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Precocious Alterations of Brain Oscillatory Activity in Alzheimer’s Disease: A Window of Opportunity for Early Diagnosis and Treatment

Identifieur interne : 003E81 ( Ncbi/Merge ); précédent : 003E80; suivant : 003E82

Precocious Alterations of Brain Oscillatory Activity in Alzheimer’s Disease: A Window of Opportunity for Early Diagnosis and Treatment

Auteurs : Valentine Hamm [France] ; Céline Héraud [France] ; Jean-Christophe Cassel [France] ; Chantal Mathis [France] ; Romain Goutagny [France]

Source :

RBID : PMC:4685112

Abstract

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia accounting for 50–80% of all age-related dementia. This pathology is characterized by the progressive and irreversible alteration of cognitive functions, such as memory, leading inexorably to the loss of autonomy for patients with AD. The pathology is linked with aging and occurs most commonly around 65 years old. Its prevalence (5% over 65 years of age and 20% after 80 years) constitutes an economic and social burden for AD patients and their family. At the present, there is still no cure for AD, actual treatments being moderately effective only in early stages of the pathology. A lot of efforts have been deployed with the aim of defining new AD biomarkers. Successful early detection of mild cognitive impairment (MCI) linked to AD requires the identification of biomarkers capable of distinguishing individuals with early stages of AD from other pathologies impacting cognition such as depression. In this article, we will review recent evidence suggesting that electroencephalographic (EEG) recordings, coupled with behavioral assessments, could be a useful approach and easily implementable for a precocious detection of AD.


Url:
DOI: 10.3389/fncel.2015.00491
PubMed: 26733816
PubMed Central: 4685112

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PMC:4685112

Le document en format XML

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<p>Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia accounting for 50–80% of all age-related dementia. This pathology is characterized by the progressive and irreversible alteration of cognitive functions, such as memory, leading inexorably to the loss of autonomy for patients with AD. The pathology is linked with aging and occurs most commonly around 65 years old. Its prevalence (5% over 65 years of age and 20% after 80 years) constitutes an economic and social burden for AD patients and their family. At the present, there is still no cure for AD, actual treatments being moderately effective only in early stages of the pathology. A lot of efforts have been deployed with the aim of defining new AD biomarkers. Successful early detection of mild cognitive impairment (MCI) linked to AD requires the identification of biomarkers capable of distinguishing individuals with early stages of AD from other pathologies impacting cognition such as depression. In this article, we will review recent evidence suggesting that electroencephalographic (EEG) recordings, coupled with behavioral assessments, could be a useful approach and easily implementable for a precocious detection of AD.</p>
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<pmc article-type="review-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Front Cell Neurosci</journal-id>
<journal-id journal-id-type="iso-abbrev">Front Cell Neurosci</journal-id>
<journal-id journal-id-type="publisher-id">Front. Cell. Neurosci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Cellular Neuroscience</journal-title>
</journal-title-group>
<issn pub-type="epub">1662-5102</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">26733816</article-id>
<article-id pub-id-type="pmc">4685112</article-id>
<article-id pub-id-type="doi">10.3389/fncel.2015.00491</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neuroscience</subject>
<subj-group>
<subject>Mini Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Precocious Alterations of Brain Oscillatory Activity in Alzheimer’s Disease: A Window of Opportunity for Early Diagnosis and Treatment</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Hamm</surname>
<given-names>Valentine</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/278898/overview"></uri>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Héraud</surname>
<given-names>Céline</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/301668/overview"></uri>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cassel</surname>
<given-names>Jean-Christophe</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/241958/overview"></uri>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Mathis</surname>
<given-names>Chantal</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/218563/overview"></uri>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Goutagny</surname>
<given-names>Romain</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:type="simple" xlink:href="http://loop.frontiersin.org/people/26868/overview"></uri>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Centre National de la Recherche Scientifique UMR 7364, Laboratoire de Neurosciences Cognitives et Adaptatives</institution>
<country>Strasbourg, France</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Université de Strasbourg, Neuropôle de Strasbourg</institution>
<country>Strasbourg, France</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by:
<italic>Lydia Jimenez-Diaz, University of Castilla La Mancha, Spain</italic>
</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by:
<italic>Andre Fisahn, Karolinska Institutet, Sweden; Valentina Kitchigina, Institute of Theoretical and Experimental Biophysics Russian Academy of Sciences, Russia</italic>
</p>
</fn>
<corresp id="fn001">*Correspondence:
<italic>Valentine Hamm,
<email xlink:type="simple">valentinehamm@hotmail.com</email>
; Romain Goutagny,
<email xlink:type="simple">goutagny@unistra.fr</email>
</italic>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>12</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection">
<year>2015</year>
</pub-date>
<volume>9</volume>
<elocation-id>491</elocation-id>
<history>
<date date-type="received">
<day>30</day>
<month>9</month>
<year>2015</year>
</date>
<date date-type="accepted">
<day>04</day>
<month>12</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2015 Hamm, Héraud, Cassel, Mathis and Goutagny.</copyright-statement>
<copyright-year>2015</copyright-year>
<copyright-holder>Hamm, Héraud, Cassel, Mathis and Goutagny</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia accounting for 50–80% of all age-related dementia. This pathology is characterized by the progressive and irreversible alteration of cognitive functions, such as memory, leading inexorably to the loss of autonomy for patients with AD. The pathology is linked with aging and occurs most commonly around 65 years old. Its prevalence (5% over 65 years of age and 20% after 80 years) constitutes an economic and social burden for AD patients and their family. At the present, there is still no cure for AD, actual treatments being moderately effective only in early stages of the pathology. A lot of efforts have been deployed with the aim of defining new AD biomarkers. Successful early detection of mild cognitive impairment (MCI) linked to AD requires the identification of biomarkers capable of distinguishing individuals with early stages of AD from other pathologies impacting cognition such as depression. In this article, we will review recent evidence suggesting that electroencephalographic (EEG) recordings, coupled with behavioral assessments, could be a useful approach and easily implementable for a precocious detection of AD.</p>
</abstract>
<kwd-group>
<kwd>Alzheimer’s disease</kwd>
<kwd>mild cognitive impairment</kwd>
<kwd>recognition memory</kwd>
<kwd>spatial navigation memory</kwd>
<kwd>oscillatory activity</kwd>
<kwd>electroencephalography</kwd>
</kwd-group>
<counts>
<fig-count count="0"></fig-count>
<table-count count="0"></table-count>
<equation-count count="0"></equation-count>
<ref-count count="83"></ref-count>
<page-count count="6"></page-count>
<word-count count="0"></word-count>
</counts>
</article-meta>
</front>
<body>
<sec>
<title>Introduction</title>
<p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that starts with mild short-term memory deficits before progressively culminating in total loss of cognitive and executive functions. Currently, the precise etiology of the pathology is not known and there is no cure. Genetic studies (
<xref rid="B57" ref-type="bibr">Price et al., 1998</xref>
;
<xref rid="B72" ref-type="bibr">Van Cauwenberghe et al., 2015</xref>
) have identified mutations in the genes of the transmembrane amyloid peptide precursor protein (APP) and those of presenilins 1 and 2 (PS1, PS2) responsible for rare dominantly inherited early onset familial AD (FAD). Proteolytic processing of APP first by the β-site APP cleaving enzyme, followed by the PS-containing γ-secretase complex, generates amyloid-β (Aβ) peptides that deposit in amyloid plaques. Many studies showed an increased production of more amyloidogenic Aβ peptides associated with FAD-linked mutations, providing strong support for the amyloid hypothesis (
<xref rid="B27" ref-type="bibr">Hardy and Selkoe, 2002</xref>
). According to this conceptual framework, it would be the early accumulation of soluble Aβ in specific brain areas that elicits abnormal patterns of neuronal activity leading to cognitive decline (
<xref rid="B53" ref-type="bibr">Palop and Mucke, 2010</xref>
). Therefore, a lot of efforts have been deployed in order to lower Aβ levels as a possible therapy for AD. Various types of treatments have been tested including the use of γ-secretase inhibitors and immunization against Aβ. Unfortunately, these drugs were less successful than expected, inducing no improvement, or even a worsening of cognitive functions, often accompanied by drastic side-effects that preclude their use as suitable therapeutics for AD (
<xref rid="B49" ref-type="bibr">Mikulca et al., 2014</xref>
). Diverse hypothesis were put forward to explain the disappointing results obtained by current anti-Aβ treatments. First, other APP fragments, like the β-carboxy terminal fragment (β-CTF;
<xref rid="B45" ref-type="bibr">Lahiri et al., 2002</xref>
;
<xref rid="B56" ref-type="bibr">Pimplikar et al., 2010</xref>
;
<xref rid="B46" ref-type="bibr">Lauritzen et al., 2012</xref>
;
<xref rid="B66" ref-type="bibr">Tamayev et al., 2012</xref>
;
<xref rid="B22" ref-type="bibr">Goutagny et al., 2013</xref>
), the amyloid intracellular domain (AICD), or the recently described CTF-η (
<xref rid="B77" ref-type="bibr">Willem et al., 2015</xref>
), might play key roles in AD pathogenesis and associated cognitive symptoms. A second hypothesis highlights the fact that treatments were given too late in the time-course of AD, when neuronal damages are already too extensive and irreversible. However, these two points are closely linked. We need to develop new early biomarkers, independent of the amyloid hypothesis, that do not entirely rely on Aβ dosage. In this article, we will review recent evidences indicating that the characterization of oscillatory activity in patients using electroencephalographic (EEG) recordings, a cost-efficient and easily implementable strategy, might represent a new opportunity for the early detection of AD. In addition, we propose that characterization of cross-frequency coupling (CFC), a specific motif of oscillatory interactions, might represent an extremely sensitive early biomarker.</p>
</sec>
<sec>
<title>Brain Oscillatory Activity</title>
<p>Cognitive processes (i.e., information processing and storage by brain networks) require a highly coordinated operation of multiple neuronal groups. One likely mechanism is through the coordinated rhythmic activity of neuronal populations, which give rise to oscillations (
<xref rid="B78" ref-type="bibr">Womelsdorf et al., 2007</xref>
). These oscillations can be recorded using various techniques, such as electrocorticography, local field potential, magnetoencephalography, or EEG. Rhythmic fluctuations of electric potentials measured by these technics are generated by the spatial summation of highly synchronized post-synaptic potentials occurring in large clusters of neurons. They have an excellent temporal resolution (in the millisecond timescale) and now, new analytic methods allow locating generators of these oscillations with a decent spatial resolution even using EEG (around one voxel with the LORETA method in human EEG;
<xref rid="B20" ref-type="bibr">Gianotti et al., 2007</xref>
).</p>
<p>The spectral content of EEG is classically divided in five frequency bands: δ (from 1 to 4 Hz), θ (4 to 7 Hz), α (8 to 12 Hz), β (15 to 30 Hz), and γ (>30 Hz). On a functional level, these diverse oscillations are associated with different brain states. Oscillatory activities are related to global states (i.e., δ waves are mainly present during sleep) or specific behaviors (i.e., β rhythm is usually associated to motor tasks and is thought to reflect the activity of motor cortices;
<xref rid="B55" ref-type="bibr">Pfurtscheller et al., 1998</xref>
). Some frequency domains are more closely related to cognitive processes. First, θ oscillations are thought to play a key role in working memory processes (
<xref rid="B59" ref-type="bibr">Sauseng et al., 2010</xref>
). θ phase affects memory processing through the modulation of neuronal plasticity within hippocampal and cortical areas and plays a modulatory role in the induction of long-term-potentiation (LTP), a long lasting enhancement of synaptic efficacy which may constitute one of the cellular substrates for learning and memory. In addition, θ rhythm is also strongly linked to hippocampal pyramidal cells that code for spatiotemporal aspects of the animal’s environment (
<xref rid="B33" ref-type="bibr">Huxter et al., 2003</xref>
). Second, α oscillations are more related to attentional processes by filtering out irrelevant informations and preventing interference from conflicting stimuli (
<xref rid="B40" ref-type="bibr">Klimesch, 2012</xref>
). Finally, γ oscillations are modulated by a variety of cognitive processes such as object recognition and working memory (
<xref rid="B67" ref-type="bibr">Tiitinen et al., 1993</xref>
;
<xref rid="B82" ref-type="bibr">Yordanova et al., 1997a</xref>
,
<xref rid="B83" ref-type="bibr">b</xref>
;
<xref rid="B32" ref-type="bibr">Herrmann and Mecklinger, 2000</xref>
;
<xref rid="B17" ref-type="bibr">Fries et al., 2001</xref>
;
<xref rid="B11" ref-type="bibr">Debener et al., 2003</xref>
;
<xref rid="B30" ref-type="bibr">Herrmann et al., 2004a</xref>
,
<xref rid="B31" ref-type="bibr">b</xref>
) and are thought to temporally link distributed cell assemblies from different sources that are processing related informations.</p>
<p>Slow and fast oscillatory activities are not independent. Indeed, slow and fast rhythms interact with each other, the phase of slow oscillations (mainly θ rhythm) being able to modulate the amplitude of fast oscillations (β and γ rhythms). This phenomenon, known as CFC, is positively associated with cognitive processes in humans (
<xref rid="B7" ref-type="bibr">Canolty et al., 2006</xref>
;
<xref rid="B26" ref-type="bibr">Händel and Haarmeier, 2009</xref>
;
<xref rid="B1" ref-type="bibr">Axmacher et al., 2010</xref>
), monkeys (
<xref rid="B8" ref-type="bibr">Canolty et al., 2010</xref>
), rats (
<xref rid="B70" ref-type="bibr">Tort et al., 2008</xref>
,
<xref rid="B69" ref-type="bibr">2009</xref>
) and mice (
<xref rid="B79" ref-type="bibr">Wulff et al., 2009</xref>
). More specifically, it is hypothesized that at fast frequencies, CFC would allow distributed brain regions to be synchronized (using the slow one as a “carrier”), which consequently facilitates communication.</p>
<p>Given the key role played by oscillatory activities on cognitive processes such as memory, numerous studies have closely looked at brain oscillatory alterations in AD patients, as well as in animal models of the pathology. In the next parts of this article, we will review recent findings on oscillatory activity alterations in the time course of AD.</p>
</sec>
<sec>
<title>Oscillatory Activity in Mild Cognitive Impairment (MCI) and AD Patients</title>
<p>More than 980 articles in the last 40 years have looked at EEG activity in mild cognitive impairment (MCI) and/or AD patients. Indeed, with the development of new analytic methods that can account for different confounding results (for example, volume conduction), EEG activity seems sensitive enough for an early detection of preclinical AD and predictive of future conversion from MCI to AD.</p>
<p>The majority of studies focusing on EEG characterization in AD patients have been done using resting state paradigms. Resting state EEG corresponds to recordings performed in the motionless subject with eyes closed. This task is a fully standardized procedure and can therefore be done in highly comparable experimental conditions. Compared to age-matched healthy control subjects, both MCI and AD patients exhibit an increase in relative power of slow oscillations (δ and θ rhythms) associated with a decrease in relative power of fast oscillations (α, β, and γ rhythms;
<xref rid="B73" ref-type="bibr">van der Hiele et al., 2007</xref>
;
<xref rid="B10" ref-type="bibr">Czigler et al., 2008</xref>
;
<xref rid="B52" ref-type="bibr">Moretti et al., 2010</xref>
). The relative amplitude of θ oscillations has been proposed as a marker for AD as it allows the correct classification of 85% of MCI subjects, distinguishing the ones who progress to clinically manifested AD from those who remain stable (
<xref rid="B37" ref-type="bibr">Jelic et al., 2000</xref>
). Furthermore, increased θ power is already present in subjects with subjective complaints 7 years before decline to the MCI state (
<xref rid="B58" ref-type="bibr">Prichep et al., 2006</xref>
).</p>
<p>However, differences in resting state EEG might not be specific to AD. Indeed, multiple types of dementia could also be characterized by similar global network alterations. As an example, an increase in relative θ power is also found in dementia with Lewy bodies (
<xref rid="B38" ref-type="bibr">Kai et al., 2005</xref>
) and a decrease in relative γ power is also found in normal aging, after a brain injury or a stroke (
<xref rid="B29" ref-type="bibr">Herrmann and Demiralp, 2005</xref>
).</p>
<p>Therefore, and to achieve higher specificity, it could be suitable to combine behavioral paradigms in real time with electrophysiological recordings. During behavioral tasks, memory-related activation reveals specific EEG functional differences between MCI patients and control ones that facilitates the early diagnostic of probable AD. As an example, haptic tasks are sensitive to early perceptive-cognitive and functional deficits in MCI patients. Indeed, during tactile tasks, θ-power over right occipital regions is a suitable marker to distinguish healthy subjects from MCI patients (
<xref rid="B25" ref-type="bibr">Grunwald et al., 2002</xref>
). In addition, in a face-name encoding mnemonic task, the recording of EEG alterations is associated with the Mini-Mental State Examination and may serve as a clinically valuable marker for disease severity (
<xref rid="B18" ref-type="bibr">Garn et al., 2014</xref>
).</p>
<p>However, multiple forms of memory are affected in AD (
<xref rid="B13" ref-type="bibr">Didic et al., 2011</xref>
) and episodic memory impairment is not specific to AD but is also found in other types of dementia and psychiatric disorders. It was proposed that navigation deficits could help to distinguish patients at higher risk of developing AD from individuals with normal cognitive aging and those with other neurodegenerative diseases (
<xref rid="B47" ref-type="bibr">Lithfous et al., 2013</xref>
). Indeed, spatial disorientation is already present at MCI and early AD stages. Specific spatial tasks in both virtual or real world paradigms may possibly predict the conversion from normal aging to MCI and from MCI to dementia (
<xref rid="B39" ref-type="bibr">Kalová et al., 2005</xref>
;
<xref rid="B44" ref-type="bibr">Laczó et al., 2011</xref>
;
<xref rid="B76" ref-type="bibr">Weniger et al., 2011</xref>
;
<xref rid="B51" ref-type="bibr">Moodley et al., 2015</xref>
). Spatial navigation depends on θ (
<xref rid="B9" ref-type="bibr">Cornwell et al., 2008</xref>
;
<xref rid="B36" ref-type="bibr">Jacobs et al., 2010</xref>
;
<xref rid="B62" ref-type="bibr">Snider et al., 2013</xref>
) and γ (
<xref rid="B54" ref-type="bibr">Park et al., 2014</xref>
) oscillations. Therefore, behavioral assessments of spatial memory processes combined with EEG techniques might represent a promising strategy for an early detection of preclinical AD with a high specificity.</p>
</sec>
<sec>
<title>Oscillatory Activity in Animal Models of AD</title>
<p>In spite of the considerable restriction that few spontaneous animal models recapitulate the entire spectrum of the sporadic form of AD (
<xref rid="B65" ref-type="bibr">Strittmatter et al., 1993</xref>
;
<xref rid="B19" ref-type="bibr">Giannakopoulos et al., 1997</xref>
;
<xref rid="B34" ref-type="bibr">Inestrosa et al., 2005</xref>
;
<xref rid="B3" ref-type="bibr">Bons et al., 2006</xref>
;
<xref rid="B68" ref-type="bibr">Toledano et al., 2014</xref>
;
<xref rid="B63" ref-type="bibr">Stefanova et al., 2014</xref>
,
<xref rid="B64" ref-type="bibr">2015</xref>
), parallel research on animals has provided an essential contribution in understanding the mechanisms underlying abnormal oscillatory patterns in AD.</p>
<p>Hippocampal slices preparations from rodents and transgenic mice models of AD constitutes a useful tool for investigating mammalian synaptic alterations during amyloid pathology (
<xref rid="B48" ref-type="bibr">Mathis et al., 2011</xref>
;
<xref rid="B28" ref-type="bibr">Hazra et al., 2013</xref>
). However, spontaneous oscillations are not present in hippocampal slices. Indeed, hippocampal oscillatory activity is the product of multiple intra- and extra-hippocampal oscillators (the hippocampus and the medial septum for θ oscillations;
<xref rid="B4" ref-type="bibr">Borisyuk et al., 1999</xref>
;
<xref rid="B12" ref-type="bibr">Denham and Borisyuk, 2000</xref>
;
<xref rid="B75" ref-type="bibr">Wang, 2002</xref>
;
<xref rid="B23" ref-type="bibr">Goutagny et al., 2009</xref>
) and an intra-hippocampal excitation/inhibition loop, together with inputs from the entorhinal cortex for γ rhythms (
<xref rid="B6" ref-type="bibr">Bragin et al., 1995</xref>
). The study of hippocampal oscillations in slices requires the application of a cholinergic or glutamatergic (kainate receptor) agonist to increase cellular excitability. With such a type of approach, it was shown that application of Aβ
<sub>1-42</sub>
reduced the power of kainate-induced γ oscillations in mice (
<xref rid="B43" ref-type="bibr">Kurudenkandy et al., 2014</xref>
). However, the utility of these models must be considered in light of how well they mimic the actual phenomenon. A bath application of carbachol to hippocampal slices can generate either θ or γ rhythms depending on experimental parameters such as: slice orientation, thickness, drug concentration, and temperature (
<xref rid="B41" ref-type="bibr">Konopacki et al., 1987</xref>
;
<xref rid="B16" ref-type="bibr">Fisahn et al., 1998</xref>
;
<xref rid="B15" ref-type="bibr">Fellous and Sejnowski, 2000</xref>
). However, there is no evidence that in freely moving animals θ and γ rhythms require cholinergic neurotransmission. Another popular model of hippocampal γ rhythms uses kainite receptor activation (
<xref rid="B71" ref-type="bibr">Traub et al., 2005</xref>
). Once again, although robust γ can be observed in hippocampal preparations, it does not appear as though
<italic>in vivo</italic>
γ rhythms are mediated by kainate receptors. Only one report has generated a model of simultaneous θ and γ rhythms in the presence of kainic acid (
<xref rid="B21" ref-type="bibr">Gloveli et al., 2005</xref>
) which required a unique hippocampal slice containing transverse and longitudinal circuitries. The recent development of a new
<italic>in vitro</italic>
preparation, which respects the complex three-dimensional organization of intrinsic hippocampal circuits, has circumvented most of the issues aforementioned. Using this preparation, it is possible to characterize spontaneously occurring θ (
<xref rid="B23" ref-type="bibr">Goutagny et al., 2009</xref>
) and γ (
<xref rid="B35" ref-type="bibr">Jackson et al., 2011</xref>
) oscillations. In a transgenic mouse model of AD, the TgCRND8 mice, hippocampal θ-γ uncoupling was shown to precede soluble Aβ and plaque accumulation (
<xref rid="B22" ref-type="bibr">Goutagny et al., 2013</xref>
).</p>
<p>Oscillatory activities can also be measured in anesthetized animals (
<xref rid="B80" ref-type="bibr">Xu et al., 2015</xref>
). In this paradigm, θ oscillations can be recorded under urethane anesthesia either spontaneously or after sensory stimulation (tail or paw pinches) or electric stimulation of the brainstem
<italic>nucleus pontis oralis</italic>
(
<xref rid="B2" ref-type="bibr">Bland and Whishaw, 1976</xref>
). With this approach, it was shown that APP/PS1 transgenic mice showed an age-dependent decrease in hippocampal θ activity correlating with plaque load (
<xref rid="B61" ref-type="bibr">Scott et al., 2012</xref>
). However, under urethane anesthesia, θ oscillations are exclusively of type II (atropine-sensitive) and no type I θ (atropine-resistant) is present (
<xref rid="B42" ref-type="bibr">Kramis et al., 1975</xref>
). Therefore, in order to fully capture possible alterations in hippocampal oscillatory activity in animal models of AD, recordings in freely moving animals are required.</p>
<p>Many cognitive paradigms used in MCI and early AD diagnoses are based on verbal episodic memory tasks that present a translational problem for animal studies. Indeed, episodic memory, which is characterized by conscious recollection of context-rich events, is rather difficult to probe in animals. Several episodic-memory-like paradigms are currently being developed in rodents and apes but their extrapolation and dependence on a similar set of temporal lobe structures than human episodic memory still need to be confirmed. Fortunately, nature knows best, the hippocampus and the parahippocampal formation, responsible for episodic memory in humans, seem to have anatomical and functional homologs across mammal species. As an example, these brain regions are implicated in the encoding and retrieval of information related to environment during spatial navigation in rodents (
<xref rid="B50" ref-type="bibr">Molter et al., 2012</xref>
). In a recent study, rhesus monkeys learned how to freely drive a wheelchair to navigate through a complex maze, providing a strong support for an electrophysiological investigation of spatial navigation in the real world (
<xref rid="B14" ref-type="bibr">Etienne et al., 2014</xref>
). In rodents as in humans, spatial representations are related to modulation of θ oscillations as well as θ-γ coupling (
<xref rid="B33" ref-type="bibr">Huxter et al., 2003</xref>
;
<xref rid="B5" ref-type="bibr">Bott et al., 2015</xref>
). Interestingly, in a transgenic mouse model of AD, the Tg5xFAD mice, a decrease of θ and γ frequencies precedes disturbances in learning performances in a navigation task (
<xref rid="B60" ref-type="bibr">Schneider et al., 2014</xref>
).</p>
</sec>
<sec>
<title>Conclusion</title>
<p>To conclude, with the support of the previously described results obtained both on animal models and patients, we propose that CFC alterations might constitute a promising early biomarker of AD. Indeed, modifications in hippocampal θ-γ coupling during spatial navigation might occur in the very first stages of AD and serve as a possible predictor for the pathology (
<xref rid="B24" ref-type="bibr">Goutagny and Krantic, 2013</xref>
). Future research aimed at identifying biomarkers based on combined EEG and behavioral testing approaches should integrate the fact that spatial navigation memory tasks used to diagnose AD in patients can be transposed to animals. In this way, animal studies leave the door open on diagnostic and therapeutic pathways that could be transposable in patients. Moreover, independently of progress made on earlier disease targets, it may be assumed that patients diagnosed at the most precocious stage of the pathology still have enough brain plasticity resources to sustain effective responses to therapeutic interventions, including environmental enrichment (
<xref rid="B74" ref-type="bibr">Verret et al., 2013</xref>
;
<xref rid="B81" ref-type="bibr">Yeung et al., 2015</xref>
), to stop the progression of AD or even reverse it.</p>
</sec>
<sec>
<title>Author Contributions</title>
<p>VH and RG wrote the review. CM provided critical inputs. CH and JC helped to correct the manuscript.</p>
</sec>
<sec>
<title>Conflict of Interest Statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</body>
<back>
<ack>
<p>This work was supported by the University of Strasbourg, the Centre National de la Recherche Scientifique (CNRS) and by grants from the Fondation Fyssen, the European Research Executive Agency, the NARSAD young investigator award and Neurex.</p>
</ack>
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