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\begin{document}

\title{Scoping review of relationship between alcohol, memory consolidation,
and ripple activity: recompilation of mean methodologies to analyze
ripples}



\author[1]{Marina Ruelas}%
\author[1]{Rita Fuentes-Aguilar (Second Corresponding Author)}%
\author[2]{Laura Medina-Ceja}%
\affil[1]{Monterrey Institute of Technology and Higher Education}%
\affil[2]{University of Guadalajara}%


\vspace{-1em}



  
  \date{May 08, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Alcohol abuse is not only responsible for 5.3\% of the total deaths in
the world, but also has a substantial impact on neurological and memory
disabilities throughout the population. One extensively studied brain
area involved in cognitive functions is the hippocampus. Evidence in
several rodent models has shown that ethanol produces cognitive
impairment in hippocampal-dependent tasks and that the damage is varied
according to the stage of development at which the rodent was exposed to
ethanol and the dose. To the authors' knowledge, there is a biomarker
for cognitive processes in the hippocampus that has not been evaluated
in association with memory impairment by alcohol administration. This
biomarker is called Sharp Wave Ripples which are synchronous neuronal
population events that are well known to be involved in memory
consolidation. Methodologies for facilitation or automatic
identification of ripples and their analysis have been reported for a
wider bandwidth than Sharp Wave Ripples. This review is focused on
communicating the state-of-the art about the relationship between
alcohol, memory consolidation and ripple activity as well as the use of
the main methodologies to identify SWRs automatically.%
\end{abstract}%



\sloppy


\textbf{Scoping review of relationship between alcohol, memory
consolidation, and ripple activity: recompilation of mean methodologies
to analyze ripples}

Short running title: Relationship between alcohol, memory consolidation
and ripple activity.

Marina Ruelas\textsuperscript{a}, Laura
Medina-Ceja\textsuperscript{b,*}and Rita Q.
Fuentes-Aguilar\textsuperscript{c,*}

\textsuperscript{a} School of Engineering and Sciences, Tecnol\selectlanguage{ngerman}ógico de
Monterrey. Av. General Ramón Corona 2514, Col. Nuevo México, CP 45138,
Zapopan, Jalisco, México.

\textsuperscript{b} Laboratory of Neurophysiology, Department of
Cellular and Molecular Biology, CUCBA, University of Guadalajara, Camino
Ing. R. Padilla Sánchez 2100, Las Agujas, Nextipac, CP 45110 Zapopan,
Jalisco, Mexico.

\textsuperscript{c} Institute of Advanced Materials for Sustainable
Manufacturing, Tecnologico de Monterrey. Av. General Ramón Corona 2514,
Col. Nuevo México, CP 45138, Zapopan, Jalisco, México,

Correspondence authors:

Dr. Laura Medina-Ceja, Laboratory of Neurophysiology, Department of
Cellular and Molecular Biology, CUCBA, University of Guadalajara, Camino
Ing. R. Padilla Sánchez 2100, Las Agujas, Nextipac, CP 45110 Zapopan,
Jalisco, Mexico, e-mail laura.mceja@academicos.udg.mx.

Dr. Rita Q. Fuentes-Aguilar, Tecnológico de Monterrey, Institute of
Advanced Materials for Sustainable Manufacturing. Av. General Ramón
Corona 2514, Col. Nuevo México, Zapopan, Jalisco, México, CP 45138,
e-mail rita.fuentes@tec.mx.

\textbf{Acknowledgments}

This work was supported by the institutional grants: PROSNI-2020, 2021
and Programa Fortalecimiento a la investigaciòn 2020-2021 to LMC from
the University of Guadalajara. M. R. acknowledges a MSc. stipend from
CONACYT and a tuition scholarship from Tecnológico de Monterrey. R.Q.
F-A. acknowledges Advanced Cyberphysical Systems Lab. Figures were
created with BioRender.com.

\textbf{Abstract}

Alcohol abuse is not only responsible for 5.3\% of the total deaths in
the world, but also has a substantial impact on neurological and memory
disabilities throughout the population. One extensively studied brain
area involved in cognitive functions is the hippocampus. Evidence in
several rodent models has shown that ethanol produces cognitive
impairment in hippocampal-dependent tasks and that the damage is varied
according to the stage of development at which the rodent was exposed to
ethanol and the dose. To the authors' knowledge, there is a biomarker
for cognitive processes in the hippocampus that has not been evaluated
in association with memory impairment by alcohol administration. This
biomarker is called Sharp Wave Ripples which are synchronous neuronal
population events that are well known to be involved in memory
consolidation. Methodologies for facilitation or automatic
identification of ripples and their analysis have been reported for a
wider bandwidth than Sharp Wave Ripples. This review is focused on
communicating the state-of-the art about the relationship between
alcohol, memory consolidation and ripple activity as well as the use of
the main methodologies to identify SWRs automatically.

\textbf{Keywords:} Alcohol, cognitive impairment, identification of
ripples, memory consolidation, ripples, Sharp Wave Ripples

\textbf{Abbreviations}

CA Cornu ammonis

DG Dentate gyrus

EtOH Ethanol

HFOs High Frequency Oscillations

RMS Root mean square

SWRs Sharp wave-ripple complexes

This review has a total of 32 pages, four tables, and three figures.

\textbf{Introduction}

Memory is defined as the storage of a learned item and evidence suggests
that a brain region called the hippocampus is active during its
consolidation (Tulving and Markowitsch, 1998; Duff et al., 2020).
Several biomarkers for cognitive operations in the hippocampus have been
studied, including altered structure, functional connectivity, and
volume of hippocampus (Feng et al., 2019); however, in this review; we
concentrate on Sharp Wave-Ripple complexes (SWRs). SWRs are brief
(50-100 ms), high-frequency (120-250 Hz), synchronous events that occur
during non-rapid-eye movement sleep and ``off-line'' states of the brain
associated with consummatory behaviors and memory consolidation
(Buzsáki, 2015; Cowen et al., 2020; Oliva et al., 2020; Zhen et al.,
2021; García-Pérez et al., 2022). In addition, the effect of alcohol on
memory has been studied previously (Abrahao et al., 2017; Miyake et al.,
2020; Hamel et al., 2022), however, to the authors' knowledge, SWRs have
not been evaluated in association with memory impairment by chronic
alcohol administration.

Moreover, effective identification of ripples may be performed visually
by an expert, although it is a laborious and time-consuming work.
Methodologies for facilitation or automatic identification of ripples
and their analysis are more commonly reported for epilepsy than for
SWRs. Epileptic patients present High Frequency Oscillations (HFOs),
which are events with frequencies of 80-600 Hz (Burnos et al., 2014). As
the bandwidth of SWRs is 120-250 Hz, which is in between the bandwidth
of HFOs, the reported methodologies for HFOs may be used for the
identification of SWRs. Different approaches for automatic detection of
ripples are also reviewed in this article.

This review intends to communicate the state-of-the art about the
relationship between alcohol, memory consolidation, and ripple activity,
as well as the use of the main methodologies to identify SWRs
automatically.

\emph{1. Alcoholism as a global problem of health and study models}

Alcohol is a toxic and psychoactive substance that causes dependence and
has been consumed as a socially acceptable drug for centuries (World
Health Organization, 2018a). In 2016, 43\% of the total population (15+
years) was current drinkers and 12.5\% were former drinkers. Among
drinkers, the record of alcohol per capita consumption was 15.1 liters
per year (World Health Organization, 2018b). Alcohol consumption can
progress to alcoholism, a condition characterized by a physical
dependence on alcohol and the inability to stop or limit drinking (Wood,
2013). Globally, an estimated 237 million men and 46 million women
suffer from alcohol use disorders (Härtl and Garwood, 2018). Beyond the
significant social and economic losses to individuals and society, the
harmful use of alcohol has important health consequences. The impact of
alcohol consumption on chronic and acute health outcomes is determined
by the total volume of alcohol consumed and the pattern of drinking
(World Health Organization, 2018a).

Harmful use of alcohol is accountable for 7.1\% and 2.2\% of the global
burden of disease for males and females, respectively (World Health
Organization, 2018a). Twenty-five chronic diseases and conditions are
entirely attributable to alcohol, including alcoholic fibrosis and
sclerosis of the liver, alcoholic cardiomyopathy, and fetal alcohol
syndrome. Additionally, alcohol is a risk component in certain cancers
(e.g. mouth cancer, liver cancer, breast cancer, and nasopharynx
cancer), mental and behavioral disorders (e.g. unipolar depressive
disorders), neurological conditions (e.g. epilepsy), cardiovascular and
circulatory diseases (e.g. hypertensive heart disease and ischemic heart
disease), brain vascular diseases (e.g. ischemic stroke), and diabetes
(Shield et al., 2013; Hendricks, 2020). Of all deaths attributed to
alcohol in 2016, 28\% were due to injuries, such as those from traffic
crashes, self-harm, and interpersonal violence; 21\% due to digestive
disorders; 19\% due to cardiovascular diseases, and the remainder due to
infectious diseases, cancers, mental disorders, and other health
conditions (Härtl and Garwood, 2018).

Accordingly, alcoholism is a global health problem that requires the
study of the molecular and cellular mechanisms that produce different
acute and chronic diseases. This can be assessed with animal models. At
this respect, the resemblance in the anatomical organization, functional
development, and effects of alcohol at the same concentrations (Table 1)
between the human and rodent brain compels rodents as a commonly used
animal model (Clark and Squire, 2013).

In addition, an accurate model of alcoholism requires the following
criteria: a) alcohol must produce positive reinforcing effects, b) the
animals must consume the alcohol for its pharmacological effects and not
only for its caloric value, taste, or smell, c) the animal should orally
self-administer ethanol (EtOH) under free-choice conditions, d)
self-administration of EtOH must lead to pharmacologically relevant
blood alcohol concentrations, e) the animals should be willing to work
to obtain EtOH, f) chronic consumption of EtOH should lead to metabolic
and functional tolerance, g) physical signs of withdrawal should develop
following EtOH withdrawal after a period of chronic consumption, and h)
the animal model easily demonstrates relapse drinking after a prolonged
period of abstinence (McBride et al., 2014).

Alcohol models include different methods of ethanol administration like
consumption of EtOH in the drinking water (Augier et al., 2014),
ethanol-containing liquid diet, vapor inhalation, repeated
intraperitoneal injections, intra-gastric infusions of EtOH,
schedule-induced EtOH polydipsia, voluntary ethanol drinking paradigm,
and food restriction with alcohol as the only fluid available (Crabbe,
2014; Kliethermes, 2005; Tabakoff and Hoffman, 2000). A summary of the
principal models is shown in Table 2.

\emph{2. Relationship between alcohol, memory consolidation, and
hippocampus}

Evidence in several rodent models has shown that ethanol produces
cognitive impairment in hippocampal-dependent tasks and that the damage
varies according to the stage of development at which the rodent was
exposed to EtOH and the dose (Table 3) (White et al., 2000; Reid et al.,
2020; Mira et al., 2020). As brain development persists during childhood
and adolescence in mammals, alcohol consumption is not only risky during
the prenatal stages, but adolescence as well (Table 3). Furthermore,
adolescence is usually the age for the start of alcohol consumption and
abuse in humans (Mira et al., 2020) due to social enhancement and coping
motives (Kuntsche et al., 2005). On the other hand, alcohol-related
alterations in adult rodents are not conclusive. However, some
experiments have reported cognitive impairment in non-spatial and
spatial tasks in the Morris water maze as well as behavioral flexibility
impairment (Matthews et al., 2020; Ho et al., 2022).

In addition, several experimental evidences demonstrate that alcohol
consumption during gestation, young, and adult rodents produce physical
changes such as a reduction in neuronal cell number, brain size, density
and volume (Klintsova et al., 2007; Lee et al., 2015; Livy et al.,
2003), neurodegeneration (Bird et al., 2018), and decreased neurogenesis
(Ieraci and Herrera, 2007) that may explain cognitive impairment (Table
4). However, further investigation is needed.

Memory consolidation is one of the principal processes altered in
cognitive impairment produced by alcohol. Memory can be divided into
short-term and long-term memory. Short-term memory, called working
memory, maintains current, albeit transient, representations of
goal-relevant knowledge obtained by verbal and visual-spatial
information (Kumar et al., 2020). Short-term memory is converted into
long-term memory by a process called memory consolidation, which is
enhanced by repetition and by adding several sensory modalities or
adding emotional context (Tonegawa et al., 2018; Klinzing et al., 2019;
Girardeau and Lopes-dos-Santos, 2021). Figure 1 provides an overview of
the subdivisions of memory.

In addition, memory consolidation requires both hippocampus-dependent
and non-hippocampus-dependent processes, but we will focus on the role
of the hippocampus structure (Kibble and Halsey, 2015; Klinzing et al.,
2019). The hippocampus is a bilateral structure, meaning that the brain
has two hippocampi, which are located deep in the innermost fold of the
temporal lobe (Figure 2a,b) (Stimac, 2022). It has a seahorse-like shape
formed by the cornu ammonis (CA), further divided into four zones,
namely, CA1, CA2, CA3, and CA4 (Mira et al., 2020). The hippocampus
forms the hippocampal formation, which also includes the dentate gyrus
(DG) and the subiculum. Together, these structures play important roles
in learning and memory, and considering that the DG is one of the two
sites for neurogenesis in the mature brain (Sokolowski and Corbin, 2012;
Abbott and Nigussie, 2019). Hippocampus in mammals has a five-layered
structure, consisting mainly of pyramidal cells that have both apical
and basal dendrites. In contrast, the DG has a three-layered structure
consisting mainly of granule cells that have only apical dendrites.
Interneurons are a minority of neurons in the hippocampal formation,
making up only 10-20\% of the total, but they play a crucial role in
regulating circuit-level signaling within the hippocampus due to their
dense axonal arborization (Bird et al., 2018).

The hippocampus plays a relevant role in memory consolidation. In this
brain region, multimodal sensory and spatial information from the
entorhinal cortex via its principal trisynaptic circuit is processed and
integrated (Figure 3) (Chao et al., 2020; Park et al., 2021). In this
circuit the axons of layer II neurons of the entorhinal cortex project
through the perforant pathway into the granule cells of the DG. Granule
cell axons, termed mossy fibers, are projected into the mossy fiber
pathway to stimulate pyramidal cells in the CA3 region of the
hippocampus. Finally, the CA3 axons, called the Schaffer collaterals,
project through the Schaffer collateral pathway to make excitatory
synapses on more proximal regions of CA1 pyramidal cell dendrites. The
major output of the hippocampus is through the pyramidal neurons in the
CA1 region, which project to the subiculum before extending back to the
entorhinal cortex. Both, CA1 and the subiculum, have projections into
the fornix, primarily to the septal nuclei and the mammillary bodies;
moreover, in this circuit backprojection pathways could serve to
modulate information processing in

hippocampal CA1 (Xu et al., 2016; Martin, 2021).

As mentioned before, human and rodent brains have a resemblance in the
anatomical organization and functional development, particularly in the
hippocampal formation, which is illustrated in Figure 2. These
similarities in memory function across mammalian species compel rodents
as animal models (Clark and Squire, 2013). Furthermore, both humans and
rodents manifest SWRs in the hippocampus when they are consolidating
memory (Buzsáki, 2015).

\emph{3. Role of ripples on memory consolidation}

SWRs are bursts (50-100 ms) of cell activity in the hippocampal local
field during a pause in active exploration or while asleep. Sharp wave
ripples encompass two distinct components, namely sharp waves and
ripples, which collectively constitute this neural phenomenon. Sharp
waves refer to brief, high-amplitude waveforms that are characterized by
a rapid and synchronized depolarization followed by a slower,
hyperpolarizing phase. On the other hand, ripples are fast oscillations
that occur within the context of sharp waves. Ripples are high-frequency
oscillations in the range of 100-250 Hz and are superimposed on the
sharp wave waveform. Sharp wave ripples, as a whole, refer to the
combined occurrence of these two phenomena. The timescale at which SWRs
are presented aligns with the optimal window to induce synaptic
plasticity, and therefore, they have been accepted as a definite
biomarker for the encoding of memory fingerprints on synaptic weights
(Buzsáki, 2015; Cowen et al., 2020; Evangelista et al., 2020; Oliva et
al., 2020; Roumis and Frank, 2015). Brain rhythms, such as slow waves,
ripples, and spindles, happen at approximately the same speed in
mammals, irrespective of brain size (Buzsáki et al., 2013). Several
electroencephalography traces of mammals from the literature and their
reported characteristics have been summarized and compared by Buzsáki et
al. (2013).

Studies suggest that the CA3 region is the source of SPW in the
hippocampus, as the spatial distribution of spontaneous SPW closely
resembles that of Schaffer collateral evoked responses (Buzsáki et al.,
1983). Also, evidence demonstrates that the blockage of CA3 output of
the trisynaptic hippocampal circuit impairs the consolidation of
contextual fear memory and the CA1 ripples and the ripple-associated
reactivation of experience-dependent firing patterns of CA1 neurons,
underlie the importance of the trisynaptic circuit and SWR in the
consolidation of hippocampus-dependent memory (Nakashiba et al., 2009).
Similarly, the SWR are physiological events associated with replay
(multifactorial event) that underlie memory consolidation in the
trisynaptic hippocampal circuit and the organization of this replay is
influenced by the brain state (Buszáki, 1989, 2015), then during replay,
SWR are also influenced by other factors in the trisynaptic circuit,
such as genetic, microcircuits and behavioral states. De la Prida (2020)
analyzes, under scrutiny based on the evidence, some of these factors in
the CA1 hippocampal region, such as cell-type and input-specific
connectivity as well as radial expression of receptors and intrinsic
properties that influence the replay.

Additionally, the behavior of CA3 cells can be compared to that of
pacemaker cells, as they exhibit early firing during population events
and recruit follower cells to fire (Wittner and Miles, 2007). As
described before, these pyramidal cells have extensive axon collaterals
that project to both CA3 and CA1 regions, and the synapses they form
account for most connections within the hippocampus (Amaral and Witter,
1989). Although recent research has called into question the extent of
connectivity in CA3 (Guzman et al. 2016).

A group of interneurons cooperate to coordinate temporally and spatially
the spike content of SWRs to replay the awake neuronal sequence segments
in a compressed manner (Buzsáki, 2015). The connections between these
interneurons and the pyramidal neurons are organized in a precise and
intricate manner to allow for the generation and propagation of SWRs
(Buzsáki, 2015). Parvalbumin -positive basket cells and oriens
lacunosum-moleculare cells form local inhibitory circuits within the
hippocampus, with their axons forming perisomatic and dendritic synapses
on pyramidal neurons, respectively. These inhibitory connections help to
shape the spatiotemporal patterns of SWRs and regulate the timing and
coordination of the network activity during SWRs (Klausberger and
Somogyi, 2008). It has been demonstrated that parvalbumin -positive
basket cells fire before oriens lacunosum-moleculare during multiple
brain rhythms including ripples and theta waves (Varga et al., 2012).

Axo-axonic cells have been shown to preferentially fire just after the
peak of the theta cycles and discharge transiently at the beginning of
SWRs (Klausberger et al., 2003). Other interneurons, such as ivy cells,
appear to be only weakly modulated by SWRs (Buzsáki, 2015). However, the
exact mechanisms underlying SWRs are still an active area of research,
and further studies are needed to fully understand the complex interplay
of interneurons in SWR generation and propagation.

After a sharp wave event, there is a brief period during which
hyperpolarization occurs, ending the wave and creating a refractory
period (Buzsáki, 2015). Specific groups of active cells that encode a
particular memory tend to be preferentially replayed during SWRs (Wilson
and McNaughton, 1994). During SWR replay, newly obtained and previously
known knowledge is merged to affect judgements, plan actions, and maybe
inspire original ideas (Buzsáki, 2015).

SWRs have been shown to be critically involved in the process of
episodic memory consolidation (Jadhav et al., 2012). Spatial learning
requires remembering and choosing paths to goals (Shin et al., 2019).
Disruption of SWRs impairs spatial memory (Buzsáki, 2015), which
consolidation depends on the reactivation of hippocampal place cells
that were active during recent behavior (Oliva et al., 2020). Continuous
track of hippocampal-prefrontal ensembles throughout learning of a
spatial alternation task demonstrated that during pauses between
behavioral trajectories, reverse and forward hippocampal replay supports
an internal cognitive search of available past and future possibilities
and exhibits opposing learning gradients for prediction of past and
future behavioral paths, respectively (Shin et al., 2019).

Examination of the role of SWRs during the consolidation of social
memory---the ability of an animal to recognize and remember a member of
the same species---revealed that CA2 pyramidal neurons that are active
during social exploration of previously unknown conspecifics are
reactivated during SWRs. This suggests that SWRs originating from
different regions may have different functional roles: CA3 SWRs seem to
be important for spatial memory, whereas consolidation of social memory
requires SWRs arising in CA2 and object remapping dorsal CA1 and CA3
(Oliva et al., 2020).

Various changes in SWRs have been reported in different pathologies such
as epilepsy (Mooij et al., 2022), models of Alzheimer's disease (Jones
et al., 2019; Stoiljkovic et al., 2019; Prince et al., 2021; Caccavano
et al., 2020), and aging (Cowen et al., 2020; Witton et al., 2014).
However, little evidence has been reported on the effect of alcohol on
SWRs and memory consolidation. According to a study by Krawczyk et al.
(2016), increased duration and amplitude were observed in SPW waveforms
when evaluating the effect of prenatal ethanol exposure on recordings
from CA3 hippocampal pyramidal cells \emph{in vitro} . A previous
experiment was done by Mikaye et al. to analyze the effect of acute
ethanol administration on the spike patterns of hippocampal cell
populations. Their results suggested that ethanol does not significantly
alter the frequency of hippocampal SWRs (Miyake et al., 2020). While
these studies provide valuable insights into the effects of ethanol
exposure on SPW waveforms, further investigations are warranted to fully
elucidate the complex relationship between chronic alcohol use and SWRs.
Advancing our knowledge in this area can have important implications for
addressing alcohol-related cognitive impairments and developing targeted
interventions.

Understanding the role of hippocampal ripples in memory consolidation is
crucial for elucidating the mechanisms underlying alcohol-induced memory
impairments. However, as have been revised, analyzing ripple activity
can be challenging due to the complex and dynamic nature of these
events, as well as the variability in the methods used to detect and
quantify them.

To address these challenges, researchers have developed a range of
analytical models and techniques aimed at improving the accuracy,
reliability, and reproducibility of ripple analyses. The use of these
analysis models is essential for understanding the functional
significance of hippocampal ripples and their relationship with memory
consolidation (Girardeau, 2021, Creery, 2022). As can be seen in the
next section of this paper, by providing more accurate and reliable
measures of ripple activity, these techniques can help to elucidate the
neural mechanisms underlying memory processes and the effects of various
factors, such as alcohol consumption, on these processes.

Moreover, these techniques can facilitate comparisons between different
experimental conditions and between different studies, thereby enhancing
the generalizability and reproducibility of findings across different
research contexts.

\emph{4. Principal methodologies to study and analyze ripples as a
biomarker of memory consolidation}

Studies of oscillations with frequencies above traditional
electroencephalogram limits (greater than 40 Hz), have increased over
the last decade due to increasing evidence that suggests that HFOs
reflect a mechanism of epileptic phenomena and might serve as a
biomarker of epileptogenesis and epileptogenicity. This has resulted in
a growing interest in the detection and analysis of these events (Birot
et al., 2013; Navarrete et al., 2016). The bandwidth of SWRs (120-250
Hz) (Buzsáki, 2015) falls between the one of HFOs (80-500 Hz) (Burnos et
al., 2014), therefore, the methodologies for automatic identification of
HFOs might be adapted to identify SWRs. In recent decades, there has
been a substantial surge in the field of artificial intelligence, which
has been extensively explored for enhancing the classification of HFOs.
Although our subsequent discussion primarily focuses on conventional
methodologies that do not utilize machine or deep learning, it is worth
noting that readers may find the works of Wong et al., 2021 and
Navas-Olive et al., 2020 intriguing in the context of HFO
classification.

In general, HFO detection methods are classified in three main groups:
manual review, supervised detection, and unsupervised detection. Manual
review is a visual inspection performed by an expert. It is a
time-consuming process and highly subjective to the perception of the
reviewer; however, it is currently considered the gold standard when
assessing the performance of automated algorithms for HFO detection.
Supervised detection consists of methods with high sensitivity and low
specificity detection, which is later reviewed manually to eliminate
false positive events. On the other hand, unsupervised detection methods
must have high sensitivity and high specificity, which is difficult to
achieve (Birot et al., 2013; Navarrete et al., 2016).

In the last two decades, several methods for the automatic detection of
HFOs have been developed. Broadly speaking, the supervised and
unsupervised detection algorithms perform a series of common steps to
identify the putative events. The first step is to emphasize the
frequency of interest by filtering the raw signal. Then, a threshold
handling detection method is implemented. This can be based on the
energy, statistical or spectral characteristics of the filtered data.
Finally, depending on the method, a supervised or unsupervised mechanism
is implemented to distinguish the HFO from noise, artifacts, and spikes
(Navarrete et al., 2016).

Staba et al. (2002) reported the first supervised method for the
automatic detection of HFOs, which uses the root mean square (RMS) to
calculate the threshold after applying a band-pass filter. The authors
declared a sensibility of 84\%. Later, other authors used the algorithm
proposed by Staba et al. as the initial steps for theirs. For example,
Burnos et al. (2014) decreased the number of standard deviations (SDs)
established to classify the oscillation as an event of interest (EoI).
Then, they utilized the instantaneous power spectra of the Fourier
Transform representation to eliminate some false positive events. This
methodology had a higher sensibility in 4 out of 5 accepted patients
when compared to the methodology proposed by Staba et al (Burnos et al.,
2014). Other authors that used the algorithm of Staba et al. as the
initial steps of theirs include Crèpon et al. (2010), Ellenrieder et al.
(2012), and Blanco et al. (2010).

Charupanit and Lopour (2017) reported a simple statistical detection
method. The iterative algorithm uses an estimate of the amplitude
probability distribution of the background activity to calculate the
optimum threshold for identification of HFO. It described a sensitivity
of 99.6\%. Additionally, Shimamoto et al. (2018) computed an algorithm
that uses independent component analysis to detect ripples. The events
detected were further classified as true or false ripples by
implementing a topographical analysis to the time-frequency plots. They
declared a precision of over 91\% and a sensibility of over 79\%.

Another supervised algorithm to detect HFOs was implemented by Birot et
al. (2013) . This algorithm uses the RMS amplitudes and a Fourier or
Wavelet energy ratio to detect putative events. They implemented this
algorithm in both human and animal datasets and had an area under the
curve greater than 0.95. Another author that used wavelet entropy was
Zelmann et al. (2010) with a reported sensitivity and specificity of
96\%. Finally, Liu et al. (2021) computed in 2021 an algorithm that
combines several reported features like Short-time Energy and Hilbert
Transform with visual extracted features from the frequency spectrum.
They also evaluated this algorithm with datasets from both patients and
animal models and reported a sensibility greater than 91\%.

However, despite the increasing methodologies for effective automatic
identification of HFOs, there is currently no gold standard other than
visual examination. Therefore, we suggest that a global consensus of the
identification of true ripple activity and its analysis must be reached
to evaluate more accurately and compare properly under different
experimental conditions. It has been proposed as a solution to implement
different algorithms across similar datasets to reach a consensus about
the one method that performs best in all contexts (Navarrete et al.,
2016).

\emph{5. Perspectives}

Despite the extensively reported negative impacts of alcohol on health,
alcohol consumption persists at high rates worldwide. Alcohol abuse
generates significant impairments in cognition, which have been
demonstrated by rodent models. Morphological alternations of the
hippocampus after exposure to ethanol may explain physiological
impairments. To enhance memory consolidation, SWRs help transport
compressed hippocampus representation to dispersed circuits. Selective
disruption of SWRs interferes with memory. To the author's knowledge,
there is no evidence of research where Sharp Wave Ripples are evaluated
in association with memory impairment produced by chronic alcohol
administration. The study of the effect of alcohol on SWRs as a memory
consolidation biomarker may provide an opportunity for new forms of
management to restore and improve cognitive impairment in alcoholism.
This could be done by utilizing alcoholism animal models, such as rats
exposed to chronic levels of alcohol, and recording their SWRs during
``off-states''. The detection and analysis of SWRs could be assisted by
automatic detection algorithms that have been previously reported for
HFOs. Out of all the algorithms reported here, the parallel
implementation of several methodologies can potentially resolve false
negative identification of events. Additionally, implementation of
several methodologies on similar datasets is necessary to pinpoint a
gold standard that performs best in all contexts with minimal
adjustments.

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\textbf{Figure captions}

\textbf{Figure 1.} Subdivisions of the human memory and its associated
brain regions. Reproduced and modified from (Kandel et al., 2013;
Sweatt, 2003).

\textbf{Figure 2.} The anatomical features of the
hippocampus.\textbf{(a)} and \textbf{(b)} illustrate the hippocampal
formation of human and rodent brain, respectively, in relation to three
structures: 1) the fornix, which represents one of its output pathways;
2) the entorhinal cortex, which serves as both an input and an output
pathway; and 3) the mammillary body, a target that it projects to. The
amygdala is located in the anterior part of the temporal lobe. The
images are adapted and modified from Martin (2021) and Sokolowski and
Corbin (2012).

\textbf{Figure 3.} Pathways in the hippocampus. Information arrives to
the hippocampus from the entorhinal cortex through the perforant
pathways and travels to the CA1 by both a direct (blue) and an indirect
(red), also called Trisynaptic circuit of the hippocampus. Arrows denote
the direction of impulse flow. Reproduced and modified from (Kandel et
al., 2013).

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