Temporal patterning of pulses during deep brain stimulation affects central nervous system arousal

Quinkert, Amy Wells; Schiff, Nicholas D.; Pfaff, Donald W.

doi:10.1016/j.bbr.2010.06.009

Cited by 41 publications

(32 citation statements)

References 31 publications

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“…Despite the fact that we know little about the mechanism of how temporal dynamics effect arousal, our data offer proof that temporal patterning within a pulse train can make a difference when all other physical parameters of the pulses are held constant. Under no circumstances would we assert that Nonlinear1 and -2 are unique, but instead these results show that temporal patterning of a pulse train for DBS is important, even when all other physical characteristics of the pulse train are held constant (46). These results suggest that work on the temporal dynamics of arousal systems will foster a deep understanding of how they work.…”

Section: P H a S E T R A N S I T I O Nmentioning

confidence: 86%

“…Although neuroscientists observe temporally patterned neuronal responses, especially in sensory systems (45), it is still an open question whether these patterns are actually used by the CNS. A recently published study tested the hypothesis that, in DBS that increases arousal, temporally patterned electrical pulse trains work significantly better to support the animal's initiation of behavior, compared with conventional pulse trains that are physically identical in every way except the temporal patterning (46). Total number of pulses was also held constant.…”

Section: P H a S E T R A N S I T I O Nmentioning

confidence: 99%

“…In this study (46), mice were implanted with bilateral monopolar electrodes either in the ventral hippocampus or the medial thalamus. During the study, behavioral motor activity of each mouse was measured using an infrared home cage monitoring system.…”

Section: P H a S E T R A N S I T I O Nmentioning

confidence: 99%

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Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain

Quinkert

Vimal

Weil

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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We review a concept of the most primitive, fundamental function of the vertebrate CNS, generalized arousal (GA). Three independent lines of evidence indicate the existence of GA: statistical, genetic, and mechanistic. Here we ask, is this concept amenable to quantitative analysis? Answering in the affirmative, four quantitative approaches have proven useful: (i) factor analysis, (ii) information theory, (iii) deterministic chaos, and (iv) application of a Gaussian equation. It strikes us that, to date, not just one but at least four different quantitative approaches seem necessary for describing different aspects of scientific work on GA. In this brief review, we present a global concept of brain function, generalized arousal (GA), and illustrate the application of four mathematical methods to its description and analysis. Here, in order of the sections below, we (i) use factor analysis to help prove that GA actually exists; (ii) use information theory to characterize stimuli that elicit GA; (iii) resort to the logistic equation to speculate on how GA might make use of nonlinear dynamics; and (iv) having studied GA in the context of the hunger-induced activation of behavior, characterize the behavioral data with a simple Gaussian.We have proposed that the most powerful and essential activity in any vertebrate nervous system is GA (1). As conceived, GA is universal and fundamental, initiating the activation of all behavioral responses in all vertebrate animals. The operational definition of GA is that a more-aroused animal or human is (i) more responsive to sensory stimuli in all modalities; (ii) more active motorically; and (iii) more reactive emotionally (1). GA's performance requirements are listed in Table 1.Evidence for the Existence of GA of the CNS Three independent lines of evidence indicate that generalized CNS arousal actually exists: statistical, genetic, and mechanistic.The first line of evidence derives from recently reported behavioral results that show, statistically, the influence of GA (2). We did several experiments with mice, which tapped all three components of the operational definition of GA: S (sensory alertness) measured as motor activity in response to sensory stimuli of various modalities; M (motor activity) measured as spontaneous home cage motor activity; and E (emotional reactivity) measured as motor activity and freezing behavior in a conditioned fear paradigm. These three parameters did not covary either genetically or phenotypically. We analyzed the data using factor analysis, which probes the covariance structure of a large data set-in our case it tabulates the statistical relations among various arousal-related response measures. Factor analysis as applied here "lets the subject (in our case, a mouse) tell us" the structure of its arousal functions. Application of this analysis to our five sets of experimental data allowed us to estimate the contribution of GA, measured as the most generalized, least specific factor, as revealed by an unrotated covariance matrix and a forced one...

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Section: P H a S E T R A N S I T I O Nmentioning

confidence: 86%

Section: P H a S E T R A N S I T I O Nmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain

Quinkert

Vimal

Weil

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Normal physiologic rhythms do not tend to have the frequency or phase specificity of artificial stimulation, and more varied stimuli may consequently affect neural networks differently. Poisson stimulation patterns may better reflect the stochastic firing patterns of neurons and in some cases may prove more effective that constant-frequency stimulation (Quinkert et al, 2010; Wyckhuys et al, 2010). Cross-frequency coupling has a role in spatial memory (Shirvalkar et al, 2010), and sinusoidal stimulation could provide less synchronizing input to the neural network.…”

Section: Discussionmentioning

confidence: 99%

Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter

et al. 2014

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Optogenetic channels have greatly expanded neuroscience’s experimental capabilities, enabling precise genetic targeting and manipulation of neuron subpopulations in awake and behaving animals. However, many barriers to entry remain for this technology – including low-cost and effective hardware for combined optical stimulation and electrophysiologic recording. To address this, we adapted the open-source NeuroRighter multichannel electrophysiology platform for use in awake and behaving rodents in both open and closed-loop stimulation experiments. Here, we present these cost-effective adaptations, including commercially available LED light sources; custom-made optical ferrules; 3D printed ferrule hardware and software to calibrate and standardize output intensity; and modifications to commercially available electrode arrays enabling stimulation proximally and distally to the recording target. We then demonstrate the capabilities and versatility of these adaptations in several open and closed-loop experiments, demonstrate spectrographic methods of analyzing the results, as well as discuss artifacts of stimulation.

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“…From a fundamental neuroscientific point of view, this has been conceptualized as an elevation of generalized CNS arousal (Pfaff, 2006) [6]. In the mouse, locomotion is the most elementary of behavioral responses, and in this work we utilize such movement patterns as the basis of our inference relating DBS parameter changes to behavioral effects (Leshner and Pfaff (2011) [7], Benjamini et al (2011) [8], Quinkert et al (2010, 2011, 2012) [9–11]).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Mouse Motor Activity under Deep Brain Stimulation: The Extraction of Arousal Information

2015

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In the present paper, we quantify, with a rigorous approach, the nature of motor activity in response to Deep Brain Stimulation (DBS), in the mouse. DBS is currently being used in the treatment of a broad range of diseases, but its underlying principles are still unclear. Because mouse movement involves rapidly repeated starting and stopping, one must statistically verify that the movement at a given stimulation time was not just coincidental, endogenously-driven movement. Moreover, the amount of activity changes significantly over the circadian rhythm, and hence the means, variances and autocorrelations are all time varying. A new methodology is presented. For example, to discern what is and what is not impacted by stimulation, velocity is classified (in a time-evolving manner) as being zero-, one- and two-dimensional movement. The most important conclusions of the paper are: (1) (DBS) stimulation is proven to be truly effective; (2) it is two-dimensional (2-D) movement that strongly differs between light and dark and responds to stimulation; and, (3) stimulation in the light initiates a manner of movement, 2-D movement, that is more commonly seen in the (non-stimulated) dark. Based upon these conclusions, it is conjectured that the above patterns of 2-D movement could be a straightforward, easy to calculate correlate of arousal. The above conclusions will aid in the systematic evaluation and understanding of how DBS in CNS arousal pathways leads to the activation of behavior.

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Temporal patterning of pulses during deep brain stimulation affects central nervous system arousal

Cited by 41 publications

References 31 publications

Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain

Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain

Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter

Stochastic Modeling of Mouse Motor Activity under Deep Brain Stimulation: The Extraction of Arousal Information

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