Abnormal UP/DOWN Membrane Potential Dynamics Coupled with the Neocortical Slow Oscillation in Dentate Granule Cells during the Latent Phase of Temporal Lobe Epilepsy

Ouedraogo, David W.; Lenck-Santini, Pierre-Pascal; Marti, Geoffrey; Robbe, David; Crépel, Valérie; Epsztein, Jérôme

doi:10.1523/eneuro.0017-16.2016

Cited by 8 publications

(11 citation statements)

References 91 publications

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“…We select parameter values that permit chaotic oscillations (as elicited at a ‘strange attractor’) (Izhikevich, 2007), in which the voltage dynamics follow a trajectory consistently around a point in the coordinate space to yield a non-uniform waveform shape (relative to a sinusoid, Cole & Voytek, 2017, Figure 3a,b). This rhythm is phenomenologically similar to a slow oscillation measured in the ferret visual cortex LFP (see Figure 2A of Fröhlich & McCormick, 2010) and in hippocampal LFP in rats (see the Visual Abstract of Ouedraogo et al, 2016). After simulating data using the ode45 function in MATLAB, we rescale time such that the rhythm has an approximately 1 Hz periodicity to match a slow oscillation timescale.…”

Section: Resultssupporting

confidence: 78%

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Wodeyar

Marshall

Chu

et al. 2023

Preprint

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Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically non-sinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.

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Section: Resultssupporting

confidence: 78%

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Wodeyar

Marshall

Chu

et al. 2023

Preprint

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“…The function of DG as a gate for cortical activity to prevent seizure generation fails in TLE (Hsu, 2007; Krook-Magnuson et al, 2015; Dengler and Coulter, 2016; Lu et al, 2016). Granule cells (GCs), the output neurons of DG, lose their usual sparseness due to network reorganization (Artinian et al, 2011; Dengler and Coulter, 2016; Dengler et al, 2017) making cortical excitation easier to propagate to CA3 (Behr et al, 1998; Patrylo et al, 1999; Ouedraogo et al, 2016). Even in healthy animals, repeated excitation of GCs induces seizures (Krook-Magnuson et al, 2015) and prolonged stimulation of DG causes TLE (Sloviter, 1983).…”

Section: Introductionmentioning

confidence: 99%

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

Madar

Pfammatter

Bordenave

et al. 2020

Preprint

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8Health 9 Acknowledgement: We thank Lin Lin who helped start behavioral tests in mice in the Jones lab 10 and Drs. C.E.L Stark and M.A. Yassa for allowing us to use software and materials they 11 developed.12 Abstract: 24 In temporal lobe epilepsy, the ability of the dentate gyrus to limit excitatory cortical input to the 25 hippocampus breaks down, leading to seizures. The dentate gyrus is also thought to help 26 discriminate between similar memories by performing pattern separation, but whether epilepsy 27 leads to a breakdown in this neural computation, and thus to mnemonic discrimination 28 impairments, remains unknown. Here we show that temporal lobe epilepsy is characterized by 29 behavioral deficits in mnemonic discrimination tasks, in both humans and mice. Using a recently 30 developed assay in brain slices of the same epileptic mice, we reveal a decreased ability of the 31 dentate gyrus to perform certain forms of pattern separation. This is due to a subset of granule 32 cells with abnormal bursting that can develop independently of early EEG abnormalities. 33Overall, our results linking physiology, computation and cognition in the same mice, advance 34 our understanding of episodic memory mechanisms and their dysfunction in epilepsy. 35 36 Keywords: 37 Patch-clamp, low-dose systemic kainate model, kainic acid, interictal spikes, novelty 38 recognition, firing rate, spiking patterns, similarity metrics, input-output, entorhinal cortex, 39 perforant path, EPSC, excitation/inhibition ratio, dentate gate, Hebb-Marr theory 40 41 Introduction: 42 Temporal lobe epilepsy (TLE) represents about 60% of all epilepsy cases, a third of 43 which are refractory to medication (Tellez-Zenteno and Hernandez-Ronquillo, 2012). TLE is 44DG neural pattern separation and 2) that such a failure causes mnemonic discrimination 87 impairments remain experimentally untested. 88Here we tested, in humans and mice, whether TLE is characterized by deficits in 89 mnemonic discrimination and then recorded, in brain slices from the same mice, the spiking 90 patterns of single GCs in response to parametrically varied afferent stimulation in order to gauge 91 neuronal pattern separation. 92 93 Materials and Methods: 94 Human Behavior. A mnemonic similarity task (Stark et al., 2019), also known as a 95 behavioral pattern separation (BPS) task (Stark et al., 2013), was administered to 15 patients in 96 the University of Wisconsin-Madison Epilepsy Monitoring Unit. Under an approved Institutional 97 Review Board protocol and after obtaining informed consent, the task was administered in 98 conjunction with a standard neuropsychiatric evaluation and during electroencephalographic 99 (EEG) recording, both of which are part of standard practice for diagnosing the patients' seizures. 100 Patients were 18-65 years old, male and female. Only patients with a preliminary diagnosis of 101 TLE were included in our analysis. As controls, 20 subjects without epilepsy, recruited to match 102 the patients' age and sex distributions (family members of ...

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“…While these studies focused on the neocortex in the cerebrum, whole-cell recordings from anesthetized animals have investigated other regions, such as (i) the cerebrum (including the entorhinal cortex [ 20 , 21 ], the hippocampus [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ], the basolateral amygdala [ 32 , 33 , 34 ], the piriform cortex [ 35 , 36 , 37 ], and the thalamus [ 38 ]) and even (ii) the brainstem (including the midbrain [ 39 , 40 ] and the pons [ 41 ]) and (iii) the cerebellum [ 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: In Vivo Whole-cell Recordings From Anesthetized Animalsmentioning

confidence: 99%

“…Consistent with its contribution to memory, the hippocampus is primarily responsible for Alzheimer’s disease [ 125 ] and epilepsy [ 22 , 26 ]. For instance, Šišková et al performed in vivo whole-cell recordings from hippocampal pyramidal neurons of a mouse model of Alzheimer’s disease simultaneously with high-resolution stimulated emission depletion microscopy imaging and computational modeling.…”

Section: In Vivo Whole-cell Recordings From Awake Animalsmentioning

confidence: 99%

In Vivo Whole-Cell Patch-Clamp Methods: Recent Technical Progress and Future Perspectives

Noguchi

Ikegaya

Matsumoto

2021

Sensors

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Brain functions are fundamental for the survival of organisms, and they are supported by neural circuits consisting of a variety of neurons. To investigate the function of neurons at the single-cell level, researchers often use whole-cell patch-clamp recording techniques. These techniques enable us to record membrane potentials (including action potentials) of individual neurons of not only anesthetized but also actively behaving animals. This whole-cell recording method enables us to reveal how neuronal activities support brain function at the single-cell level. In this review, we introduce previous studies using in vivo patch-clamp recording techniques and recent findings primarily regarding neuronal activities in the hippocampus for behavioral function. We further discuss how we can bridge the gap between electrophysiology and biochemistry.

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Abnormal UP/DOWN Membrane Potential Dynamics Coupled with the Neocortical Slow Oscillation in Dentate Granule Cells during the Latent Phase of Temporal Lobe Epilepsy

Abstract: Visual Abstract

Cited by 8 publications

References 91 publications

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Deficits in Behavioral and Neuronal Pattern Separation in Temporal Lobe Epilepsy

In Vivo Whole-Cell Patch-Clamp Methods: Recent Technical Progress and Future Perspectives

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