Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tGraph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.Funding was from Epilepsy Research UK (http://www.epilepsyresearch.org.uk) via grant number A1007 and the Medical Research Council (http://www.mrc.ac.uk) via grants (MR/K013998/1 and G0701310)

A critical role for network structure in seizure onset: a computational modeling approach

Published onlineJournal ArticleRecent clinical work has implicated network structure as critically important in the initiation of seizures in people with idiopathic generalized epilepsies. In line with this idea, functional networks derived from the electroencephalogram (EEG) at rest have been shown to be significantly different in people with generalized epilepsy compared to controls. In particular, the mean node degree of networks from the epilepsy cohort was found to be statistically significantly higher than those of controls. However, the mechanisms by which these network differences can support recurrent transitions into seizures remain unclear. In this study, we use a computational model of the transition into seizure dynamics to explore the dynamic consequences of these differences in functional networks. We demonstrate that networks with higher mean node degree are more prone to generating seizure dynamics in the model and therefore suggest a mechanism by which increased mean node degree of brain networks can cause heightened ictogenicity.Medical Research Counci

Incommensurate Transverse Anisotropy Induced by Disorder and Spin-Orbit-Vibron Coupling in Mn12-acetate

It has been shown within density-functional theory that in Mn$_{12}$-acetate there are effects due to disorder by solvent molecules and a coupling between vibrational and electronic degrees of freedom. We calculate the in-plane principal axes of the second-order anisotropy caused by the second effect and compare them with those of the fourth-order anisotropy due to the first effect. We find that the two types of the principal axes are not commensurate with each other, which results in a complete quenching of the tunnel-splitting oscillation as a function of an applied transverse field.Comment: Will be presented at MMM conference 200

A first look at dust lifting and dust storms near the south pole of Mars with a mesoscale model

Surface wind stresses and dust lifting in the south polar region of Mars are examined with a three-dimensional numerical model. The focus of this study is the middle to late southern spring period when cap-edge dust lifting events are observed. Mesoscale model simulations of high southern latitudes are conducted at three dates within this season (L_s = 225°, 255°, and 310°). Assuming that dust injection is related to the saltation of sand-sized grains or aggregates, the Mars MM5 mesoscale model predicts surface wind stresses of sufficient strength to initiate movement of sand-sized particles (∼100 μm), and hence dust lifting, during all three periods. The availability of dust and/or sand-sized particles is not addressed within this study. Instead, the degree to which the existence of sufficiently strong winds limit dust injection is examined. By eliminating forcing elements from the model, the important dynamical modes generating high wind stresses are isolated. The direct cap-edge thermal contrast (and topographic slopes in some locations) provides the primary drive for high surface wind stresses at the cap edge, while sublimation flow is not found to be particularly important, at these three dates. Simulations in which dust is injected into the lowest model layer when wind stresses exceed a threshold show similar patterns of atmospheric dust to those seen in recent observations. Comparison between these simulations and those without active dust injection shows no signs of consistent positive or negative feedback due to dust clouds on the surface wind stress fields during the late spring season examined here

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Magnetic resonance imaging-guided phase 1 trial of putaminal AADC gene therapy for Parkinson's disease.

ObjectiveTo understand the safety, putaminal coverage, and enzyme expression of adeno-associated viral vector serotype-2 encoding the complementary DNA for the enzyme, aromatic L-amino acid decarboxylase (VY-AADC01), delivered using novel intraoperative monitoring to optimize delivery.MethodsFifteen subjects (three cohorts of 5) with moderately advanced Parkinson's disease and medically refractory motor fluctuations received VY-AADC01 bilaterally coadministered with gadoteridol to the putamen using intraoperative magnetic resonance imaging (MRI) guidance to visualize the anatomic spread of the infusate and calculate coverage. Cohort 1 received 8.3 × 1011 vg/ml and ≤450 μl per putamen (total dose, ≤7.5 × 1011 vg); cohort 2 received the same concentration (8.3 × 1011 vg/ml) and ≤900 μl per putamen (total dose, ≤1.5 × 1012 vg); and cohort 3 received 2.6 × 1012 vg/ml and ≤900 μl per putamen (total dose, ≤4.7 × 1012 vg). (18)F-fluoro-L-dihydroxyphenylalanine positron emission tomography (PET) at baseline and 6 months postprocedure assessed enzyme activity; standard assessments measured clinical outcomes.ResultsMRI-guided administration of ascending VY-AADC01 doses resulted in putaminal coverage of 21% (cohort 1), 34% (cohort 2), and 42% (cohort 3). Cohorts 1, 2, and 3 showed corresponding increases in enzyme activity assessed by PET of 13%, 56%, and 79%, and reductions in antiparkinsonian medication of -15%, -33%, and -42%, respectively, at 6 months. At 12 months, there were dose-related improvements in clinical outcomes, including increases in patient-reported ON-time without troublesome dyskinesia (1.6, 3.3, and 1.5 hours, respectively) and quality of life.InterpretationNovel intraoperative monitoring of administration facilitated targeted delivery of VY-AADC01 in this phase 1 study, which was well tolerated. Increases in enzyme expression and clinical improvements were dose dependent. ClinicalTrials.gov Identifier: NCT01973543 Ann Neurol 2019;85:704-714

Cyclones, tides, and the origin of a cross-equatorial dust storm on Mars

We investigate the triggering mechanism of a cross-equatorial dust storm observed by Mars Global Surveyor in 1999. This storm, which had a significant impact on global mean temperatures, was seen in visible and infrared data to commence with the transport of linear dust fronts from the northern high latitudes into the southern tropics. However, other similar transport events observed in northern fall and winter did not lead to large dust storms. Based on off-line Lagrangian particle transport analysis using a high resolution Mars general circulation model, we propose a simple explanation for the diurnal, seasonal and interannual variability of this type of frontal activity, and of the resulting dust storms, that highlights the cooperative interaction between northern hemisphere fronts associated with low pressure cyclones and tidally-modified return branch of the Hadley circulation