impaired in both dementia groups, however, the absence of relations to brain volume suggests that it might be more related to functional rather than macrostructural pathophysiological changes.
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http://dx.doi.org/10.3389/fnagi.2018.00347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251343PMC
October 2018

Canonical Structure and Orthogonality of Forces and Currents in Irreversible Markov Chains.

J Stat Phys 2018 15;170(6):1019-1050. Epub 2018 Feb 15.

1Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY UK.

We discuss a canonical structure that provides a unifying description of dynamical large deviations for irreversible finite state Markov chains (continuous time), Onsager theory, and Macroscopic Fluctuation Theory (MFT). For Markov chains, this theory involves a non-linear relation between probability currents and their conjugate forces. Within this framework, we show how the forces can be split into two components, which are orthogonal to each other, in a generalised sense. This splitting allows a decomposition of the pathwise rate function into three terms, which have physical interpretations in terms of dissipation and convergence to equilibrium. Similar decompositions hold for rate functions at level 2 and level 2.5. These results clarify how bounds on entropy production and fluctuation theorems emerge from the underlying dynamical rules. We discuss how these results for Markov chains are related to similar structures within MFT, which describes hydrodynamic limits of such microscopic models.
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http://dx.doi.org/10.1007/s10955-018-1986-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566214PMC
February 2018

Functional connectivity in dementia with Lewy bodies: A within- and between-network analysis.

Hum Brain Mapp 2018 03 29;39(3):1118-1129. Epub 2017 Nov 29.

Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, United Kingdom.

Dementia with Lewy bodies (DLB) is a common form of dementia and is characterized by cognitive fluctuations, visual hallucinations, and Parkinsonism. The phenotypic expression of the disease may, in part, relate to alterations in functional connectivity within and between brain networks. This resting-state study sought to clarify this in DLB, how networks differed from Alzheimer's disease (AD), and whether they were related to clinical symptoms in DLB. Resting-state networks were estimated using independent component analysis. We investigated functional connectivity changes in 31 DLB patients compared to 31 healthy controls and a disease comparator group of 29 AD patients using dual regression and FSLNets. Within-network connectivity was generally decreased in DLB compared to controls, mainly in motor, temporal, and frontal networks. Between-network connectivity was mainly intact; only the connection between a frontal and a temporal network showed increased connectivity in DLB. Differences between AD and DLB were subtle and we did not find any significant correlations with the severity of clinical symptoms in DLB. This study emphasizes the importance of reduced connectivity within motor, frontal, and temporal networks in DLB with relative sparing of the default mode network. The lack of significant correlations between connectivity measures and clinical scores indicates that the observed reduced connectivity within these networks might be related to the presence, but not to the severity of motor and cognitive impairment in DLB patients. Furthermore, our results suggest that AD and DLB may show more similarities than differences in patients with mild disease.
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http://dx.doi.org/10.1002/hbm.23901DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5900719PMC
March 2018

Mechanisms of Connectome Development.

Authors:
Marcus Kaiser

Trends Cogn Sci 2017 Sep 10;21(9):703-717. Epub 2017 Jun 10.

ICOS Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK. Electronic address:

At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options. We argue that longitudinal studies, experimentally derived parameters for connection formation, and biologically realistic computational models are needed to better understand the link between brain network development, network structure, and network function.
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http://dx.doi.org/10.1016/j.tics.2017.05.010DOI Listing
September 2017

Mechanisms underlying different onset patterns of focal seizures.

PLoS Comput Biol 2017 05 4;13(5):e1005475. Epub 2017 May 4.

Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom.

Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types-low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. We show that the two patterns are associated with different mechanisms at the spatial scale of a single ECoG electrode. The LAF onset is initiated by independent patches of localised activity, which slowly invade the surrounding tissue and coalesce over time. In contrast, the HAS onset is a global, systemic transition to a coexisting seizure state triggered by a local event. We find that such a global transition is enabled by an increase in the excitability of the "healthy" surrounding tissue, which by itself does not generate seizures, but can support seizure activity when incited. In our simulations, the difference in surrounding tissue excitability also offers a simple explanation of the clinically reported difference in surgical outcomes. Finally, we demonstrate in the model how changes in tissue excitability could be elucidated, in principle, using active stimulation. Taken together, our modelling results suggest that the excitability of the tissue surrounding the seizure core may play a determining role in the seizure onset pattern, as well as in the surgical outcome.
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http://dx.doi.org/10.1371/journal.pcbi.1005475DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417416PMC
May 2017

Nonlinear growth: an origin of hub organization in complex networks.

R Soc Open Sci 2017 Mar 22;4(3):160691. Epub 2017 Mar 22.

Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Interdisciplinary Computing and Complex BioSystems Research Group (ICOS), School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein-protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication-divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.
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http://dx.doi.org/10.1098/rsos.160691DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5383813PMC
March 2017

Intra- and inter-network functional alterations in Parkinson's disease with mild cognitive impairment.

Hum Brain Mapp 2017 03 13;38(3):1702-1715. Epub 2017 Jan 13.

Institute of Neuroscience, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, NE4 5PL, United Kingdom.

Mild cognitive impairment (MCI) is prevalent in 15%-40% of Parkinson's disease (PD) patients at diagnosis. In this investigation, we study brain intra- and inter-network alterations in resting state functional magnetic resonance imaging (rs-fMRI) in recently diagnosed PD patients and characterise them as either cognitive normal (PD-NC) or with MCI (PD-MCI). Patients were divided into two groups, PD-NC (N = 62) and PD-MCI (N = 37) and for comparison, healthy controls (HC, N = 30) were also included. Intra- and inter-network connectivity were investigated from participants' rs-fMRIs in 26 resting state networks (RSNs). Intra-network differences were found between both patient groups and HCs for networks associated with motor control (motor cortex), spatial attention and visual perception. When comparing both PD-NC and PD-MCI, intra-network alterations were found in RSNs related to attention, executive function and motor control (cerebellum). The inter-network analysis revealed a hyper-synchronisation between the basal ganglia network and the motor cortex in PD-NC compared with HCs. When both patient groups were compared, intra-network alterations in RSNs related to attention, motor control, visual perception and executive function were found. We also detected disease-driven negative synchronisations and synchronisation shifts from positive to negative and vice versa in both patient groups compared with HCs. The hyper-synchronisation between basal ganglia and motor cortical RSNs in PD and its synchronisation shift from negative to positive compared with HCs, suggest a compensatory response to basal dysfunction and altered basal-cortical motor control in the resting state brain of PD patients. Hum Brain Mapp 38:1702-1715, 2017. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23499DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866883PMC
March 2017

Within brain area tractography suggests local modularity using high resolution connectomics.

Sci Rep 2017 01 5;7:39859. Epub 2017 Jan 5.

Institute of Neuroscience, Newcastle University, United Kingdom.

Previous structural brain connectivity studies have mainly focussed on the macroscopic scale of around 1,000 or fewer brain areas (network nodes). However, it has recently been demonstrated that high resolution structural connectomes of around 50,000 nodes can be generated reproducibly. In this study, we infer high resolution brain connectivity matrices using diffusion imaging data from the Human Connectome Project. With such high resolution we are able to analyse networks within brain areas in a single subject. We show that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture. Specifically, modules within brain areas are spatially localised. We find that long range connections terminate between specific modules, whilst short range connections via highly curved association fibers terminate within modules. We suggest that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas. Future studies might elucidate how brain diseases change this modular architecture within brain areas.
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http://dx.doi.org/10.1038/srep39859DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5213837PMC
January 2017

Acceleration of Convergence to Equilibrium in Markov Chains by Breaking Detailed Balance.

J Stat Phys 2017 18;168(2):259-287. Epub 2017 May 18.

1Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY UK.

We analyse and interpret the effects of breaking detailed balance on the convergence to equilibrium of conservative interacting particle systems and their hydrodynamic scaling limits. For finite systems of interacting particles, we review existing results showing that irreversible processes converge faster to their steady state than reversible ones. We show how this behaviour appears in the hydrodynamic limit of such processes, as described by macroscopic fluctuation theory, and we provide a quantitative expression for the acceleration of convergence in this setting. We give a geometrical interpretation of this acceleration, in terms of currents that are under time-reversal and orthogonal to the free energy gradient, which act to drive the system away from states where (reversible) gradient-descent dynamics result in slow convergence to equilibrium.
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http://dx.doi.org/10.1007/s10955-017-1805-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979539PMC
May 2017

Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.

Brain 2017 02 23;140(2):319-332. Epub 2016 Dec 23.

Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK

SEE EISSA AND SCHEVON DOI101093/AWW332 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
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http://dx.doi.org/10.1093/brain/aww299DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5278304PMC
February 2017

Universality in human cortical folding in health and disease.

Proc Natl Acad Sci U S A 2016 Nov 24;113(45):12820-12825. Epub 2016 Oct 24.

Instituto de Física, Universidade Federal do Rio de Janeiro, RJ 21941-909, Rio de Janeiro, Brazil.

The folding of the cortex in mammalian brains across species has recently been shown to follow a universal scaling law that can be derived from a simple physics model. However, it was yet to be determined whether this law also applies to the morphological diversity of different individuals in a single species, in particular with respect to factors, such as age, sex, and disease. To this end, we derived and investigated the cortical morphology from magnetic resonance images (MRIs) of over 1,000 healthy human subjects from three independent public databases. Our results show that all three MRI datasets follow the scaling law obtained from the comparative neuroanatomical data, which strengthens the case for the existence of a common mechanism for cortical folding. Additionally, for comparable age groups, both male and female brains scale in exactly the same way, despite systematic differences in size and folding. Furthermore, age introduces a systematic shift in the offset of the scaling law. In the model, this shift can be interpreted as changes in the mechanical forces acting on the cortex. We also applied this analysis to a dataset derived from comparable cohorts of Alzheimer's disease patients and healthy subjects of similar age. We show a systematically lower offset and a possible change in the exponent for Alzheimer's disease subjects compared with the control cohort. Finally, we discuss implications of the changes in offset and exponent in the data and relate it to existing literature. We, thus, provide a possible mechanistic link between previously independent observations.
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http://dx.doi.org/10.1073/pnas.1610175113DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5111660PMC
November 2016