Seasonality in human cognitive brain responses
Christelle Meyera,b,1, Vincenzo Mutoa,b,c,1, Mathieu Jaspara,b,c,1, Caroline Kusséa,b, Erik Lambota,b, Sarah L. Chellappaa,b,
Christian Degueldrea,b, Evelyne Balteaua,b, André Luxena,b, Benita Middletond, Simon N. Archerd, Fabienne Collettea,b,c,
Derk-Jan Dijkd, Christophe Phillipsa,b,e, Pierre Maqueta,b,f,2,3, and Gilles Vandewallea,b,2,3
aGIGA-Research–Cyclotron Research Centre–In Vivo Imaging, University of Liège, 4000 Liège, Belgium; bWalloon Excellence in Life Sciences andBiotechnology (WELBIO), 4000 Liège, Belgium; cDepartment of Psychology: Cognition and Behavior, University of Liège, 4000 Liège, Belgium; dSurrey Sleep
Research Centre, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XP Guildford, United Kingdom; eDepartment of Electrical Engineeringand Computer Science, University of Liège, 4000 Liège, Belgium; and fDepartment of Neurology, Centre Hospitalier Universitaire de Liège, 4000 Liège,
Belgium

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Edited by Bruce S. McEwen, The Rockefeller University, New York, NY, and approved January 8, 2016 (received for review September 11, 2015)

Daily variations in the environment have shaped life on Earth,with circadian cycles identified in most living organisms. Likewise,
seasons correspond to annual environmental fluctuations to whichorganisms have adapted. However, little is known about seasonalvariations in human brain physiology. We investigated annual
rhythms of brain activity in a cross-sectional study of healthy young
participants. They were maintained in an environment free ofseasonal cues for 4.5 d, after which brain responses were assessedusing functional magnetic resonance imaging (fMRI) while they
performed two different cognitive tasks. Brain responses to bothtasks varied significantly across seasons, but the phase of theseannual rhythms was strikingly different, speaking for a complex
impact of season on human brain function. For the sustained
attention task, the maximum and minimum responses were locatedaround summer and winter solstices, respectively, whereas for theworking memory task, maximum and minimum responses wereobserved around autumn and spring equinoxes. These findings
reveal previously unappreciated process-specific seasonality inhuman cognitive brain function that could contribute to intraindividual cognitive changes at specific times of year and changes inaffective control in vulnerable populations.
season| cognition | fMRI | annual | attention

Daily variations in the environment have constrained life on
Earth, with circadian cycles identified in most living organisms, including in human physiology and cognition (1, 2). Seasonalvariations in the environment have also triggered annual adaptations that are observed in the majority of species (for a review, seeref. 1). However, seasonal variations may seem more limited inour species or they are at least less recognized (3). Seasonality has
indeed been reported for several physiological aspects including
blood pressure (4), cholesterol (5), or calorie intake (6), withhigher levels seen in winter or fall for food intake. Recently,
seasonal variation in expression levels of a large set of genes has
been reported for human white blood cells and adipose tissue (7).
Furthermore, seasonal variations have been observed in several
behavioral dimensions with peaks occurring at different time ofyear depending on the variable considered: conception (winter/spring peak) and death [winter peak (8)] or violent suicide [spring/summer peak (9)]. Mood has been the most extensively studied
aspect of human behavior, with a large portion of the general
population undergoing seasonal deteriorations in mood in winter,but these do not reach clinical threshold [e.g., subsyndromalseasonal affective disorder: up to 18% in North America (10)].
Furthermore, sparse studies suggest that, in addition to mood,other cognitive brain functions show annual variations in healthy
individuals, but results are not consistent (11–13).
Animal research suggests that the suprachiasmatic nucleus,
site of the master circadian clock, is at least one of the sites mediating annual rhythmicity (14). The well-characterized circadian
genetic machinery is also implicated in tracking seasonal changes(15). It is therefore likely that seasonality in human species involves
the circadian timing system and that the previously identified brain
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correlates of the circadian variations in cognitive brain function (2)play a role in annual changes in human cognition. Although seasonalchanges in photoperiod together with neurotransmitters and neurotrophic factors seem to mediate seasonal mood variation in humans(16–20), the brain bases of seasonality in human cognition remain
elusive. This lack of evidence arises in part from the fact that genuine
seasonal rhythms of human brain function are difficult to measure. A
number of factors that could directly affect brain function have indeed to be controlled: light exposure, sleep/wake rhythm, external
temperature, food intake, physical exercise, and social interactions.
Here, we took advantage of a study completed in our laboratory
under strictly controlled conditions, devoid of seasonal cues for
4.5 d, to assess annual rhythms in human cognitive brain function.
The primary goal of the study was to assess the neural correlates of
two tasks probing different cognitive domains during total sleep
deprivation. Because the enrollment of participant was carefully
timed such that the assessments would span all seasons, annual
variations in the neural responses (assessed after recovery from thesleep deprivation) could be assessed. We hypothesized that, following 4.5 d under controlled conditions, brain responses to both tasks
would undergo seasonal variations with higher and lower responses,
respectively, around summer and winter solstices. In line with previous observations (13), we further postulated that annual variations
would be more evident in the more basic attentional task comparedwith the more complex, higher-order executive task.
Significance
Evidence for seasonality in humans is limited. Mood probably
stands as the aspect of human brain function most acknowledged
as being affected by season. Yet, the present study provides
compelling evidence for previously unappreciated annual variations in the cerebral activity required to sustain ongoing cognitive
processes in healthy volunteers. The data further show that thisannual rhythmicity is cognitive-process-specific (i.e., the phase of
the rhythm changes between cognitive tasks), speaking for acomplex impact of season on human brain function. Annual variations in cognitive brain function may contribute to explain
intraindividual cognitive changes that could emerge at specifictimes of year.
Author contributions: D.-J.D. and P.M. designed research; C.M., V.M., M.J., C.K., E.L., andF.C. performed research; C.D., E.B., and A.L. contributed new reagents/analytic tools; S.L.C.,
E.B., and C.P. provided analysis expertise; C.D., A.L., and B.M. provided technical expertise;
S.N.A. and F.C. provided expertise and feedback; C.M., V.M., M.J., S.L.C., B.M., P.M., and G.V.
analyzed data; and C.M., S.N.A., D.-J.D., C.P., P.M., and G.V. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1C.M., V.M., and M.J. contributed equally to this work.

2

P.M. and G.V. contributed equally to this work.

3

To whom correspondence may be addressed. Email: pmaquet@ulg.ac.be or gilles.
vandewalle@ulg.ac.be.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1518129113/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1518129113

Fig. 1. Schematic representation of the protocol. Following an 8-h baseline night of sleep in complete darkness, participants underwent a 42-h sleep deprivation underconstant routine conditions in dim light (<5 lx, 19 °C, semirecumbent position, regular liquid isocaloric food intake, no time cues, sound-proofed room). They were thengiven a 12-h recovery sleep opportunity in darkness, an hour after which they completed fMRI recordings (red star). Functional MRI recordings were completed whilelying down in darkness and included PVT and n-back tasks. Relative clock time for participants habitually waking up at 8:00 AM. Striped blue box during sleep deprivation represents the habitual sleep period. The figure represents the last ∼2.5 d of the protocol; see Fig. S1 for a description of the entire in-laboratory experiment.

conducted in Liège (Belgium, latitude 50.633° N, longitude 5.567° E),
between May 2010 and October 2011. They were instructed tofollow a regular sleep/wake schedule for 3 wk before a 4.5-d

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Results and Discussion
Twenty-eight young, healthy participants [age 21 ± 1.5 y (mean ±SD); 14 women; Table S1] took part in a cross-sectional study

Fig. 2. Seasonal variations in brain activity associated with sustained attention. (A) Significant (pcorrected < 0.05) seasonal variations in PVT brain responses displayed over themean structural image of all participants (display at puncorrected < 0.001). Only clusters >30 voxels are displayed (see Table 1 for full results). Vertical color bar corresponds to F-testvalues (B) Double plot of PVT brain response estimates in regions of A in a sinusoidal representation. Day 1 corresponds to January 1. First letter of each month is displayed on
top. Thickblackline corresponds to averageof all responseestimates. Gray arearepresents daily daylength (in minutes) in Liège. (C) Same as B in polar coordinates; arrow length
represents seasonal variation amplitude. One degree is roughly equal to 1 d (360° for 365 d). Maximum responses were located between 152° and 188° (mean 168.9) (i.e., June 3
and July 9) (mean June 20). (D) Double plot of individual activity estimates in a representative region of A (amygdala) and its sinusoidal fit (red line). (E) Seasonal environmental
factors recorded in Liègein 2011:temperature (Celsius degrees, blue), humidity (percent, red), daylength (minutes, green),andday-to-day day-length gain/loss (minutes, violet).

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PNAS | March 15, 2016 | vol. 113 | no. 11 | 3067

Table 1. Seasonal variation in PVT brain responses
Brain areas

Side

XYZ

Z scoreP value

Frontoorbital gyrus

L
L
R
L
L
L
R
L
L
L
L
R
L
L
R
R
L
R
L
L
R
R
L

−38 54 −6−26 52 −10
10 56 −12
−26 60 20−14 20 64−50 18 38
2 20 48−4 −42 22−18 −50 36−56 −36 30−30 −46 34
30 −42 36−48 −58 16−38 16 −36
50 −56 −20
42 −58 −18−32 −28 -24
28 −14 -24−6 4 −4−22 −10 −24
18 −2 010 −6 8
−8 −8 10

3.43
3.56
3.34
4.32
4.56
3.29
3.11
3.10
3.50
3.93
3.29
3.37
3.45
4.66
3.74
3.12
4.61
4.58
3.23
4.87
4.21
3.86
3.79

0.017
0.012
0.022
<0.01*<0.01*
0.025
0.042
0.042
0.014
0.004
0.025
0.021
0.016
<0.05*
0.007
0.039<0.05*<0.05*
0.030<0.05*
0.001
0.005
0.006

Medial frontoorbital gyrus
Superior frontopolar gyrus
Superior frontal gyrus
Middle frontal gyrus
Pre-SMA
Posterior cingulate gyrus
Precuneus
Supramarginal gyrus
Intraparietal sulcus
Superior temporal sulcus
Temporal pole
Fusiform gyrus

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Parahippocampal gyrus
Hippocampus
Caudate nucleus
Amygdala
Globus pallidus
ThalamusP values corrected for multiple comparisons over a priori small volume ofinterests, except *corrected over the entire brain. X Y Z: coordinates (millimeters) in Montreal Neurological Institute stereotactic space. All regions survived
to an inclusive mask (puncorrected = 0.001) consisting of a brain map of thepotential PVT brain responses covarying with day length, suggesting that annual variations in all regions are significantly driven by the seasonal changes in
day length. No region survived to an inclusive mask (puncorrected = 0.001),
whereas all regions survived exclusive masking (puncorrected < 0.05) with a brain
map of the potential executive brain responses covarying (i) subjective moodand (ii) PVT performance (median, 20% fastest, 20% slowest reaction times),suggesting that annual variations in all regions are not significantly driven by
the seasonal changes in these variables. L, left; R, right.

in-laboratory protocol devoid of seasonal cues (Figs. S1 and S2).
Functional MRI (fMRI) recordings were acquired 1 h after wakeup time, following 63 h of strictly controlled experimental conditions (Fig. 1). Each recording included a sustained attention task
[visual psychomotor vigilance task, PVT (21)], and a higher-order
executive function task [auditory n-back task, involving storage,
updating, and comparison of information in working memory (22)].
We first focused on the brain responses induced by the PVT
and found significant annual variations in areas involved in alertness [thalamus (23) and amygdala (24)] and in executive control[frontal areas (25) and hippocampus (26)] (Fig. 2A and Table 1).
Seasonal variations were also detected in the globus pallidus,
parahippocampal gyrus, fusiform gyrus, supramarginal gyrus, andin the temporal pole recruited during PVT execution (27, 28) andin the precuneus involved in visuospatial attention (29). As postulated, extraction of the seasonal variations in PVT brain responses revealed a similar rhythm in all these brain regions, withmaximal responses around mid-June, and minimal around midDecember (i.e., around solstices) (Fig. 2B).
Variations in PVT brain responses were not related to significant changes in PVT performance, which remained good andstable throughout the year (P > 0.2; Table S2). This guarantees
that fMRI differences were not significantly biased by differencesin performance to the task and suggests that fMRI is moresensitive than the behavioral tests we used in identifying seasonalvariations in cognition. Stable performance throughout the year
via distinct brain dynamics implies, however, that the “cost” ofcognition (i.e., the neural resources involved in or at disposal for
3068 | www.pnas.org/cgi/doi/10.1073/pnas.1518129113

cognition) change with time of year. We hypothesize that theseasonality in brain responses could predict some of the seasonalvariations in performance previously reported for potentiallymore sensitive tasks (11–13).
We next investigated whether other behavioral and physiological variables could account for the observed annual variations in
PVT brain responses. Subjective and objective neurophysiologicalmeasures of alertness and subjective assessments of affective dimensions acquired immediately before fMRI acquisitions did notchange significantly across seasons. In addition, in our dataset we
could not replicate seasonal changes in melatonin secretion profilethat were reported in some (30–33), but not all (34, 35), publications (P > 0.05; Table S2). Only self-reported mood varied
significantly over season (P = 0.003; Table S2), but this variation
was not significantly related to the seasonal changes in brain responses (Table 1 and Fig. S3). In summary, sustained attentionrelated brain activity fluctuates across seasons but these changes
were not related to variations in the behavioral, endocrine, orneurophysiological parameters assessed in our study.
Photoperiod is the most obvious factor associated with season
and both the intensity and spectral composition of light to whichpeople are exposed vary with season (36). Fig. 2, indeed, suggeststhat PVT brain responses were closely related to photoperiod(gray area, Fig. 2B). A formal analysis revealed that all PVT brain
responses showing seasonal variations were significantly associatedwith day length. This finding could imply that there is a “physiological memory” for the photoperiod to which participants were
exposed before admission to the laboratory. Indeed, before fMRI
recordings, participants had not seen sunlight for 4.5 d and had
been for 63 h in dim light during wakefulness and in darkness
during sleep episodes. Consistently, effects of prior light exposure(“photic memory”) on cognitive brain responses have formerly
been demonstrated on a much shorter timescale in humans (37)and photoperiod memory has previously been described as “aftereffects” of photoperiod on circadian clock neurons in rodents (38).
Whether our data reflect a true human photoperiod memory is,however, not possible to ascertain because many other environmental factors covary with season and photoperiod, including air
temperature and humidity (Fig. 2E).
Having established seasonal/annual variations in sustainedattention-related brain responses, we then examined whether
such variations could be generalized to other cognitive domains
by considering the n-back task implemented in our protocol. We
found that brain responses to this executive task varied significantly with season in the thalamus, including the pulvinar, and in
prefrontal and frontopolar areas, similar to the PVT results. In
addition, significant annual variation was observed in the insula,a brain region involved in executive processes, attention, andaffective regulation (39) (Fig. 3A and Table 2). Compared with
PVT brain responses, significant seasonal variations seemed toencompass a reduced set of brain areas, which could indicate a
relative decrease in seasonality on executive brain responses, inline with previous suggestions of a reduced seasonal impact on
behavioral measures of more complex tasks (13).
This qualitative task-specific difference was complemented by astatistically significant difference in the dynamics of brain response
estimates across the year, with maximum and minimum responses
being located ∼3 mo later for the n-back compared with the
PVT (i.e., around autumn and spring equinoxes, respectively)(Fig. 3 B and C) (day of the year at responses maximum phase:
PVT, 168.9 ± 8.2; n-back, 265.7 ± 13; t11 = −20.16; P < 0.001).
Similar to the PVT, performance on the n-back was good andstable throughout the year in our sample (Table S2). However,covariation with photoperiod was not significant for any of theexecutive brain responses that significantly varied with season.
As depicted in Fig. 3, there seems, however, to be a striking similarity between annual dynamics in executive brain responses andday-to-day variation in daylength (i.e., the number of minutes ofMeyer et al.

day length gained or lost from one day to the next, which peaks at
the equinoxes). This similarity is indeed confirmed statistically(Table 2). As for photoperiod, however, factors such as air temperature and humidity (Fig. 2E) covary with day-to-day day-lengthvariations such that these are equally likely to contribute to seasonality in cognitive brain function.
Overall, the results provide clear evidence for seasonality indiverse types of cognitive processes and suggest that the annual
dynamics are process-specific. One might postulate that more basic
cognitive processes, such as attention, are more tightly related tobasic environmental changes (e.g., day length), whereas higher
cognitive processes are related to more complex cues, such as, forinstance, social interactions (e.g., summer holidays usually encompass usually July and August in Belgium). This speculation
cannot be tested here but would imply that brain response seasonal dynamics would be different in countries with different environmental and social constraints.
Interestingly, seasonal variations have been found in monoamines that are often related to cognitive functions, notably attention and executive processes (40, 41). Seasonal changes inserotonin levels in cerebrospinal fluid and blood as well as serotonin transporter binding have been repeatedly observed (but not
systematically; see refs. 42 and 43), mostly leading to higher serotonin levels in summer (19, 44, 45) (i.e., with a pattern potentially similar to the annual variations we observed in PVT brain
responses). As a matter of fact, sunlight-dependent variations
in serotonin levels have been detected in cortical (frontal,
cingulate, and insular cortex), limbic (amygdala and hippocampus),
Meyer et al.

and subcortical (thalamus) areas (44–46), similar to those detected here in response to a PVT task. In contrast, the emerging
seasonal pattern for dopamine brain concentration is characterized by higher levels in fall and lower levels in spring (47, 48),
that is, with a pattern reminiscent of the annual variations inexecutive brain response observed in our sample. Similarly,
seasonal variation in serum brain-derived neurotrophic factor
concentration, a protein involved in learning and the regulationand plasticity of neuronal network, has been reported to undergo
annual dynamics leading to higher circulating levels in the fall(49). Whether brain responses to learning tasks would have asimilar annual pattern as the brain activity related to a working
memory task remains to be investigated. As a whole, it seems
that key modulators of brain function show at least some seasonality, potentially contributing to the seasonal changes incognitive brain response we detected.
Influence of season is broad in the animal kingdom and encompasses locomotion, body mass, endocrine function (melatonin secretion), pelage, and sexual activity (1, 50, 51). Expressionof at least part of the human genome seems to be seasonal (7),speaking for a potential broad impact of season also in humans.
Our findings indicate that, in addition to time of day (2), time ofyear influences higher cognitive brain function in healthy participants. Our results have direct and important bearing on our
understanding of intraindividual cognitive changes that could
emerge at specific times of year.
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Fig. 3. Seasonal variations in executive brain activity. Display as in Fig. 2. (A) Significant (pcorrected < 0.05) seasonal variations in auditory three-back brain
responses minus control task brain responses (simple letter detection). (B) Executive brain response estimates in regions of A. Gray area represents day-to-day
change in photoperiod in Liège (minutes). (C) Same as B in polar coordinates. Maximum responses were located between 243° and 282° (mean 265.75) (i.e.,
September 3 and October 12) (mean September 22). (D) Double plot of individual activity estimates in a representative regions of A (middle frontal region)and its sinusoidal fit (red line).

Table 2. Seasonal variation in executive brain responses(three-back minus letter detection)
Brain areas

Side

XYZ

Z scoreP valueSuperior frontopolar gyrus
Superior frontal sulcus
Middle frontal gyrus

R
L
R
L
L
L
R
L
R
R
R
R

32 56 18
−24 42 22
26 52 6−20 56 4−50 30 14−46 −14 20
34 16 10−30 −6 14
38 −20 4
34 −16 −6
16 −18 12
10 −22 12

4.16
3.19
4.07
3.66
3.20
3.29
3.52
3.39
3.56
3.34
3.14
3.10

0.001
0.032
0.002
0.008
0.031
0.024
0.013
0.018
0.011
0.021
0.037
0.040

Inferior frontal sulcus
Frontal operculum
Anterior insula
InsulaPosterior insula
ThalamusPulvinar

P values corrected for multiple comparisons over a priori small volume
of interests. X Y Z: coordinates (millimeters) in Montreal Neurological Institute stereotactic space. No regions survived inclusive masking (puncorrected< 0.001), whereas all regions survived exclusive masking (puncorrected < 0.05)
with a brain map of the potential executive brain responses covarying with(i) daylength, (ii ) subjective mood, and (iii ) three-back performance(d-prime), suggesting that annual variations in all regions are not significantly driven by these variables. Most regions survived inclusive masking(puncorrected = 0.001) consisting of a brain map of the potential executive
brain responses covarying with day-to-day day-length variation, suggestingthat annual variations in all regions, except thalamus and pulvinar, are significantly driven by the seasonal changes in day-to-day day-length variation.
L, left; R, right.

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Materials and Methods
Additional methodological descriptions are provided in SI Materials
and Methods.
The study was approved by the local Ethics Committee of the University ofLiège and participants gave their written informed consent. Participants
underwent first 3 wk of a controlled sleep–wake schedule before the inlaboratory procedure which began in the evening of day 1 and ran over fournights (Fig. S1). This period was completed in the absence of seasonal cues
(no access to daylight or external information such as internet access orcellular phones). Starting on the morning of day 3, participants remained
awake for 42 h under constant routine (CR) conditions during which endocrine, neurophysiology, and neuropsychological measures were regularly collected. Sleep deprivation was followed by a 12-h recovery night in darkness.
Day 5, while the participant were still in dim light, was devoted to an fMRI
session that was carried out 1 h after wake up and is the main focus of thecurrent paper. Subjective sleepiness and affective dimensions were assessedhourly throughout the protocol.
Cognitive Tasks. The fMRI session included two cognitive tasks separated in
two acquisition runs. The PVT required pressing a button as quickly as possible
when a stopwatch pseudorandomly started in the center of the screen. Meaninterstimulus interval was set to be between 2 and 10 s and trial duration was
a maximum of 10 s. The auditory three-back implies to state whether or not a
consonant was identical to the consonant presented three stimuli earlier.
Stimulus onset interval was set at 2 s. Letters were presented in block of 30
consonants separated by 10 to 20 s of rest. Six blocks of three-back were
presented to each participant in addition to four blocks of a letter detection
task consisting of identifying the letter “k” in a stream of consonants (sameblock duration and stimulus interval).

stick functions, convolved with a canonical hemodynamic response function.
For the PVT 20% fastest, 20% slowest, and intermediate reaction times andlapses were modeled as separate regressors. For the n-back, three-back, andletter detection task trial types were modeled separately. The design matrix
also included regressors for movement parameters, derived from realignment of functional volumes, which were considered as covariates of nointerest. A high-pass filter was implemented using a cutoff period of 128 s.
Contrasts of interest consisted of the main effect of the intermediate reaction times of the PVT and the executive component of the three-back(three-back hits minus letter detection hits). These individual contrast images were entered in the second-level analysis. This latter random effects
analysis included two covariates consisting of year-long period sine andcosine functions for which each day of the year (day 1 = January first) isalmost equivalent to 1° (365 d for 360°).
Statistical inferences were performed using F test combining both sineand cosine covariates and thresholded at P < 0.05 after corrections for
multiple comparisons (familywise error method) over the entire brain volume or over small spherical volumes (10-mm radius) around a priori locationsof interest taken from literature (SI Materials and Methods).
Significant F tests indicate voxels with a response showing a seasonalvariation with annual periodicity. From the parameters of the sine and cosine regressors, the phase and amplitude of the seasonal effect are estimated as the arctangent of the ratio of the parameters and the square rootof their sum of square, respectively:RðtÞ = betas* sinð2* π* t=TÞ + betac * cos 2 * π * t=T + «= Α* cosð2* π* t=T  φÞ + «and
φ = atan2ðbetas,betacÞ

A = sqrt betas2 +betac2 ,where R is the voxel response, betas/betac are the parameters for the sine/
cosine regressors, T is the period of 1 y, « is the residual of the model, φ is thephase of the seasonal response [R is maximum at tMAX = φ*T/(2*π)], and A isthe amplitude of the seasonal response.
Four additional separate random effects analyses included (i) day length,(ii) subjective mood, (iii) day-to-day day-length variation, and (iv) behavioral
performance (PVT performance: median, 20% fastest, and 20% slowest reaction times; three-back performance: d-prime) as covariates to constitutemaps of the brain areas covarying with each variable. Maps were used asinclusive or exclusive masks over the results of the seasonality analyses
thresholded at P < 0.001 or P < 0.05 uncorrected, respectively.
Behavioral and Physiological Data Analysis. Multiple regression analyses
searching for seasonal variation in behavioral measures were performed(STATISTICA 10; StatSoft) using sine and cosine as independent variables.
We tested their influence on (i ) the subjective sleepiness alone, (ii ) the
six affective dimensions of the visual analog scale, (iii ) PVT median reaction time, lapses, per 20 and per 80 together, and (iv) and three-backd-prime and criterion together.
Similar multiple regression analyses searching for seasonal variation inphysiological and endocrine measures were also performed. Dependent
variables were grouped as follows: (i) theta and alpha power on Cz together;(ii) melatonin amplitude, DMOn, DLMOff, midpoint, and width; (iii) total
sleep time, sleep efficiency, stage-two and slow wave, each one separately,
for baseline night and recovery night; and (iv) the timing of the last fMRI
session relatively to DLMon and DLMoff.

fMRI Data Analysis. Data were spatially preprocessed (standard parameters)
and analyzed using SPM8 (www.fil.ion.ucl.ac.uk/spm) implemented in
MATLAB 7.10 (MathWorks Inc.). Statistical analysis proceeded in two steps, afixed and a random effects analysis, to take into account the variance at theindividual and at the group level, respectively. Each trial type of the PVT andeach hit of the three-back and letter detection task were modeled using

ACKNOWLEDGMENTS. We thank the Institut Royal Météorologique ofBelgium and A. Shaffii-Le Bourdiec, A. Golabek, A. Claes, B. Herbillon,
B. Lauricella, and P. Hawotte. This study was funded by Fonds De La
Recherche Scientifique Grant FRSM 3.4516.11, Actions de Recherche Concertées
Grant ARC 09/14-03 of the Fédération Wallonie-Bruxelles, Université de Liège,
Fondation Médicale Reine Elisabeth, Walloon Excellence in Life Sciences andBiotechnology Grant WELBIO-CR-2010-06E, the European Regional Development
Fund Radiomed project, Fondation Simone et Pierre Clerdent, Fonds Léon
Fredericq, Wallonie-Bruxelles International, Fonds H. et L. Fredericq, Fundação
Bial Grant 226/10, and Wolfson-Royal Society Merit Award WM120086.

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