Surrogate data for hypothesis testing of physical systems

Lancaster, Gemma; Iatsenko, Dmytro; Pidde, Aleksandra; Ticcinelli, Valentina; Stefanovska, Aneta

doi:10.1016/j.physrep.2018.06.001

Cited by 346 publications

(293 citation statements)

References 226 publications

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“…To better understand the disease and refine therapies, 25 we relate the duration of these bursts to properties of the system generating them in the 26 pathological state and in a proxy of the healthy state. The data suggest that the system 27 generating bursts is more complex in the pathological state, and we show that a 28…”

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confidence: 51%

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

Duchet

Ghezzi

Weerasinghe

et al. 2020

Preprint

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1Parkinson's disease motor symptoms are associated with an increase in subthalamic 2 nucleus beta band oscillatory power. But these oscillations are phasic, and there is a 3 growing body of evidence suggesting that beta burst duration may be of critical 4 importance to motor symptoms, making insights into the dynamics of beta bursting 5 generation valuable. In this study, we ask the question "Can average burst duration 6 reveal how dynamics change between the ON and OFF medication states?". Our 7 analysis of local field potentials from the subthalamic nucleus demonstrates using linear 8 surrogates that the system generating beta oscillations acts in a more non-linear regime 9 OFF medication and that the change in the degree of non-linearity is correlated with 10 motor impairment. Further, we pinpoint specific dynamical changes responsible for 11 changes in the temporal patterning of beta oscillations between medication states by 12 fitting to data biologically inspired models, and simpler beta envelope models. Finally, 13 we show that the non-linearity can be directly extracted from average burst duration 14 profiles under the assumption of constant noise in envelope models. This reveals that 15 average burst duration profiles provide a window into burst dynamics, which may 16 underlie the success of burst duration as a biomarker. In summary, we demonstrate a 17 relationship between average burst duration profiles, dynamics of the system generating 18 beta oscillations, and motor impairment, which puts us in a better position to 19 understand the pathology and improve therapies such as deep brain stimulation. 20 April 27, 2020 1/39 65 OFF medication states to changes in dynamical properties of the system generating 66 beta oscillations? 67In previous studies, STN beta bursts have been mostly studied based on one 68 arbitrary threshold of the beta envelope as events above the threshold, potentially with 69April 27, 2020 2/39 a minimum duration condition (examples of various thresholds shown in Fig 1A). 70Average burst duration and amplitude profiles describing the average burst duration 71 and amplitude for a range of thresholds (see Fig 1A and Fig 1B for an illustration of 72 average burst duration profiles) have been introduced [13,14]. However, they played a 73 minor role and have not been considered systematically on an individual patient basis. 74 Here, we leave behind the arbitrary choice of a threshold by relying on profiles across 75 thresholds to provide an unbiased characterisation of beta oscillation temporal 76 patterning. It has been established that STN beta burst duration is a better metric 77 than burst amplitude to distinguish between the healthy and pathological states in an 78 animal model of PD [19]. We begin our study by investigating in STN recordings of PD 79 patients whether average burst duration profiles are better at distinguishing between the 80 ON and OFF medication states than average burst amplitude profiles. We only observe 81 changes in temporal patterning of beta oscillations in...

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confidence: 51%

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

Duchet

Ghezzi

Weerasinghe

et al. 2020

Preprint

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“…For this we employ phase locking values (PLV) (Lachaux et al, 1999) that are a statistical measure for similarity between the phases of two signals, hence defined as where the phase difference θ i ( m ) − θ j ( m ) between the regions i and j is calculated at times m = 1 … M . The same procedure is also applied to surrogate time-series to find the level of statistically significant phase coherence (Lancaster et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

Mascaro

Falotico

Petkoski

et al. 2020

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2Being able to replicate real experiments with computational simulations is a unique opportunity 3 to refine and validate models with experimental data and redesign the experiments based on 4 simulations. However, since it is technically demanding to model all components of an experiment, 5 traditional approaches to modeling reduce the experimental setups as much as possible. In this 6 1 Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationsstudy, our goal is to replicate all the relevant features of an experiment on motor control and 7 motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous 8 integration of new experimental data into a computational modeling framework. First, results show 9 that we could reproduce experimental object displacement with high accuracy via the simulated 10 embodiment in the virtual world by feeding a spinal cord model with experimental registration of the 11 cortical activity. Second, by using computational models of multiple granularities, our preliminary 12 results show the possibility of simulating several features of the brain after stroke, from the 13 local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies 14 are proposed to merge the two pipelines. We further suggest that additional models could be 15 integrated into the framework thanks to the versatility of the proposed approach, thus allowing 16 many researchers to achieve continuously improved experimental design. 17 18 response of the environment itself, in that the output of the brain is relevant only if it has the ability to 19 impact the future and hence the input the brain receives. This "closed-loop" can be simulated in a virtual 20 world, where simulated experiments reproduce actions (output from the brain) that have consequences 21 (future input to the brain) (Zrenner et al., 2016). To the aim of reproducing in silico the complexity of real 22 experiments, different levels of modeling shall be integrated. However, since modeling all components of an 23 experiment is very difficult, traditional approaches of computational neuroscience reduce the experimental 24 setups as much as possible. An "Embodied brain" (or "task dynamics", see Zrenner et al. (2016)) approach Allegra Mascaro, Falotico, Petkoski et al. Towards closed-loop experiments and simulationscould overcome these limits by associating the modelled brain activity with the generation of behavior 25 within a virtual or real environment, i.e. an entailment between an output of the brain and a feedback 26 signal into the brain (Tessadori et al., 2012; DeMarse et al., 2001; Reger et al., 2000). The experimenter 27 can interfere with the flow of information between the neural system and environment on the one hand 28 and the state and transition dynamics of the environment on the other. Closing the loop can be performed 29 effectively by (i) validating the models on experimental data, and (ii) designing new experiments based on 30 the hypotheses f...

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“…Several methods have been established for surrogate data creation, each with its own limitations and advantages (see [19] for a review). Among many, wavelet-based methods allow to create frequency-specific surrogate data through randomization of the wavelet coefficients [20].…”

Section: Technical Background: Maximum Overlap Discrete Wavelet Transmentioning

confidence: 99%

“…S 0 (t) =A * cos(2πf 1 + θ) + w 1 (19) y(t) =A * (cos(2πf 2 + θ) + cos(2πf 3 + θ) + cos(2πf 4 + θ)) (20)…”

Section: Example 5: Information Transfer From One Source Frequency Tounclassified

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

Pinzuti

Wollsdtadt

Gutknecht

et al. 2020

Preprint

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Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in the computation of transfer entropy and entirely avoids filtering of the original signals. The approach thereby avoids well-known problems due to phase shifts or the ineffectiveness of filtering in the information theoretic setting. We also show that measuring frequency-resolved information transfer is a partial information decomposition problem that cannot be fully resolved to date and discuss the implications of this issue. Last, we evaluate the performance of our algorithm on simulated data and apply it to human magnetoencephalography (MEG) recordings and to local field potential recordings in the ferret. In human MEG we demonstrate top-down information flow in temporal cortex from very high frequencies (above 100Hz) to both similarly high frequencies and to frequencies around 20Hz, i.e. a complex spectral configuration of cortical information transmission that has not been described before. In the ferret we show that the prefrontal cortex sends information at low frequencies (4-8 Hz) to early visual cortex (V1), while V1 receives the information at high frequencies (> 125 Hz). Author SummarySystems in nature that perform computations typically consist of a large number of relatively simple but interacting parts. In human brains, for example, billions of neurons work together to enable our cognitive abilities. This well-orchestrated teamwork requires information to be exchanged very frequently. In many cases this exchange February 8, 2020 1/37 happens rhythmically and, therefore, it seems beneficial for our understanding of physical systems if we could link the information exchange to specific rhythms. We here present a method to determine which rhythms send, and which rhythms receive information. Since many rhythms can interact at both sender and receiver side, we show that the interpretation of results always needs to consider that the above problem is tightly linked to partial information decomposition -an intriguing problem from information theory only solved recently, and only partly. We applied our novel method to information transfer in the human inferior temporal cortex, a brain region relevant for object perception, and unexpectedly found information transfer originating at very high frequencies at 100Hz and then forking to be received at both similarly high but also much lower frequencies around 20Hz. These results ...

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Surrogate data for hypothesis testing of physical systems

Cited by 346 publications

References 226 publications

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

Measuring spectrally-resolved information transfer for sender- and receiver-specific frequencies

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