Changes in neural network connectivity in mice brain following exposures to palatable food

Journal Pre-proof Changes in neural network connectivity in mice brain following exposures to palatable food Nifareeda Samerphob, Dania Cheaha, Acharaporn Issuriya, Surapong Chatpun, Wanida Lertwittayanon, Ole Jensen, Ekkasit Kumarnsit

PII:

S0304-3940(19)30645-7

DOI:

https://doi.org/10.1016/j.neulet.2019.134542

Reference:

NSL 134542

To appear in:

Neuroscience Letters

Received Date:

12 July 2019

Revised Date:

23 September 2019

Accepted Date:

9 October 2019

Please cite this article as: Samerphob N, Cheaha D, Issuriya A, Chatpun S, Lertwittayanon W, Jensen O, Kumarnsit E, Changes in neural network connectivity in mice brain following exposures to palatable food, Neuroscience Letters (2019), doi: https://doi.org/10.1016/j.neulet.2019.134542

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Changes in neural network connectivity in mice brain following exposures to palatable food

Nifareeda Samerphob1,4, Dania Cheaha2,4, Acharaporn Issuriya1, Surapong Chatpun3,4,

1

ro of

Wanida Lertwittayanon1 , Ole Jensen5, Ekkasit Kumarnsit1,4*

Department of Physiology, Faculty of Science, Prince of Songkla University, Hat Yai,

2

-p

Songkhla, 90112, Thailand

Department of Biology, Faculty of Science, Prince of Songkla University, Hat Yai,

Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat

lP

3

re

Songkhla, 90112, Thailand

Yai, Songkhla, 90112, Thailand

Research Unit for EEG Biomarkers of Neuronal diseases, Faculty of Science, Prince of

na

4

5

ur

Songkla University, Hat Yai, Songkhla, 90112, Thailand Department of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT,

Jo

United Kingdom

*

Corresponding author: e-mail: [email protected]

1

Highlights Chocolate cues facilitated satiated chocolate-experienced mice to seek for chocolate LFP oscillations in the LHa and NAc were sensitive to chocolate cues Altered coherence activity among regions were correlated with hedonic hunger

ro of

Interrelations among the OB, NAc, LHa and HP were crucial for emotional drive to eat

Abstract

Previously, satiated animals or human subjects can still be motivated to eat by

-p

palatable food-associated cues. However, neural circuitries of hedonic hunger have not been well investigated. This study identified neural network connectivities between major brain

re

areas in response to chocolate-associated cues following repeated exposures to chocolate. Adult male Swiss albino ICR mice were anesthetized and implanted with intracranial

lP

electrodes in the lateral hypothalamus (LHa), nucleus accumbens (NAc), olfactory bulb (OB) and hippocampus (HP) for local field potential (LFP) recording. LFP oscillations were

na

recorded before and after repeated exposures to chocolate for chocolate experienced group whereas control group was not exposed to chocolate. On testing days, satiated animals were

ur

individually put into a place preference-like apparatus with two opposite chambers of

Jo

chocolate and normal chow scent cues, separately. The results showed that chocolate experienced group significantly increased time spent in chocolate chamber whereas control group did not. One-way ANOVA revealed significant influence of chocolate sessions on LFP spectral powers of multiple frequencies in the LHa (delta, low gamma and high gamma) and NAc (high gamma). Moreover, coherence function analyses also highlighted significant increases in LHa-NAc and LHa-OB, and decrease in LHa-HP coherent activities in response

2

to olfactory cues of chocolate. This study demonstrated modifications of neural network connectivity and associative learning following multiple exposures to palatable food. These findings might explain why energy homeostatic hunger is overridden by hedonic hunger.

ro of

Keywords: chocolate, hedonic hunger, local field potential, neural network, scent cue

Introduction

In general, appetite is driven by physiological energy deprivation [1]. It can be

-p

externally stimulated by exposure to palatable food even in the absence of homeostatic

hunger [2]. Feeding behavior is necessary not only for energy intake but also important to

re

obtain the pleasure. It was believed that hedonic hunger associated with binge eating and loss of control over eating [3]. Exposure to appetitive food cues is one of major potential causes

lP

leading to maladaptive eating behaviors [4]. Previously, people with high sensitivity to appetitive food cues are at risk to have overeating and poor diet choices [5,6]. Moreover,

na

reward sensitivity to palatable food was demonstrated to motivate seeking for rewarding substances and pursue situations and stimuli with high reward potential including impulse

ur

control deficit [7]. Therefore, it has been hypothesized that reward-sensitive individuals are

Jo

more attuned to foods with high rewarding properties of eating [8]. Visual detection to food triggers anticipatory responses associated with reward system that would determine the subsequent feeding response [9]. It means that, apart from physiologic need for energy, feeding behavior is also sensitive to various factors including eating experiences, reward and perception of food cues.

3

Several brain regions have been known to regulate feeding behavior. Basically, the lateral hypothalamic area (LHa), known as a hunger brain integration, plays a key role in activating food consumption. Glutamate agonist injection and electrical stimulation to the region can elicit feeding in satiated animals [10,11]. However, it is also essential to have neural circuitries for both energy homeostasis and food reward function [12,13]. Neural connections between the LHa and the integrator of hedonic feeding, nucleus accumbens (NAc) are believed to appreciate for the role play in modulating reward effects of feeding

ro of

behavior [14]. Food intake promoted by the dopaminergic and opioidergic activities was necessary for seeking behavior and hedonic pleasure, respectively [15]. Environmental

rewarding stimuli were found to activate the ventral tegmental area (VTA) resulting in

-p

dopamine released in the NAc [16]. Therefore, the exposure to palatable food associated cues

re

is possible to alter LFP oscillatory pattern in the LHa and NAc.

In general, the hippocampus (HP) plays an important role in learning and memory.

lP

Deficits in episodic memory were correlated with uncontrolled eating probably via disruption of recent eating memory [17]. In addition, memory inhibition might be important in gating the

na

incentive properties of food cues (see [18]). With the olfactory function, specific aromatic exposure to third trimester infants through their pregnant mother’s diet resulted in

ur

consumption of the diet which the scent was perceived previously [19]. Moreover, food pellet odors induced Fos expression in a cell layer of the olfactory bulb (OB) and locomotor activity

Jo

in both fasted and satiated rats whereas sniffing response was increased only in negative energy status [20]. In contrast, the olfactory bulbectomized rats changed the feeding pattern by decreasing meal size and increasing meal number, but remained the same amount of food consumption in total [21]. Interrupting some of olfacto-hypothalamic pathways was demonstrated to disturb meal size and time interval from the last meal [22]. It seems likely that these brain regions form essential circuits and generate neural signals to modulate food

4

intake. However, the underlying mechanisms of hedonic hunger remains to be investigated, especially the interrelation of key brain regions. It is known that hunger usually leads to changes in perception, emotion, learning and memory and feeding behavior probably through interconnections among brain regions. Therefore, the aim of this study is to characterize the neural network connectivity in response to palatable food-associated cues. Materials and Methods

ro of

Male Swiss albino ICR mice (7‑ 8 weeks old) provided by Southern Laboratory Animal Facility of Prince of Songkla University, (Songkhla, Thailand) were used for all

experiments in this study in accordance with guidelines of the European Science Foundation

-p

(Use of Animals in Research 2001) and International Committee on Laboratory Animal

Science, ICLAS (2004). The experimental protocols for care and use of experimental animals

re

described in the present study were approved and guided by the Animals Ethical Committee of Prince of Songkla University (MOE 0521.11/840). All efforts were made to minimize

lP

animal suffering and to reduce the number of animals used. They were individually housed in a standard cage (26 cm x 33 cm x 15 cm) with saw dust bedding, free access to food and

na

water and 12/12 h light-dark condition.

ur

Intracranial electrodes were implanted by following the procedures as previously described [23]. Animals were intramuscularly injected with a mixture of 150 mg/kg ketamine

Jo

(Calypsol, Gedeon Richter Ltd., Hungary) and 15 mg/kg xylazine (Xylavet, Thai Maji Pharmaceutical Co., Ltd., Thailand) for deep anesthetization. After animal’s head was fixed with a stereotaxic apparatus, approximately 15 min lidocaine (Locana, L.B.S. Laboratory Ltd., Part, Thailand) injection was applied under the dorsal scalp as a local anesthesia. Mice received a midline incision through the scalp to expose the dorsal skull. Silver wire electrodes were implanted unilaterally into the left lateral hypothaamus (AP: -1.5 mm, ML: 1 mm, DV:

5

5 mm), nucleus accumbens (AP: +0.8 mm, ML: 1 mm, DV: 4.8 mm), dorsal CA1 region of hippocampus (AP: -2.5 mm, ML: 1.5 mm, DV: 1.5 mm) and olfactory bulb (AP: +4.5 mm, ML: 1 mm, DV: 2 mm). A ground electrode was implanted into the midline of the cerebellum (AP: -6.5 mm, midline, DV: 2 mm). A stereotaxic coordinate atlas was used to define flat-skull positions [24]. Anchored screws were fixed in additional holes for the extra stability to keep all electrodes permanently in place. Afterwards, electrodes and screws were mounted on the skull by using dental cement. The antibiotic ampicillin (General Drug House

ro of

Co., Ltd., Thailand) was given intramuscularly once a day for 3 consecutive days to prevent infection. Then, animals were allowed to recover fully for at least 2 weeks.

The place preference paradigm for testing preclinical behavior of rewarding and

-p

aversive effects [25] was modified in this study. The apparatus consists of a three-

re

compartment chamber with the two compartments on the opposite ends being designed as spaces for chow scent vs. chocolate scent separately. The center compartment was in the

lP

middle and assigned as a neutral zone with the gates between compartments opened to allow an animal moving freely among compartments. Perforated cups containing normal food chow

na

and chocolate chunks were separately placed in the middle of two opposite compartments to allow animals to perceive specific scent in each zone. Time spent for exploring in each zone

ur

of the chamber was assessed to determine the preference. Control and chocolate-experienced mice were individually scheduled through the

Jo

experimental protocol. Before testing, animals in both groups were given a small piece of chocolate (2 g) once in their home cages to prevent neophobia when exposed to a novel stimulus during the test. Thereafter, animals were habituated to the apparatus 30 min per day for 3 consecutive days. Prior to recording of electrical brain signals and behavior, all mice were orally fed with fluid chow to ensure fullness. Previously, proper volume of feeding was recommended with 10–30 ml per kg body weight [26]. In this study, animals were fed with 10 6

ml/kg body weight to receive 20 g/kg dry weight of food powder. Normally, they consume 40 g/kg per day [27]. Forced feeding to animals was to ensure satiety and to confirm that behavioral results were driven by palatable food preference not by hunger. During the first observation, LFP signals and locomotor activity were recorded simultaneously while animal exploring the space in the chamber for 30 min. Olfactory scents of normal food chow and Hershey’s creamy milk chocolate randomly placed in 2 opposite chambers were given to animals as conditioned stimuli. After that, 9 chocolate experienced

ro of

group received chocolate (a piece of 2 g chocolate per each mouse in their home cage on day 1, 3, 5 and 7) while 7 control group did not receive any chocolate chunk in their home cage.

Finally, LFP signals and exploratory behavior of chocolate experienced and control animals

-p

were recorded for the second time on day 8 which animals were fed to induce fullness before

re

exposure to chocolate and normal food chow in the apparatus. All behaviors and LFP recording were conducted during the dark phase of the day. Data collected during a 20–30

lP

min period of each record were analyzed.

LFP signals from each brain regions were collected, amplified and digitized using a

na

PowerLab 16/35 system (AD Instruments, Australia) with 16-bit A/D at a sampling rate of 2 kHz and a bandwidth of 1–200 Hz. Data were stored in a computer using LabChart 7.3.7 Pro

ur

software. Notch filtering at 50 Hz was applied to remove the noise from power line artifacts. Power spectral density (PSD) was generated by LabChart software using a Hanning window

Jo

cosine (window size = 0.976 s, overlaps = 0.488 s). Fast Fourier transform (FFT) algorithm was used for frequency power analysis. Percent relative power was computed from the power for the particular frequency (P (f) ) according to the equation;

%Relative power=

(P (f) at second recording−P (f) at first recording) (P (f) at second recording+P (f) at first recording)

x100

7

Percent relative coherence was computed from the coherence for the particular frequency (C (f) ) according to the equation;

%Relative coherence=

(C (f) at second recording−C (f) at first recording) (C (f) at second recording+C (f) at first recording)

x100

Data before and after chocolate sessions (for chocolate experienced animals) and the first and second observations (for paralleled control animals) were computed to detect changes in LFP power and neural network connectivity induced by repeated exposures to

ro of

palatable food. In this study, frequency ranges were determined into slow delta (0.5–4 Hz), theta/low theta (4.5–8 Hz), alpha/high theta (8.5-12 Hz), low beta (12.5-18 Hz), high beta (18.5-30 Hz), low gamma (30.5–45 Hz) and high gamma (60–100 Hz) ranges.

-p

Locomotor movement and behaviors of animals were recorded by using a video

re

camera fixed over the apparatus. Speed of animal movement was analyzed by using a video tracking system with custom made software as described previously [28]. Briefly, the image

lP

of animal body was detected based on a contrast between white color of animal body and black color of the chamber background. The center of animal image was tracked to follow

na

the position and movement of animal. The translocation of animal was detected with sensitivity at 2-mm threshold and validated by using a scale bar. While exploring freely in the

ur

apparatus, the image of animal was continuously captured and transferred to the computer for locomotor activity analysis. One movement was defined with 1 period of continuous

Jo

translocation to 1 stop. Locomotor velocity was calculated from distance travelled divided by time used. Averaged speed and time spent in chocolate zone were analyzed to represent chocolate preference. All data were averaged and expressed as mean ± Standard Error of Mean (S.E.M.). Paired t-test was used to determine the difference between two sets of related data for

8

locomotor speed. One-way ANOVA was used to determine the influence of chocolate exposure on LFP power and coherence. Moreover, two-way repeated measure ANOVA was used to analyze the influence of treatment factors in the analyses of time spent during firstand second- observations within the groups. Tukey’s post hoc and Dunn’s method were used for multiple comparisons. P–value <0.05 was accepted to be statistically significant. Results

ro of

Local field potential patterns in 4 major brain regions following chocolate sessions Raw signals of LFP were recorded from the olfactory bulb (OB), nucleus accumbens (NAc), lateral hypothalamus (LHa) and hippocampus (HP). Representative LFP tracings from

-p

control (Fig. 1A-B) and chocolate experienced (Fig. 1C-D) groups during first- and secondrecordings were shown for general inspection. One-way ANOVA confirmed significant

re

influence of repeated exposures to chocolate sessions on LFP spectral power in the LHa

lP

[F(14,118)=17.603, p<0.001] and NAc [F(13,105)=76.031, p<0.001] but not the HP and OB (Fig. 2A-D). In the LHa, chocolate-experienced group had significant increase in power of delta

na

band and decreases in power of low gamma and high gamma bands. In the NAc, chocolatetexperienced group was found to have significant decreases in power of high gamma band compared to control levels. Altogether, repeated exposures to chocolate sessions were found

ur

to change LFP oscillations in the LHa and NAc.

Jo

Coherent activities between the pairs of brain regions following chocolate sessions No obvious change between LHa-HP, LHa-NAc and LHa-OB coherence values of

the first and second observations of control group was seen (upper case in Fig. 3A-C). On the other hand, increased coherence values of high frequency ranges were produced during postchocolate session of chocolate experienced group in both pairs of region. Statistical analysis

9

confirmed significant influence of chocolate exposures on LHa-HP [F(13,105)=49.239, p<0.001], LHa-NAc [F(13,105)=60.382, p<0.001] including LHa-OB [F(13,105)=61.115, p<0.001] in relative coherent activity (lower case in Fig. 3A-C). Multiple comparisons indicated significant decreases in coherence values of high gamma in LHa-HP, increases in high beta and low gamma frequency activities in LHa-NAc, and increases in coherence value of high beta frequency band in LHa-OB. Patterns of NAc-HP, NAc-OB and HP-OB coherent activities of both control and

ro of

chocolate experienced groups were not relatively changed between the first and second

observations (upper case in Fig. 4A-C). There was no significant difference between relative

-p

coherent activities of both groups (lower case in Fig. 4A-C).

Time spent in place preference-like apparatus and locomotor activity of chocolate

re

experienced animals

lP

Locomotor tracking during the pre- and post- chocolate sessions of chocolate experienced mice in the normal food- and chocolate- zones were expressed (Fig. 5A).

na

Chocolate repeated consumptions produced a significant influence in time spent among normal food and chocolate zones evaluated by the analysis of two-way repeated ANOVA [F (1, 35)

= 11.451, p = 0.010]. Tukey’s post hoc test indicated that chocolate sessions

ur

significantly increased time spent in chocolate zone in comparison to normal food zone. The

Jo

multiple comparisons also indicated time spent decrease in food zones and increase in chocolate zone following chocolate sessions. No significant difference of time spent between them was seen for control group (Fig.5B). The effect of chocolate sessions in chocolate experienced mice on movement speed was also evaluated. No significant change was observed between pre- and post- chocolate sessions (Fig.5C). Discussion

10

The present findings demonstrated food seeking behavior and neural signaling pattern induced by exposure to palatable food cues in satiated mice. These data support the concept that integrative brain mechanism of feeding control depends on both homeostatic and emotional inputs. Previously, conclusive reports showed that in some conditions, homeostatic signals are easily overridden by non-homeostatic signaling [29]. Through the evolution of animals, energy conservation might be priority in order to survive in environment niches. There are many factors that stimulate eating compared to factors that inhibit it. This was

ro of

crucial in the beginning of animal’s evolution when starvation was a way of life. However, excessive food is highly available nowadays. This is problematic for human as the brain still facilitates drive to eat for energy conservation leading to various metabolic syndromes.

-p

Beta (12-30 Hz) and gamma (30-80 Hz) range oscillations generated in many parts of

re

the brain are associated with attention, perception, and cognition [30–33]. In addition, beta rhythms were immediately followed by gamma rhythms in sensory evoked potential

lP

recordings [34]. Beta oscillation was essential for synchrony among cortical brain areas in functional bain integration, while gamma rhythms were prevalent in neural synchronization

na

of local networks [35,36]. Furthermore, increased coherence in higher frequency ranges (beta and gamma) between regions were correlated with increased ability to synchronize neurons,

ur

while decreased coherence was correlated with diminished synchrony of connection as found in autism and schizophrenia patients [37].

Jo

The present study showed that chocolate cues significantly changed local field

potential oscillations in the LHa and NAc in chocolate-experienced mice but not control animals. More details of neural signaling were elucidated when data were analyzed in term of power oscillation. These LFP patterns were considered as conditioned responses. It is possible that the olfactory inputs are fed to the eating related brain circuitries and amplified through associative learnings. Therefore, the presence of chocolate cues in the present study 11

would act as conditioned stimuli that trigger brain mechanism of emotional drive to eat via LHa-OB and LHa-NAc interpathway. Changes in coherent activity between LHa-NAc to chocolate cues suggest that homeostatic and hedonic feedings are interrelated. These can explain why hedonic and homeostatic hungers accompany both during the period of starvation and fullness. In the present study, coherent activity was decreased between the pair LHa-HP. According to a mechanism of conditioned response, the decreased connectivity between the

ro of

HP and LHa would weaken signal of homeostatic hunger whereas increased connectivities between the LHa-OB and LHa-NAc would strengthen the sensory and reward process of

-p

hedonic hunger during the exposure to chocolate cues instead (Fig.6). Conclusion

re

In summary, the present data demonstrated neural network connectivities induced by

lP

palatable food associated cues. The enhanced connectivity during LHa-NAc and LHa-OB suggest the model response to palatable food-associated cues that homeostatic satiety failed

na

to suppress the intense drive to eat palatable food.

ur

Conflict of interest

Jo

All authors declared that they have no conflict of interest.

Ethical approval All procedures performed in this study involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.

12

Acknowledgements This work was financially supported by the graduate school, and the department of Physiology, faculty of Science, Prince of Songkla University, Songkhla, Thailand and the research professional development project under the Science Achievement Scholarship of Thailand. Also, a personal scholarship through oversea thesis research scholarship supported

Jo

ur

na

lP

re

-p

ro of

by the graduate school, Prince of Songkla University is highly appreciated.

13

References [1]

J.C. Franklin, B.C. Schiele, J. Brozek, A. Keys, Observations on human behavior in experimental semistarvation and rehabilitation, J. Clin. Psychol. 4 (1948) 28–45.

[2]

M.R. Lowe, M.L. Butryn, Hedonic hunger: a new dimension of appetite?, Physiol.

[3]

A.A. Witt, M.R. Lowe, Hedonic hunger and binge eating among women with eating disorders, Int. J. Eat. Disord. 47 (2014) 273–280.

C. Kidd, N.J. Loxton, Junk food advertising moderates the indirect effect of reward

-p

[4]

ro of

Behav. 91 (2007) 432–439.

sensitivity and food consumption via the urge to eat, Physiol. Behav. 188 (2018) 276–

J.D. Beaver, A.D. Lawrence, J. van Ditzhuijzen, M.H. Davis, A. Woods, A.J. Calder,

lP

[5]

re

282.

Individual differences in reward drive predict neural responses to images of food, J.

[6]

na

Neurosci. 26 (2006) 5160–5166.

A.L. Maxwell, N.J. Loxton, J.M. Hennegan, Exposure to food cues moderates the

ur

indirect effect of reward sensitivity and external eating via implicit eating

Jo

expectancies, Appetite. 111 (2017) 135–141. [7]

N.J. Loxton, R.J. Tipman, Reward sensitivity and food addiction in women, Appetite. 115 (2017) 28–35. doi:https://doi.org/10.1016/j.appet.2016.10.022.

[8]

J.M. Hennegan, N.J. Loxton, A. Mattar, Great expectations. Eating expectancies as mediators of reinforcement sensitivity and eating, Appetite. 71 (2013) 81–88.

[9]

E. Stice, S. Spoor, J. Ng, D.H. Zald, Relation of obesity to consummatory and

14

anticipatory food reward, Physiol. Behav. 97 (2009) 551–560. [10]

B. Glenn Stanley, V.L. Willett, H.W. Donias, L.H. Ha, L.C. Spears, The lateral hypothalamus: a primary site mediating excitatory amino acid-elicited eating, Brain Res. 630 (1993) 41–49. doi:10.1016/0006-8993(93)90640-9.

[11]

J.M.R. Delgado, B.K. Anand, Increase of Food Intake Induced by Electrical Stimulation of the Lateral Hypothalamus, Am. J. Physiol. -- Leg. Content. 172 (1952)

[12]

ro of

162 LP – 168. http://ajplegacy.physiology.org/content/172/1/162.abstract. I.C. de Macedo, J.S. de Freitas, I.L. da Silva Torres, The influence of palatable diets in reward system activation: a mini review, Adv. Pharmacol. Sci. 2016 (2016).

D.C. Castro, S.L. Cole, K.C. Berridge, Lateral hypothalamus, nucleus accumbens, and

-p

[13]

re

ventral pallidum roles in eating and hunger: interactions between homeostatic and reward circuitry, Front. Syst. Neurosci. 9 (2015) 90.

R. Gutierrez, M.K. Lobo, F. Zhang, L. de Lecea, Neural integration of reward, arousal,

lP

[14]

and feeding: recruitment of VTA, lateral hypothalamus, and ventral striatal neurons,

K.C. Berridge, ‘Liking’and ‘wanting’food rewards: brain substrates and roles in eating

ur

[15]

na

IUBMB Life. 63 (2011) 824–830.

disorders, Physiol. Behav. 97 (2009) 537–550. E.S. Bromberg-Martin, M. Matsumoto, O. Hikosaka, Dopamine in motivational

Jo

[16]

control: rewarding, aversive, and alerting, Neuron. 68 (2010) 815–834.

[17]

A.A. Martin, T.L. Davidson, M.A. McCrory, Deficits in episodic memory are related to uncontrolled eating in a sample of healthy adults., Appetite. (2017).

[18]

A.A. Martin, T.L. Davidson, Human cognitive function and the obesogenic

15

environment, Physiol. Behav. 136 (2014) 185–193. [19]

B. Schaal, Human Foetuses Learn Odours from their Pregnant Mother’s Diet, Chem. Senses. 25 (2000) 729–737. doi:10.1093/chemse/25.6.729.

[20]

M.J. Prud’Homme, M.C. Lacroix, K. Badonnel, S. Gougis, C. Baly, R. Salesse, M. Caillol, Nutritional status modulates behavioural and olfactory bulb Fos responses to isoamyl acetate or food odour in rats: roles of orexins and leptin, Neuroscience. 162

[21]

ro of

(2009) 1287–1298. M.M. Meguid, J.R. Gleason, Z.-J. Yang, Olfactory bulbectomy in rats modulates feeding pattern but not total food intake, Physiol. Behav. 54 (1993) 471–475.

C.G. Larue, J. Le Magnen, The olfactory control of meal pattern in rats, Physiol.

-p

[22]

[23]

re

Behav. 9 (1972) 817–821.

D. Cheaha, S. Bumrungsri, S. Chatpun, E. Kumarnsit, Characterization of in utero

lP

valproic acid mouse model of autism by local field potential in the hippocampus and

[24]

na

the olfactory bulb, Neurosci. Res. 98 (2015). doi:10.1016/j.neures.2015.04.006. G. Paxinos, K.B.J. Franklin, The mouse brain in stereotaxic coordinates, Gulf

[25]

ur

professional publishing, 2004.

C. Spyraki, H.C. Fibiger, A.G. Phillips, Dopaminergic substrates of amphetamine-

Jo

induced place preference conditioning, Brain Res. 253 (1982) 185–193.

[26]

K. Diehl, R. Hull, D. Morton, R. Pfister, Y. Rabemampianina, D. Smith, J. Vidal, C. Van De Vorstenbosch, A good practice guide to the administration of substances and removal of blood, including routes and volumes, J. Appl. Toxicol. 21 (2001) 15–23.

[27]

A.S. Chen, J.M. Metzger, M.E. Trumbauer, X. Guan, H. Yu, E.G. Frazier, D.J. Marsh,

16

M.J. Forrest, S. Gopal-Truter, J. Fisher, Role of the melanocortin-4 receptor in metabolic rate and food intake in mice, Transgenic Res. 9 (2000) 145–154. [28]

D. Cheaha, E. Kumarnsit, Alteration of spontaneous spectral powers and coherences of local field potential in prenatal valproic acid mouse model of autism, Acta Neurobiol. Exp. (Wars). 75 (2015).

[29]

M. Alonso-Alonso, S.C. Woods, M. Pelchat, P.S. Grigson, E. Stice, S. Farooqi, C.S.

future research needs, Nutr. Rev. 73 (2015) 296–307. [30]

S.F. Farmer, Rhythmicity, synchronization and binding in human and primate motor

-p

systems, J. Physiol. 509 (1998) 3–14.

W. Singer, Synchronization of cortical activity and its putative role in information

re

[31]

ro of

Khoo, R.D. Mattes, G.K. Beauchamp, Food reward system: current perspectives and

processing and learning, Annu. Rev. Physiol. 55 (1993) 349–374. R.D. Traub, J.G.R. Jefferys, M.A. Whittington, Fast oscillations in cortical circuits,

lP

[32]

[33]

na

MIT press Cambridge, MA, 1999.

C.S. Herrmann, I. Fründ, D. Lenz, Human gamma-band activity: a review on cognitive

ur

and behavioral correlates and network models, Neurosci. Biobehav. Rev. 34 (2010) 981–992.

C. Pantev, Evoked and induced gamma-band activity of the human cortex, Brain

Jo

[34]

Topogr. 7 (1995) 321–330.

[35]

P.R. Roelfsema, A.K. Engel, P. König, W. Singer, Visuomotor integration is associated with zero time-lag synchronization among cortical areas, Nature. 385 (1997) 157.

17

[36]

N. Kopell, G.B. Ermentrout, M.A. Whittington, R.D. Traub, Gamma rhythms and beta rhythms have different synchronization properties, Proc. Natl. Acad. Sci. 97 (2000) 1867–1872.

[37]

V.K. Yeragani, D. Cashmere, J. Miewald, M. Tancer, M.S. Keshavan, Decreased coherence in higher frequency ranges (beta and gamma) between central and frontal EEG in patients with schizophrenia: a preliminary report, Psychiatry Res. 141 (2006)

Jo

ur

na

lP

re

-p

ro of

53–60.

18

Figure captions Fig.1 Representative raw LFP traces of the OB (Olfactory bulb), NAc (Nucleus accumbens), LHa (Lateral hypothalamus) and HP (Hippocampus) during first- (A) and second- (B) observations of control mice and pre- (C) and post- (D) chocolate sessions of chocolate experienced mice. The signals were displayed in 20 seconds scale with 0.2 millivolt units of

Jo

ur

na

lP

re

-p

ro of

amplitudes for the NAc, LHa, HP and 1 millivolt units for the OB.

19

Fig.2 Mean percentage of relative power in multiple frequencies ranges from the lateral hypothalamus (A), nucleus accumbens (B), dorsal hippocampus (C) and olfactory bulb (D) in chocolate experienced (n=9) and control (n=7) animals. Data were expressed in mean±S.E.M, and averaged values were statistically analyzed by using one-way ANOVA. * and ** indicate power activities of chocolate experienced animals with significant difference at p value <

Jo

ur

na

lP

re

-p

ro of

0.05 and 0.001, respectively, in relation to control group.

Fig.3 Averaged coherence of the lateral hypothalamus to their interconnectivity; the hippocampus (A), nucleus accumbens (B) and olfactory bulb (C) during pre- and post-

20

chocolate sessions (dashed and straight blue line, respectively) of chocolate experienced mice and first- and second- observations (dashed and straight black line, respectively) of control mice over frequencies domain (upper), and their percent relative coherence in multiple frequencies bands (lower). Data were expressed in mean±S.E.M, and averaged values were statistically analyzed by using one-way ANOVA. * indicates coherence of chocolate

Jo

ur

na

lP

re

-p

ro of

experienced animals with significant difference at p value < 0.05 in relation to control group.

Fig.4 Averaged coherence of the nucleus accumbens to their interconnectivity; the hippocampus (A) and olfactory bulb (B) and the hippocampus with the olfactory bulb (C)

21

during pre- and post- chocolate sessions (dashed and straight blue line, respectively) of chocolate experienced mice and first- and second- observations (dashed and straight black line, respectively) of control mice over frequencies domain (upper), and their percent relative coherence in multiple frequencies bands (lower). Data were expressed in mean±S.E.M, and

Jo

ur

na

lP

re

-p

ro of

averaged values were statistically analyzed by using one-way ANOVA.

Fig.5 Behavioral changes following chocolate sessions. Time spent in the apparatus between normal chow food and chocolate zones during pre- and post-chocolate sessions of chocolate experienced mice (n=9) (A), and first- and second-observations of control mice (n=7) (B)

22

were observed. Locomotor speeds during pre- and post-chocolate sessions of chocolate experienced group were monitored (C). Data were expressed in mean±S.E.M, and averaged values were statistically analyzed by using two-way ANOVA and paired sample t-test. *

Jo

ur

na

lP

re

-p

ro of

indicate the difference significantly at p value < 0.05.

Fig.6 Schematic diagrams of collective LFP data of neural connectivity among 4 brain regions following chocolate experiences in a mouse model. Significant increases (solid line)

23

and decreases (dotted line) in coherence value indicated for drawing of successful circuit-

Jo

ur

na

lP

re

-p

ro of

based learning for neural network observed during the exposure to chocolate cues.

24