Temporal evolution of beta bursts in the parkinsonian cortical and basal ganglia network

Cagnan, Hayriye; Mallet, Nicolas; Moll, Christian K.E.; Gulberti, Alessandro; Holt, Abbey B.; Westphal, Manfred; Gerloff, Christian; Engel, Andreas K.; Hamel, Wolfgang; Magill, Peter J.; Brown, Peter; Sharott, Andrew

doi:10.1073/pnas.1819975116

Cited by 116 publications

(135 citation statements)

References 49 publications

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“…This growing body of evidence supports the hypothesis that longer duration beta bursts in subcortical structures, equating to longer periods of beta oscillations and synchrony, represent pathological or impaired sensorimotor processing in Parkinson's disease (Anidi et al, 2018;Cagnan et al, 2019;Deffains et al, 2018;Lofredi et al, 2019b;Tinkhauser et al, 2018Tinkhauser et al, , 2017aTinkhauser et al, , 2017b and that one mechanism of STN DBS is to remove pathological sensorimotor network processing, while leaving intact normal physiological processing (Anidi et al, 2018;Holt et al, 2019;Tinkhauser et al, 2017a;Torrecillos et al, 2018). The discovery that averaged beta band power was attenuated during increasing intensities of DBS led to its successful use as the control variable in closed loop DBS studies Eusebio et al, 2011;Little et al, 2016aLittle et al, , 2016bLittle et al, , 2013Piña-Fuentes et al, 2019Rosa et al, 2017Rosa et al, , 2015Syrkin-Nikolau et al, 2017;Velisar et al, 2019;Whitmer et al, 2012).…”

Section: Beta Burst Duration As a Biomarker Of The Efficacy Of Therapsupporting

confidence: 55%

A Novel Method for Calculating Beta Band Burst Durations in Parkinson’s Disease Using a Physiological Baseline

Anderson

Neuville

Kehnemouyi

et al. 2020

Preprint

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Parkinson's disease, Deep Brain Stimulation HighlightsA novel method for measuring variability in subthalamic local field potential oscillations in Parkinson's disease using a physiological baseline of power.Modeling normal brain activity using a physiological 1/f spectrum.Burst durations depend on choice of bandwidth and center frequency.Defining an inert frequency band whose mean burst duration overlap the physiological 1/f spectrum, from which the baseline was determined.Burst durations progressively shortened during increasing intensities of deep brain stimulation. ABSTRACT BackgroundPathological bursts of neural activity in Parkinson's disease present as exaggerated subthalamic neuronal oscillations in the 8-30 Hz frequency range and are related to motor impairment. New MethodThis study introduces a novel method for determining burst dynamics using a baseline that matches physiological 1/f spectrum activity. We used resting state local field potentials from people with Parkinson's disease and a simulated 1/f signal to measure beta burst durations, to demonstrate how tuning parameters (i.e., bandwidth and center frequency) affect burst durations, to compare this with high power threshold methods, and to study the effect of increasing neurostimulation intensities on burst duration. ResultsBurst durations calculated using the Anderson method captured the longest and broadest distribution of burst durations in a pathological beta band compared to previous methods. Mean beta band burst durations were significantly shorter on compared to off neurostimulation (p = 0.011), and their distribution was shifted towards that of the physiological 1/f spectrum during increasing intensities of stimulation. Comparison with Existing MethodExisting methods of measuring local field potential power either lack temporal specificity to detect bursts (power spectral density diagrams and spectrograms) or include only higher power bursts and portions of the neural signal. ConclusionsWe suggest that this novel method is well suited to quantify the full range of fluctuations in beta band neural activity in the Parkinsonian brain. This method may reveal more relevant feedback biomarkers than averaged beta band power for future closed loop algorithms.

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Section: Beta Burst Duration As a Biomarker Of The Efficacy Of Therapsupporting

confidence: 55%

A Novel Method for Calculating Beta Band Burst Durations in Parkinson’s Disease Using a Physiological Baseline

Anderson

Neuville

Kehnemouyi

et al. 2020

Preprint

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“…This could be a result of both alleviating changes in the operating point of STN-GPe network as result of striatal inactivation (increased GPe firing rates [65]) or/and the lack of entrainment of GPe neurons into bursts. The GPe bursting could in turn entrain STN neurons to burst with large bouts of synchronized inhibition, which has been also suggested by [64] because the phase of prototypical GPe neurons leads that of STN neurons by a cycle. On the other hand, STN and GPe could also be entrained simultaneously by cortical β-bursts directly or via striatum respectively [64].…”

Section: Control Of the Amplitude And Duration Of β Band Oscillationmentioning

confidence: 78%

“…The β-oscillation burst duration and β-oscillation burst amplitude in humans [20] as well as non-human primates [63] is positively correlated that is, stronger oscillatory bursts also last longer. Recent data also suggests a positive correlation between beta amplitude and duration in 6-OHDA lesioned rats, however it is stronger in GPe as compared to STN [64].…”

Section: Control Of the Amplitude And Duration Of β Band Oscillationmentioning

confidence: 94%

“…It is unclear how these neurons are entrained to produce spike bursts. The spike bursts in the GPe neurons could be caused by spike bursts in striatal neurons [64,65] in rats. In 6-OHDA lesioned rats, with an increase in number of spikes/burst in striatal neurons, GPe neurons show an increased burst index [65].…”

Section: Control Of the Amplitude And Duration Of β Band Oscillationmentioning

confidence: 99%

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Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

Bahuguna

Sahasranamam²,

Kumar

2020

PLoS Comput Biol

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The excess of 15-30 Hz (β-band) oscillations in the basal ganglia is one of the key signatures of Parkinson's disease (PD). The STN-GPe network is integral to generation and modulation of β band oscillations in basal ganglia. However, the role of changes in the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations has remained unclear. In order to uncouple their effects, we studied the dynamics of STN-GPe network using numerical simulations. In particular, we used a neuron model, in which firing rates and spike bursting can be independently controlled. Using this model, we found that while STN firing rate is predictive of oscillations, GPe firing rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent. That is, only when the network was operating in a state close to the border of oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the β band oscillations. In these network states, an increase in GPe bursting enhanced the oscillations whereas an equivalent proportion of spike bursting in STN suppressed the oscillations. These results provide new insights into the mechanisms underlying the transient β bursts and how duration and power of β band oscillations may be controlled by an interplay of GPe and STN firing rates and spike bursts.

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“…to threshold based stimulation [46]. Moreover, it 686 appears that pathological synchronization is established earlier than common thresholds 687 used to define beta bursts [47]. Non-linear information not present in the features 688 studied in [46] (windowed power spectra) may provide additional predictive power, and 689 linear surrogates estimated on a slower time scale than real time could provide a useful 690 baseline to compare current recordings against.…”

mentioning

confidence: 99%

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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Temporal evolution of beta bursts in the parkinsonian cortical and basal ganglia network

Cited by 116 publications

References 49 publications

A Novel Method for Calculating Beta Band Burst Durations in Parkinson’s Disease Using a Physiological Baseline

A Novel Method for Calculating Beta Band Burst Durations in Parkinson’s Disease Using a Physiological Baseline

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations

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

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