Cortical Ripples During NREM Sleep And Waking in Humans Part 1

Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement sleep (NREM). Recently, cortical ripples in humans have been shown to enfold the replay of neuron firing patterns during cued recall. Here, using intracranial recordings from 18 patients (12 female), we show that cortical ripples also occur during NREM in humans, with similar density, oscillation frequency (;90 Hz), duration, and amplitude to waking. 

Hippocampal ripples are an important neuron activity in the human brain and have a very important impact on the brain's memory function. Research shows that the stability and regularity of hippocampal ripples are closely related to the strength of memory.

Positive emotions and emotions can promote the stability and regularity of human hippocampal ripples, thereby improving the memory level of the human brain. This means that as long as we face life positively and maintain a happy attitude, hippocampus ripples can work more efficiently, thereby helping us remember, think, learn, and create better.

Bad emotions and negative attitudes will affect the normal work of hippocampal ripples and reduce memory function. Therefore, we must always maintain a good mood and an optimistic attitude, to better stimulate the potential of memory.

In addition, good living habits are also an important factor in ensuring the stability and regularity of hippocampal ripples. We should get enough sleep, listen to music, participate in sports, etc. to maintain physical and mental health from different aspects, and gradually establish a high-quality life routine. Only in this way can we make better use of the memory of the human brain, strengthen our own learning and creative abilities, and make life more colorful.

In short, the relationship between hippocampal ripples and memory is inseparable. Only through positivity, good living habits and a positive emotional attitude can the memory potential of the human brain be stimulated, allowing us to better utilize the functions of the human brain and enjoy a better life. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in a variety of ways.

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Ripples occurred in all cortical regions with similar characteristics, unrelated to putative hippocampal connectivity, and were less dense and robust in higher association areas. Putative pyramidal and interneuron spiking phase-locked to cortical ripples during NREM, with phase delays consistent with ripple generation through pyramidal–interneuron feedback. Cortical ripples were smaller in amplitude than hippocampal ripples but were similar in density, frequency, and duration. 

Cortical ripples during NREM typically occur just before the upstate peak, often during spindles. Upstates and spindles have previously been associated with memory consolidation, and we found that cortical ripples grouped cofiring between units within the window of spike-timing-dependent plasticity. Thus, human NREM cortical ripples are as follows: ubiquitous and stereotyped with a tightly focused oscillation frequency; similar to hippocampal ripples; associated with upstates and spindles; and associated with unit cofiring. These properties are consistent with cortical ripples possibly contributing to memory consolidation and other functions during NREM in humans.

Keywords: 

cortex; hippocampus; humans; ripples; sleep; waking.

Significance Statement

In rodents, hippocampal ripples organize replay during sleep to promote memory consolidation in the cortex, where ripples also occur. However, evidence for cortical ripples in human sleep is limited, and their anatomic distribution and physiological properties are unexplored. Here, using human intracranial recordings, we demonstrate that ripples occur throughout the cortex during waking and sleep with highly stereotyped characteristics. 

During sleep, cortical ripples tend to occur during spindles on the down-to-upstate transition and thus participate in a sequence of sleep waves that is important for consolidation. Furthermore, cortical ripples organize single-unit spiking with timing optimal to facilitate plasticity. Therefore, cortical ripples in humans possess essential physiological properties to support memory and other cognitive functions.

Introduction

Hippocampal ripples have been extensively studied in rodents during non-rapid eye movement sleep (NREM) when they mark the replay of events from the prior waking period and are critical for memory consolidation in the cortex (Wilson and McNaughton, 1994; Girardeau et al., 2009; Ego-Stengel and Wilson, 2010; Buzsáki, 2015; Maigret et al., 2016). 

They are associated with cortical replay (Ji and Wilson, 2007; Peyrache et al., 2009; L. A. Johnson et al., 2010), and with cortical sleep waves (spindles, downstates, upstates) (Siapas and Wilson, 1998), a relationship crucial for consolidation (Latchoumane et al., 2017). Rat hippocampal ripples comprise a;140 Hz oscillation riding on the peak of a;70 ms duration sharp-wave, followed by a slower local potential (Buzsáki, 2015). 

Human hippocampal sharp-wave–ripples also occur during NREM with similar temporal relationships to cortical spindles and down-to-upstates, and similar hippocampal topography, but with a median frequency of 80-90 Hz (Staresina et al., 2015; Jiang et al., 2019a,b,c).

Recently, ripples were found in rat association cortex but not primary sensory or motor cortices during sleep, with increased coupling to hippocampal ripples in sleep following learning (Khodagholy et al., 2017). An earlier study reported ripples in the waking and sleeping cat cortex, especially during NREM (Grenier et al., 2001). In humans, cortical ripples during waking were more frequently found in the lateral temporal than rolandic cortex, and coupled to parahippocampal gyrus ripples more often before correct paired-associates recall (Vaz et al., 2019). 

Lateral temporal units fired in phase with the local waking ripples in patterns previously observed during learning (Vaz et al., 2020). Waking hippocampal ripples were also associated with cortical activity patterns selective for faces and buildings during free recall (Norman et al., 2019). Evidence for cortical ripples during NREM in humans is limited, but a previous study indicated that they may be suppressed during, and increased following, the cortical downstate (von Ellenrieder et al., 2016).

Thus, there is an emerging appreciation that, in humans and rodents, hippocampal and cortical ripples play an important role in memory during both sleep and waking. However, many fundamental questions remain unresolved. The essential characteristics of ripples have not been compared between the cortex and hippocampus, or between sleep and waking, so it is unclear how ripples may differ between their putative roles of supporting consolidation versus recall, or indeed if they represent the same phenomenon. 

Knowledge of the distribution of ripples across different cortical areas during waking is limited and during sleep is essentially absent. The relations between cortical ripples and local sleep spindles, downstates, and upstates have not been determined. Such relations could support the role of cortical ripples in consolidation, as would increase cofiring between neurons within the window of spike-timing-dependent plasticity (STDP). 

Furthermore, the relationships of human cortical pyramidal and inhibitory cell-firing to ripples and each other, important for understanding ripple generation, have not been determined.

Here, using intracranial stereoelectroencephalography (SEEG) recordings, we show that cortical ripples are generated during NREM in humans, and we provide the first comprehensive characterization of cortical ripples. 

Ripples with a stereotyped, tightly focal oscillation frequency of;90 Hz and duration of;70 ms were ubiquitous throughout the cortex during waking and NREM, although slightly less dense and robust in association areas, and with no relationship to putative hippocampal connectivity. We found that cortical ripples are similar to hippocampal in oscillation frequency, density, and duration. Cortical ripples in NREM coupled strongly to down-to-upstates, and less often to spindles, consistent with a possible role in memory replay. 

Using single-unit recordings from a cortical microarray, we identify the probable generating circuits of cortical ripples and show that units cofire during ripples at short delays that are optimal for STDP. Thus, human cortical ripples during NREM have the necessary physiological properties to facilitate replay-guided plasticity. However, the ubiquity and stereotypy of human ripples across structures and states are also consistent with a more general functional role.

Materials and Methods

Patient selection. Data from a total of 18 patients (12 female, 30.0 6 12.2 years old) with pharmaco-resistant epilepsy undergoing intracranial recording for seizure onset localization preceding surgical treatment were included in this study (Table 1). Patients whose SEEG recordings were analyzed were only included in the study if they had no prior brain surgery; background EEG (except epileptiform transients) in the normal range; and electrodes implanted in what was eventually found to be nonlesional, nonepileptogenic cortex, as well as nonlesional, non epileptogenic hippocampus (such areas were suspected to be part of the focus before implantation, or were necessary to pass through to reach suspected epileptogenic areas).

Furthermore, 1 of these patients (a 51-year-old right-handed female) was also implanted with an intracranial microelectrode (Utah Array) into tissue that was suspected based on a presurgical evaluation to be included within the region of the therapeutic resection. The implantation of the array did not affect clinical monitoring. It was later resected to gain access to the surgical focus beneath, the electrode was determined not to be implanted in an epileptogenic zone, and no seizures originated from the region of the array.

Patients were excluded from the study if they had prior brain surgery or did not have nonlesioned hippocampal and cortical channels that were not involved in the early stage of the seizure discharge and did not have frequent interictal activity or abnormal local field potentials (LFPs). Utah Array patients were only included in the study if they had at least 20 pyramidal cells (PYs) and 20 interneurons (INs). Based on these criteria, 18 patients were included in this study of 84. All patients gave fully informed written consent for their data to be used for research as monitored by the local Institutional Review Boards at Cleveland Clinic and Partners HealthCare (including Massachusetts General Hospital).

Intracranial recordings. Patients were implanted with intracranial electrodes for;7 d with continuous recordings for seizure onset localization. SEEG electrode implantation and targeting were made for purely clinical purposes. SEEG recordings were collected with a Nihon Kohden JE-120 amplifier at 1000 Hz sampling (Patients S1-S17). Standard clinical electrodes were 0.8 mm in diameter, with 10-16 contacts of length 2 mm at 3.5-5 mm pitch (;150 contacts/patient).

Microelectrode recordings from 1 patient implanted with a Utah Array were also analyzed (200 min of NREM). The Utah Array is a 10
10 microelectrode array with corners omitted and a 400 mm contact pitch (Waziri et al., 2009; Keller et al., 2010; Fernández et al., 2014). Each silicon probe is 1 mm long with a base of 35-75 mm that tapers to 3- 5 mm. The probes are insulated, except for the platinum-coated tip. Data were acquired at 30 kHz (Blackrock Microsystems) with a 0.3-7.5 kHz bandpass. Data were recorded concerning a distant reference wire.

Electrophysiology preprocessing. Offline data preprocessing was performed in MATLAB 2019b, and LFPs were inspected visually using the FieldTrip toolbox (Oostenveld et al., 2011). SEEG data were downsampled to 1000 Hz with anti-aliasing and 60 Hz notch filtered (zero-phase) with 60 Hz harmonics up to 480 Hz. Transcortical contact pairs were identified using both anatomic location (using the preoperative MRI aligned to the postoperative CT), and physiological properties (high amplitude, coherence, and inversion of spontaneous activity between contacts), and selected such that no 2 pairs shared a contact. All SEEG analyses were performed using bipolar derivations between adjacent references in cortical or hippocampal gray matter to ensure that activity was locally generated (Mak-McCully et al., 2015).

Channel selection. Channels were excluded from analysis if they were in lesioned tissue, involved in the early stages of the seizure discharge, or had frequent interictal activity or abnormal LFPs. From the total 2129 bipolar channels (1202 left hemisphere) of the 17 SEEG patients (S1- S17), 28 hippocampal (16 left hemisphere) and 273 transcortical (133 left hemisphere) bipolar channels were selected for the analyses (Table 1). Most channels were rejected because they did not constitute a transcortical pair as described above. 

First, most bipolar pairs were in the white matter and thus did not record focal cortical activity. In addition, many channels were rejected for the related criterion that one of the contacts was in common with another bipolar pair that was already selected. This was done because a common contact means that the two bipolar pairs would not provide independent measurements. Polarity was corrected for individual bipolar channels such that downstates were negative and upstates were positive. This was accomplished by ensuring that negative peaks during NREM were associated with decreased and positive peaks were associated with increased mean 6100 ms 70-190 Hz analytic amplitude, an index of cell-firing that is strongly modulated by downstates and upstates (Csercsa et al., 2010).

Electrode localization. Cortical surfaces were reconstructed from the preoperative whole-head T1-weighted structural MR volume using the standard FreeSurfer recon-all pipeline (Fischl, 2012). Atlas-based automated parcellation (Fischl et al., 2004) was used to assign anatomic labels to regions of the cortical surface in the Destrieux atlas (Destrieux et al., 2010). 

In addition, automated segmentation was used to assign anatomic labels to each voxel of the MR volume, including identifying voxels containing hippocampal subfields (Iglesias et al., 2015). To localize the SEEG contacts, the postimplant CT volume was registered to the MR volume, in standardized 1 mm isotropic FreeSurfer space, using the general registration module (H. Johnson et al., 2007) in 3D Slicer (Fedorov et al., 2012). The position of each SEEG contact, in FreeSurfer coordinates, was then determined by manually annotating the centroids of the electrode contact visualized in the coregistered CT volume.
Each transcortical contact pair was assigned an anatomic parcel from the atlas above by ascertaining the parcel identities of the surface vertex closest to the contact pair midpoint. Subcortical contacts were assigned an anatomic label corresponding to the plurality of voxel segmentation labels within a 2-voxel radius. Transcortical contact pair locations were registered to the average template brain for visualization by spherical morphing (Fischl et al., 1999). 

To plot values on a template brain, channel means were averaged for each cortical region, using amalgamations of the parcels from Desikan et al. (2006) (Table 2), with the two hemispheres combined, and then morphed onto a left hemisphere ico5 average template. White-matter streamline distances between channels were computed using the 360 parcels of the HCP-MMP1.0 atlas (Glasser et al., 2016), as determined by probabilistic diffusion MRI tractography (Behrens et al., 2007), and are population averages from Rosen and Halgren (2021). When two channels were in the same HCP parcel, the distance was considered to be 0.

Time-frequency analyses. Average time-frequency plots of the ripple event-related spectral power were generated from the broadband LFP using EEGLAB (Delorme and Makeig, 2004). Event-related spectral power was calculated from 1 Hz to the Nyquist frequency (500 Hz) with 1 Hz resolution with ripple centers at t = 0 by computing and averaging fast Fourier transforms with Hanning window tapering. 

Each 1 Hz bin of the time-frequency matrix was normalized concerning the mean power at 2000 to 1500 ms and masked with two-tailed bootstrapped significance (N = 200) with false discovery rate (FDR) correction and a = 0.05 using 2000 to 1500 ms as baseline. Grand average time-frequency plots were generated by averaging the average time-frequency plots of all channels for a given region (i.e., neocortex or hippocampus) and state (i.e., NREM or waking).

Sleep and waking epoch selection. 

Epochs included in the study did not fall within at least 1 h of a seizure and were not contaminated with frequent interictal spikes (IISs) or artifacts. NREM periods were selected from continuous overnight recordings where the d (0.5-2 Hz) analytic amplitude from the cortical channels was persistently increased (Table 1). Sleep epochs were confirmed by visual inspection to have normal-appearing downstates, upstates, and spindles. 

Downstates, upstates, and spindles were also automatically detected, and quantification of these events showed they had the densities, amplitudes, and frequencies that are characteristic of NREM (see details in Detection of downstates, upstates, and sleep spindles). Waking periods were selected from continuous daytime recordings that had persistently low cortical d as well as high cortical a (8-12 Hz), b (20-40 Hz), and high g (70-190 Hz) analytic amplitudes. When the data included EOG (N = 15/17 SEEG patients), waking periods also required that the 0.5-40 Hz analytic amplitude of the EOG trace was increased. Waking epochs were required to be separated from periods of increased d analytic amplitude by at least 30 min.

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