Psikhologicheskie Issledovaniya • ISSN 2075-7999
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Polikanova I.S., Sysoeva O.V., Tonevitsky A.G. Association between serotonin transporter (5HTT) and mental fatigue development [Full text]

Full text in Russian: Поликанова И.С., Сысоева О.В., Тоневицкий А.Г. Связь серотонинового транспортера (5НТТ) с развитием утомления
Lomonosov Moscow State University, Moscow, Russia
Washington University School of Medicine, Saint-Louis, USA

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Effects of long-term mental fatigue on subjective, behavioral and electroencephalographic (EEG) parameters in LL and LS + SS polymorphisms (5HTT gene) carriers were examined. Mental fatigue was simulated with continuous cognitive tasks performance for 2.5 hours. It was shown that long-term cognitive load significantly impacted on almost all the above parameters. Significant differences in time reaction tasks and fatigue index (reflecting (alpha + theta)/beta ratio) between carriers of LL and LS + SS polymorphisms of 5HTT gene were observed. Lower serotonin concentrations were found to lead to higher resistance to fatigue development, thus the positive role of serotonin in fatigue development was shown.

Keywords: fatigue, cognitive load, serotonin transporter 5HTT, EEG, reaction time, individual alpha rhythm, fatigue index


Investigation of the problem of fatigue began in the middle of the 19th century with Erisman’s studies in hygiene of physical and mental labor [Erisman, 1877]. It has been further developed in experimental psychology in the works of researchers such as Galton, Krepelin, Ebbinghaus, and Binet [Leonova, 1994]. Significant contribution to development the problem of fatigue was also done in the psychotechnique - founder of which is Munsterberg. This problem is widely discussed in the physiology and psychophysiology by Sechenov, Pavlov, Teplov and Nebylitcyn. As a result, at the present moment in the study of fatigue has accumulated a huge amount of data in psychology, neuroscience and biochemistry. Despite this, at present there is no general theory of fatigue, describing the mechanism of it development. Also, there is no universally accepted definition of fatigue. In psychological research, the term "fatigue" is often understood as a specific functional state, resulting from intense or prolonged loads and is manifested in a temporary deterioration several psychological and physiological functions, reducing the efficiency and quality of work [Nepopalov et al., 2008].

In psychophysiology for investigation of functional states there are often used the method of spectral analysis, which reflects the dynamics of brain rhythms in different states and conditions. Many studies have shown that the spectral parameters of the electroencephalogram (EEG) are characterized by significant changes in various functional states, also in fatigue [Kropotov, 2010, Danilova, 2004; Alexandrov, 2007; Boksmen et al., 2005; Jap et al., 2009; Cheng et al., 2011; Trejo et al., 2005; Lal et al., 2007]. Many researchers have noted significant changes in the spectral characteristics of the EEG after prolonged cognitive load, which usually reflected in increase of power slow brain rhythms - alpha, theta and delta, and decrease in power of fast rhythms – beta and gamma [Boksmen et al., 2005; Jap et al., 2009; Cheng et al., 2011; Trejo et al., 2005; Lal et al., 2007]. Some authors have shown that fatigue index, which reflects the ratio of the power of slow rhythms to fast ((alpha + theta) / (beta)) is a good indicator for fatigue [Jap et al., 2009; Cheng et al., 2011]. Some researchers also noted that fatigue reflects in EEG by attenuation of individual alpha frequency (IAF), which is revealed in EEG as peak in the range of the alpha rhythm with eyes closed [Angelakis et al., 2004; Klimesh, 1999; Jap et al., 2009].

Some articles have shown the connection between fatigue and concentration of definite neurotransmitters, predominantly with dopamine and serotonin [Blomstrand, 2001; Davis et al., 2000; Castell, 1999]. Different genes have influence on their concentration, which depends on transportation, removing and density of receptors for these mediators. Meeusen showed that increase of concentration of serotonin lead to increase of developing of fatigue because of its role in sleeping [Meeusen et al., 2007; Newsholme et al., 1995]. Narita have shown a significant increase of longer (L and LS) allelic variants in 5HTT gene in patients with chronic fatigue syndrome compared to the controls [Narita, 2003]. 5HTT gene has association with serotonin transporter. And LL carries have more serotonin transporters on the cell membranes, so this transporters faster removes serotonin from synaptic cleft. In literature was shown that increase of serotonin concentration lead to fatigue development [Meeusen et al., 2007; Davis et al., 2000; Weicker, 2001]. Weicker showed that paroxetine (selective serotonin (5-HT) reuptake inhibitor) administration to endurance athletes, who performed a cycle ergometer test to exhaustion at moderate intensity, reduced time to exhaustion and post exercise cognitive performance in comparison to trials with placebo [Weicker, 2001].

In our study we hypothesized that LL genotype, which have association with less concentration of serotonin will have lower tendency to fatigue.



52 right-handed males with no history of psychiatric or neurological disease participated in the study (mean age ± SD, 24 ± 6, range: 18–30 years). The resting EEG was performed twice in control condition and after 2.5 hours of cognitive load (block of different cognitive tasks). Before and after cognitive load the participants performed block of tests including reaction time tasks, subjective assessment of current state (health, activity and mood questionnaire) and resting EEG recording (close eyes – 1 minute; open eyes – 1 minute).

Experimental tasks

There were three types of reaction time tasks: a) simple reaction task (SRT): press a button as soon as the stimulus (red cross) has appeared; b) choice reaction (CR): press “button 1” as soon as the red cross has appeared and “button 2” when green cross has appeared; c) maximal tapping (MT): press the “button 2” with maximal speed for 1 minute. Participants have also assessed subjectively their current states by using “The health, activity and mood questionnaire”, which was presented on the computer monitor.


E-prime (version 1.2) was used as stimuli presentation software. For stimuli presentation was used computer monitor Dell with screen resolution 1280×1024 pixels. All stimuli were presented on a black background. The fixation point in the centre of the screen was indicated by a white cross (Courier New, size 18). In SRT and CR tasks the duration of fixation point and stimuli was in 500–1500 ms range. In SRT task the presenting stimulus was a red cross (Courier New, size 72). In CR task the stimuli were a red cross (Courier New, size 72) and a green cross (Courier New, size 72), which were randomly presented. In both tasks the practice session consisted of 10 stimuli and the main session consisted of 70 stimuli. Only stimuli from the main session were taken into account.


The EEG was recorded with 256 channels EEG (EGI Electrical Geodesics), sampling rate 500 Hz, with vertex reference. The resting EEG consisted of 2 periods – 1 minute of eyes close and 1 minute of eyes open sessions. After EEG recording was performed re-montage to average reference and filtered (bandpass 1–150 Hz and 50 Hz notch). EEG data was automatically scanned for artifacts (the changed in amplitude more than 200 mV within the 640 ms window was considered as bad channel, more than 140mV as moving artifacts, and more than 55mV as eyes blink. The bad channel replacement procedure was performed (Net Station software).

Data analysis

The statistical analysis package Statistica 8 (for Windows, V 8.0, StatSoft) and MatLab (R2007b) were used for data analysis. For different experimental tasks (SRT, CR, MT) and psychological test (health, activity and mood questionnaire) means and standard deviations were calculated. For choice reaction task (CR) the number of errors was also counted. T-test for dependent variables and ANOVA were done to examine the effect of cognitive load on functional condition. For EEG analysis 20 seconds data of each session of resting EEG were used. Individual alpha frequency (IAF) and index of fatigue (FI) were calculated. The data were reduced to analysis of average for 5 regions and both hemisphere. The individual alpha frequency was determined EEG in eyes close session as having the maximum power at 7–14 Hz. The index of fatigue was calculated as sum of power of alpha and theta bands divided on power in beta band (FAT = sum power (4,5–11 Hz)/sum power(14–30 Hz)), [Jap et al., 2009].

Genetic analysis

For 5-HTTLPR genotyping, genomic DNA was extracted from venous blood and saliva samples according to standard procedures. Primers 5′-ATGCCAGCACCTAACCCCTAATGT-3′ and 5′-GACCGCAAGGTGGGCGGGA- 3′ were used to amplify a product that was 256 base pair (bp) product for the 14-repeat (s) allele and a 300 bp product for the 16-repeat (l) allele. Amplification reactions were performed in a total volume of 25 μL, containing approximately 100 ng of genomic template, 10 pmol of each primer, 0,2 mmol/L of each deoxynucleoside triphosphate (dNTP), 2,5 mmol/L of MgCl2, 16 mmol/L (NH4)2SO4, 0,125 mg/ml BSA, 8% glycerine, 0,001% xylencyanol and 2,5 unit of Taq polymerase. The polymerase chain reaction (PCR) cycling conditions consisted of an initial denaturation for 1 min at 94°C, followed by 35 cycles of 94°C for 30 s, 65 °C for 30 s, and 72°C for 30 s. Polymerase chain reaction products were separated on a 3% agarose gel аnd visualized by ethidium bromide staining. The polymerase chain reaction (PCR) based restriction fragment length polymorphism assay and real-time PCR were used in parallel. Analyses were carried out by different independent people. Fifty two DNA sampleswere run in duplicate. Only one mismatchwas detected – that sample was excluded.


Table 1 shows the mean points for each scale of Health, activity and mood questionnaire for two conditions: baseline condition and cognitive load condition. The results of t-test have shown the significant deterioration in Health, Activity and General subscales.

Table 1
Health, activity and mood questionnaire

5HTT Health Activity Mood General
LL 5,4/4,6** 5/4,2** 5,3/5,2 5,2/4,7*
LS + SS 5,5/4,6** 5/4,2** 5,5/5,3 5,3/4,7**

Note. Mean RT, scale: 1–7 points. Baseline condition/Cognitive load. Significant differences: * p ≤ 0,05, ** p ≤ 0,01.

Table 2 shows the mean RTs in Simple reaction task (SRT) for two conditions: baseline and cognitive load. The results of ANOVA have shown significant decrease of speed after fatigue in LL polymorphism (F(1,17) = 5,526; p < 0,04) and in LS + SS genotypes (F(1,23) = 4,361; p < 0,05). But there was found no significant difference between LL and LS + SS genotypes.

Table 2
Simple reaction task (SRT)

Gene 5НТТ Condition
Baseline Fatigue
LL 234 (23) 244 (28)
LS + SS 225 (17) 236 (26)

Note. Mean RT (SD).

Tables 3.1 and 3.2 show the mean RTs and errors in Choice reaction task (CR) for two conditions: baseline and cognitive load. The results of ANOVA have shown significant decrease of speed after fatigue in LL (F(1,17) = 8,258; p < 0,011) and in LS + SS (F(1,23) = 4,390; p < 0,05) polymorphisms. But there were no significant differences between different genotypes. There was also significant decrease in the number of errors in the whole group (F(1,44) = 4,225; p < 0,05), but separately in LL and LS + SS genotypes there were not any significant changes.

Table 3.1
Choice reaction task (CR)

Gene 5НТТ Condition
Baseline Fatigue
LL 437 (58) 421 (50)
LS + SS 405 (51) 388 (60)

Note. Mean RT (SD).

Table 3.2
Choice reaction task (errors)

Gene 5НТТ Condition
Baseline Fatigue
LL 5,64 6,8
LS + SS 6,04 7

Table 4 shows the mean RTs in maximal tapping for two conditions: baseline and cognitive load. The results of ANOVA have shown significant interaction between factors ‘condition’ (baseline and fatigue) and ‘polymorphism’ (LL and LS + SS): F(1,27) = 10,408; p < 0,004. These results mean that different genotypes have opposite tendencies in the speed changing. S-allele carriers (LS and SS) characterized by a significant decrease the speed after cognitive load (F(1,11) = 10,828; p <0,007), whereas the LL polymorphism carriers, an increase of speed, but not significantly (Table 4).

Table 4
Maximal tapping

Gene 5НТТ Condition
Baseline Fatigue
LL 178(18) 171(17)
LS + SS 164(16) 173(17)

Table 5 shows the mean IAF for LL and LS + SS genotypes in 5HTT gene in two conditions: baseline and cognitive load. ANOVA showed no significant difference between genotypes in IAF. ANOVA showed that IAF in LS and SS polymorphisms significantly decreased after fatigue in right hemisphere (F(1,23) = 5,097; p < 0,04). In LL genotype there was not significant reduction in IAF.

Table 5
Individual alpha frequency (IAF)

Brain regions IAF, Hz р-level
Baseline After load
Mean SD Mean SD
LL genotype
Frontal Left 9,83 1,05 9,74 0,99 0,528
Frontal Right 10,00 0,90 9,83 1,14 0,372
Central Left 10,04 0,87 9,84 1,12 0,500
Central Right 9,92 0,98 9,76 1,06 0,420
Parietal Left 10,14 0,92 10,12 0,92 0,744
Parietal Right 10,23 0,91 10,06 0,83 0,102
Temporal Left 9,98 0,84 9,85 0,93 0,528
Temporal Right 9,98 0,89 9,93 0,83 0,396
Occipital Left 10,28 1,07 10,07 0,86 0,306
Occipital Right 10,22 0,92 10,07 0,78 0,157
LS + SS genotypes
Frontal Left 9,71 0,91 9,49 1,04 0,458
Frontal Right 9,70 0,95 9,44 0,98 0,241
Central Left 9,49 0,98 9,49 1,14 0,732
Central Right 9,61 0,90 9,39 1,07 0,361
Parietal Left 9,73 0,97 9,75 1,06 0,977
Parietal Right 10,10 0,85 9,74 1,04 0,097
Temporal Left 9,72 1,01 9,60 1,06 0,607
Temporal Right 9,94 0,80 9,58 0,97 0,040*
Occipital Left 9,85 0,85 9,58 0,89 0,179
Occipital Right 10,05 0,82 9,64 0,89 0,086

Note. Significant differences: * p ≤ 0,05, * p ≤ 0,01.

The Table 6 shows the mean indexes of the fatigue index for different genotypes in 5HTT gene. ANOVA showed significant difference between LL and LS + SS genotypes in fatigue index (FI) for both conditions (F(4,148) = 2,7487; p < 0,03). The significant increase of FI was found in LL genotype in central and parietal left regions. For LS + SS genotypes the significant differences were found almost in all regions, except frontal left.

Table 6
Index of fatigue

Brain regions Fatigue index р-level
Baseline After load
Mean SD Mean SD
LL genotype
Frontal Left 2,98 1,75 3,99 3,33 0,121
Frontal Right 5,83 1,56 6,59 2,16 0,121
Central Left 2,38 1,30 3,46 2,74 0,034*
Central Right 2,34 1,32 3,57 1,88 0,000***
Parietal Left 2,23 1,33 3,72 2,73 0,003**
Parietal Right 3,29 2,86 4,13 3,16 0,301
Temporal Left 2,13 1,14 2,93 1,84 0,070
Temporal Right 2,08 1,39 2,96 2,79 0,088
Occipital Left 2,39 1,47 2,93 1,86 0,109
Occipital Right 2,19 1,24 2,83 1,83 0,121
LS + SS genotypes
Frontal Left 5,10 3,04 5,22 2,72 0,113
Frontal Right 7,54 2,99 7,91 2,83 0,019*
Central Left 3,85 2,87 4,25 1,85 0,049*
Central Right 3,97 3,00 5,25 2,76 0,002**
Parietal Left 4,41 3,39 5,76 3,19 0,003**
Parietal Right 5,69 4,09 7,34 4,25 0,004**
Temporal Left 3,54 2,51 4,55 2,50 0,002**
Temporal Right 3,76 2,65 4,76 2,76 <0,003**
Occipital Left 4,47 3,21 5,32 3,04 0,031*
Occipital Right 4,09 2,82 5,15 2,66 0,007**

Note. Significant differences: * p ≤ 0,05, ** p ≤ 0,01.


We have carried the statistical analysis for different polymorphisms in 5НТТ gene for three types of parameters: subjective; behavioral and EEG activity.

In subjective state after cognitive load there was deterioration of health and activity [Wijesuriya et al., 2007]. Some authors noted that after prolonged cognitive tasks the subjects feels themselves worse than before. Trejo found that after 3 hours of arithmetic tasks the energy and calmness significantly decreased and tiredness significantly increased (Trejo et al, 2005). Subjective analysis showed deterioration in health and activity after cognitive load and didn’t show any significant values between different genotypes. All polymorphisms in 5HTT gene had approximately the same results.

In behavioral reactions there was significant increase of time reaction in SRT task and decrease of time reaction in CR task with a simultaneous increase in errors. It may suggest that our subjects experienced cognitive fatigue and trying to obtain the speed they sacrificed accuracy for speed in CR task. Several studies showed that RTs became longer with increasing mental fatigue [Lorist et al., 2000; Boksem et al., 2006; Trejo et al., 2005] and increases in speed are usually associated with a decrease in accuracy. It’s well known phenomenon and in the literature called “Speed-accuracy tradeoff” [Wickelgren, 1977; Wood et al., 1976; Sternberg, 2004]. In time reaction tasks we obtained that LL genotype characterized by slower time reaction in both tasks (SRT and CR) and in both conditions (baseline and after load). Perhaps this linked with the speed of information processing which also depends on concentration of definite mediators in central nervous system.

In current study cognitive load reflected in decreasing of IAF, but significant decrease was found only in right temporal area. Some authors have shown that after mental fatigue during 3 hours in EEG power spectrum the peak of alpha frequency is decreased, but not significantly. The power of alpha rhythm is increased after mental fatigue [Boksmen et al., 2005; Trejo et al., 2005]. In EEG results we found that LS + SS polymorphisms characterized by significant decreased of IAF after fatigue in right hemisphere. In LL genotype there was not significant reduction in IAF. Many authors showed decrease of IAF after prolonged cognitive load and linked it with tiredness and fatigue development [Angelakis et al., 2004; Klimesh, 1999]. Because decline of functional state lead to shifting EEG power spectra to lower wave frequency.

Index of fatigue, which reflects the ratio of brain slow wave activity to fast brain activity significantly changed after cognitive load predominantly in central, parietal and temporal areas in eyes closed condition and in parietal and temporal regions in eyes open condition. Budi Thomas Jap showed that ratio (theta + alpha)/beta have a greater increase after prolonged monotonous activity than other algorithms (alpha/beta, (theta + alpha)/(alpha + beta) or theta/beta) [Jap et al., 2009]. He found the significant increase of this index in temporal area and also changing in delta and theta activities in frontal, central and parietal regions. In our study we found that most significant changes observed in frontal, central, parietal and temporal electrodes. In index of fatigue we also found significant results between different genotypes in 5HTT gene. We found the weak increase of fatigue index in LL genotype, significant increase was observed only in both central and parietal left regions. In LS and SS genotypes was observed huge increase of fatigue index almost in all regions, except frontal left region. The LL carries have faster serotonin removal from synaptic cleft compared with others. So, they have less serotonin concentration. In articles there were shown that increase of serotonin concentration lead to fatigue development. Therefore our results corresponds published data [Meeusen et al., 2007; Davis et al., 2000; Weicker, 2001].

Obtained in current study results correspond to literature data about the role of serotonin in the brain processes. So, numerous studies showed that the increase of serotonin lead to tiredness states and fatigue because of its role in sleep. In our study we showed that LS and SS carries in 5HTT gene characterized by lower IAF than LL carries, which have more higher and stable IAF. In fatigue index they characterized by less changes after fatigue, but in generally they had a little bit higher indexes than LS and SS carries. It means that LL carries EEG power spectra characterized by dominating of low wave frequency generally.

Raphe nuclei which containing the serotonin innervate nearly all brain regions. So, significant changes in its concentration reflected almost in all brain areas. Raphe nuclei also influenced on motor neurons activity [Nicholls et al., 2003]. So, lack of serotonin may be reflected in deterioration of motor neurons activity. Perhaps, the slower time reaction in LL genotype may be caused by lack of serotonin. Findings obtained in current study can mean that definite serotonin concentration can influence on functional state changes during prolonged cognitive load. So, we showed that less concentration of serotonin lead to high resistance of fatigue development.

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Received 16 May 2011. Date of publication: 20 August 2012.

About authors

Polikanova Irina S. Ph.D student (2012), Department of Psychophysiology, Faculty of Psychology, Lomonosov Moscow State University, ul. Mokhovaya, 11, str. 9, 125009 Moscow, Russia.
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Sysoeva Olga V. Ph.D, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO 63110, USA.
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Tonevitsky Aleksander G. Ph.D, Professor, Corresponding Member, Russian Academy of Sciences: Head, Department of Physical Education and Sport, Lomonosov Moscow State University, Leninskie Gory, 119991 Moscow, Russia.
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Suggested citation

Polikanova I.S., Sysoeva O.V., Tonevitsky A.G. Association between serotonin transporter (5HTT) and mental fatigue development. Psikhologicheskie Issledovaniya, 2012, Vol. 5, No. 24, p. 7. (in Russian, abstr. in English).

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