Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum

Tingley, David; Buzsáki, György

doi:10.1016/j.neuron.2019.10.012

Cited by 46 publications

(44 citation statements)

References 105 publications

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“…These findings were later corroborated by extracellular recordings from different associational areas [116,120]. The involvement of interneurons in extra-hippocampal ripples was recently demonstrated in the lateral septum, where direct optogenetic activation of GABAergic neurons resulted in ripple-like iHFOs [119]. These observations are in-line with similar in vitro results from the hippocampus [40]; yet in contrast with the hippocampus, the lateral septum is composed only of GABAergic cells, suggesting that the underlying mechanism of ripple generation in those areas could be quite distinct.…”

Section: (A) Cortical Ripples Are Embedded Within Lower Frequency Rhymentioning

confidence: 56%

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

McKenzie

Nitzan²,

English

2020

Phil. Trans. R. Soc. B

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Neural activity during ripples has attracted great theoretical and experimental attention over the last three decades. Perhaps one reason for such interest is that ripples occur during quiet waking moments and during sleep, times when we reflect and dream about what has just occurred and what we expect to happen next. The hope is that understanding such ‘offline’ activity may yield insights into reflection, planning, and the purposes of sleep. This review focuses on the mechanisms by which neurons organize during these high-frequency events. In studying ripples, broader principles have emerged that relate intrinsic neural properties, network topology and synaptic plasticity in controlling neural activity. Ripples, therefore, serve as an excellent model for studying how properties of a neural network relate to neural dynamics. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

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Section: (A) Cortical Ripples Are Embedded Within Lower Frequency Rhymentioning

confidence: 56%

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

McKenzie

Nitzan²,

English

2020

Phil. Trans. R. Soc. B

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“…This makes the LS the recipient of massive convergent excitation from the hippocampal-subicular-entorhinal population. This major anatomical connection convey hippocampal activity to its hypothalamic, mesencephalic, and brainstem projections (Tingley and Buzsáki, 2020).…”

Section: Figurementioning

confidence: 99%

BIN1 genetic risk factor for Alzheimer is sufficient to induce early structural tract alterations in entorhinal-hippocampal area and memory-related hippocampal multi-scale impairments

Daudin

Maréchal

Golgolab

et al. 2018

Preprint

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Late-onset Alzheimer Disease (LOAD) is the most common form of dementia and one of the most challenging diseases of modern society 1. Understanding the preclinical stages of AD that begins in the brain at least 2-3 decades before evidence of episodic memory defects in patients is pivotal for the design of successful approaches to delay or reverse the transition from normal brain function to cognitive impairments. Our working hypothesis is that LOAD genetic risk factors can be sufficient to generate early phenotypical changes before any changes in either Abeta or Tau. To test this hypothesis, we generated an hBIN1 mouse model based on the human BIN1 gene overexpression that we found in post-mortem brain samples from LOAD patients. BIN1 is the second important risk factor for AD, following the APOE gene 2. We identified co-deregulated gene repertoires common to both 7-week mouse hippocampus sub-regions and post-mortem brain samples from LOAD patients, demonstrating the validity of this hBIN1 model. We evidenced an early phenotype of neurodegeneration starting at 3 months with structural impairment fiber pathways quantified by high resolution (17.2T) (MRI-DTI) and related functional impacts. We found structural changes in entorhinal cortex-dentate gyrus (EC-DG) pathway known to be the earliest brain region impacted in LOAD 3–6. Similarly, the function of this pathway was impaired both in vitro and in vivo, with the changes in spine density and dendritic simplification of DG neurons, impaired EC-DG long-term potentiation (LTP) and behavioral deficits linked to object recognition episodic memory. As expected for a neurodegenerative model, we evidenced the progression of dysfunction at the morphological, functional and behavioral levels with age. Structural spreading involved impairment of fibers in somatosensory and temporal associative cortexes at month 15. Functional and behavioral spreading was characterized by impact on pattern separation of spatial episodic memory. Moreover, this neurodegeneration occurred without any detectable changes in Aβ42 and tau. Overall, these data show the possibility to identify a repertoire of molecular changes occurring both in patients and in hBIN1 mice and whose further manipulation can be instrumental to rescue or delay episodic memory defects.

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“…The LFP signal of contacts located in CA1 was extracted and converted to units of mV, along with the corresponding times and durations of labeled CA1 SPW-R events. SPW-R events were identified and labeled as described in Tingley and Buzsáki (2020). We here defined SPW-R event times as the mean of onset and offset times.…”

Section: Rat Datamentioning

confidence: 99%

“…Hence, the resulting distributions of SPW-R power are skewed as the synchrony between neurons throughout the network (i.e., correlations) greatly affects the LFP power in the SPW-R band (Csicsvari et al 2000;Schomburg et al 2012;Buzsáki 2015;Hagen et al 2016). Consequently, defining a fixed threshold for SPW-R detection based on e.g., the power or envelope of the LFP in a chosen frequency band (Csicsvari et al 1999a, b;Einevoll et al 2013;Ramirez-Villegas 2015;Norman et al 2019;Tingley and Buzsáki 2020) remains heuristic. Detection methods may however incorporate adaptive thresholding (Fritsch et al 1999;Jadhav et al 2012).…”

Section: Introductionmentioning

confidence: 99%

RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

et al. 2021

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Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks () and can be easily incorporated into existing data analysis workflows for processing experimental data.

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Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum

Cited by 46 publications

References 105 publications

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

Mechanisms of neural organization and rhythmogenesis during hippocampal and cortical ripples

BIN1 genetic risk factor for Alzheimer is sufficient to induce early structural tract alterations in entorhinal-hippocampal area and memory-related hippocampal multi-scale impairments

RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

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