Regional activity within the human amygdala varies with season, mood and illuminance

Domain: neuroscience
Published: 2025-12-22
Editorial outcome: minor_revision

Abstract
The brain mechanisms through which changes in season and light exposure modulate mood may involve different nuclei of the amygdala. We aimed to test this hypothesis using 7 Tesla functional magnetic resonance imaging in 29 healthy young adults. We first considered time-of-year changes in activity that are related to the slow change in photoperiod. We find that the response to emotional stimuli of selected medial and superior nuclei of the amygdala peaked around the start of winter or increased with worse mood status. We further assessed how alternating short exposures to light of different illuminance acutely affected the regional activity of the amygdala. We show that the same areas showed a linear reduction of activity when exposed to increasing light illuminance, specifically when processing emotional stimuli. Importantly, the impact of light on part of these nuclei peaked around the start of summer or decreased with worse mood. These findings provide additional evidence that humans show seasonality and that, for mood, it involves parts of the amygdala. The results bring insights into the mechanisms that underlie the long-term and acute impact of light on mood and that may contribute to the benefits of light therapy in the treatment of mood disorders.

Introduction
Mood and the symptoms of many psychiatric disorders show seasonal variations1. These variations are driven in part by the shorter photoperiod taking place during the fall and winter seasons. Light therapy is established as a non-pharmacological treatment for seasonal affective disorder (SAD), including its subclinical forms and the relatively common winter blues2,3. In addition, it is also considered an efficient adjunct therapy for other non-seasonal psychiatric disorders2–4. Beyond light’s long-term seasonal effects, light can also trigger acute (or immediate) responses that can modulate emotional processing5. The daily repetition of these acute responses is plausibly contributing to the long-term impact of light on mood.

The mechanism of light’s influence on mood and emotional processing is most likely mediated through the light-sensitive pathway of the retina that detects environmental irradiance to regulate multiple biological functions not directly related to image formation6–9, often referred to as the non-image forming (NIF) pathway10. Intrinsically photosensitive retinal ganglion cells (ipRGCs) are the main photoreceptors of the NIF system and express the photopigment melanopsin, which is maximally sensitive to blue-wavelength light (~480 nm)11. IpRGCs combine their intrinsic photosensitivity with inputs from rods and cones12 to influence multiple brain targets with a maximal efficiency shifted towards blue-wavelength light13.

The amygdala coordinates emotional signals11,14. It is a complex structure that is divided into several nuclei – likely 13 nuclei in humans, though their exact classification is still debated15. Rodent data has shown that the amygdala receives direct inputs from ipRGCs and may play a key role in mediating light exposure behaviour11. In rodents, ipRGCs project to the central nucleus of the amygdala and this pathway was found to mediate anxiety-related behaviour after acute light exposure16,17. Lesions of the medial amygdala, to which ipRCGs also project, induced altered light-enhanced startle and open-field behaviour, suggesting that the effects of light on anxiety may also be mediated by the ipRGCs innervation of the medial amygdala18. In addition, increased blue wavelength light at night was further reported to increase the activity of the basolateral amygdala in rodents19. Short photoperiod in rodent models was further reported to modify fear conditioning behaviour while increasing the dendritic spine density of the neurons of the basolateral amygdala20. The translation of these acute effects of light in rodents to humans is not fully established.

In vivo neuroimaging studies on the topic in Humans are scarce and have reported contrasting results. Functional Magnetic Resonance Imaging (fMRI) showed that blue-wavelength (473 nm) light enhanced the responses to emotional stimuli in the amygdala as well as its crosstalk with the hypothalamus21. In contrast, in a resting-state fMRI paradigm, exposure to a warm polychromatic white light (~2800 K; 100 lux) suppressed amygdala activity compared to darkness (<1 lux) while its connectivity with the ventromedial prefrontal cortex was enhanced22. Furthermore, a dose-dependent effect of three weeks of bright-light therapy was reported to reduce threat-related reactivity of the amygdala and to increase its functional connectivity with medial prefrontal cortex, suggesting these changes may be part of the mechanism that mediates the beneficial effects of bright-light therapy23. The difference between studies may arise from differences in the light sources, from being engaged in a cognitive task vs. at rest and from considering shorter or longer impacts of light. They may also be due to the context-dependent response of the different nuclei of the amygdala that could not be addressed, given the data resolution.

Moving away from light’s impact, seasonal differences in the volume of subregions of the human amygdala were reported, with a peak in the summer. However, there was no association between mood measures and amygdala subregions volumes and photoperiod24.

Overall, animal models and human research suggest the amygdala is a candidate to mediate part of the impact of light exposure, photoperiod, and season have on mood. Given the known involvement of the amygdala nuclei in psychiatric disorders25–27, a precise understanding of the mechanisms at play may have implications beyond the healthy individuals and could help to improve the management of seasonal symptoms in psychiatric disorders, the use of light therapy and/or extend it to more brain disorders.

Here, we aimed to determine whether the regional activity of the amygdala varied with time-of-year and whether light exposure affected the activity of its different nuclei. We took advantage of the higher resolution of ultra-high-field (UHF) 7 Tesla (7T) fMRI to record the activity of the amygdala in healthy young adults (devoid of psychiatric disorders) exposed to the light of various illuminances while engaged in an auditory emotional task that was previously successfully used to show an influence of light on the activity of the amygdala21. We find that the regional activity of four subparts of the medial and superior amygdala shows an association with time-of-year or mood, and with the acute impact of illuminance, specifically when processing emotional stimuli. In addition, the impact of light on part of these nuclei varied with time-of-year and mood. These findings provide insights into the mechanisms that may underlie the impact of photoperiod and light exposure on mood and psychiatric symptoms.

Results
The brain activity of 29 healthy young participants (24y±3.1; 18 women) was recorded during an fMRI protocol conducted in Liège (Belgium) in the morning (scans 3–3.5 h after habitual wake-up time), and including three different cognitive tasks, one of which is an emotional auditory task and the sole focus of this analysis (Fig. 1, Supplementary Table S1). During the task, participants were alternatively maintained in darkness or exposed to different light consisting of a blue-enriched polychromatic white light of three different illuminance levels (37, 92, 190 melanopic Equivalent Daylight Illuminance – melEDI-lux) and a low illuminance monochromatic orange light (0.16 mel EDI lux).

Participants achieved 92±7.3% (mean±SD) button response, while the accuracy to the lure gender classification task28 was 79.7% ±9.9%, which is slightly lower than previous studies using the same task29. Critically, the reaction times were faster for the neutral (1203±183 ms) as compared with the emotional stimuli (1249±184 ms) (main effect, stimulus type; F1,28 =14.6, p=0.0007) (Fig. 2C). This is in line with previous literature5,29 and confirms that the emotional content of the stimuli was successful in triggering a behavioural response. Reaction times were not significantly affected by illuminance (main effect, illuminance; F4,221 =0.92, p=0.45) or by time-of-year (using the cosine value associated with day of the year over a year-long cycle, see methods; main effect, time-of-year; F1,25 =0.12, p=0.73) and there was no interaction between illuminance and stimulus type (F4,221 =0.35, p=0.85). This does not, in principle, preclude illuminance or time-of-year from affecting the underlying brain activity and ensures that neuroimaging findings related to light exposure are not merely the results of a change in reaction times. We further assessed variation in the mood of our participants, based on scores on the Beck Depression Inventory-II30, and found no association with time-of-year, age, sex and BMI (F1,20 <0.65, p>0.4). The questionnaire-based seasonality score31 was low in all participants and was not used in the analyses (Supplementary Table S1).

The activity of the medial-superior amygdala region is related to time-of-year and mood
We used a template that consistently divided the amygdala into 10 subparts32 (Fig. 2A) to extract the regional activity within the amygdala. The statistical analysis, which controlled for age, sex and BMI, first confirmed that the task was successful in triggering emotional responses in the amygdala with higher response to emotional than to neutral stimuli (GLMM; main effect, stimulus type, F1, 27.74 =9.93; p=0.0039, R²*=0.26; Fig. 2D) (Supplementary Table S3). This is supported by the visualization of the whole brain group analysis (p <0.001 uncorrected) which yielded local positive peaks bilaterally in the amygdala (mostly over the medial and superior subparts – ~subparts 1, 3, 5 and 10) indicating that our finding does not arise from a nearby “leaking” activation (Fig. 2B). As suggested by this visualization, responses significantly differed across amygdala subparts (GLMM; main effect, subpart, F9, 499.9 =2.13; p=0.0256, R²*=0.04; Fig. 2E), though not in interaction with stimulus type (GLMM; interaction, stimulus type by subpart: F9, 499.9 =0.56, p=0.85) (Supplementary Table S3). Post hoc tests indicated that three subparts did not respond significantly to the auditory stimulations (p>0.05, Supplementary Table S3; Intermediate and dorsal basolateral nucleus-subpart 2, blue on Fig. 2A, Central nucleus-subpart 4, purple on Fig. 2A, and Ventral basolateral nucleus and Paralaminar nucleus - subpart 6, red on Fig. 2A). The remaining of the analyses only considered the 7 other subparts.

Although low mood was among our exclusion criteria, it varied across our participants, which were recorded at all times of the year (Winter: 4; Spring: 10; Summer: 6; Fall: 9; Supplementary Table S1). We therefore asked whether the activity of the amygdala subparts was associated with the time-of-year and mood. The statistical analysis provided a positive answer to the question (GLMM; main effect, time of year: F1, 17.85 =12.6, p=0.0023, R²*=0.41; main effect, mood: F1, 17.8 =17.5, p=0.0006, R²*=0.495) and further revealed that the association with time-of-year and mood differed across amygdala subparts (GLMM; three-way interaction, subpart by time of year by mood: F6, 272.4 =4.03, p=0.0007, R²*=0.08; interaction, subpart by time of year: F6, 272.6 =4.66, p=0.0002, R²*=0.09; interaction, subpart by mood: F6, 272.4 =2.99, p=0.0076, R²*=0.062) (Supplementary Table S4.a).

Considering first time-of-year, post hoc tests showed that the activity of the basomedial nucleus (BMN; t=3.54, p corrected =0.004, subpart 3, dark blue on Fig. 2A) and of the anterior amygdaloid area (AAA; t=4.96, p corrected <0.0001, subpart 9, light green on Fig. 2A) significantly varied with time of year (both subparts were also different from part of the other subparts in their association with time-of-year) (Fig. 2F, G, Supplementary Table S4.b,c). For both areas, responses were stronger around the start of winter (Fig. 2H, I). Post hoc tests then revealed that worse mood was significantly associated with a higher activity of medial and cortical nuclei (MCN; t=4.28, p corrected =0.0002, subpart 5, violet on Fig. 2A) and amygdala transition areas (ATA, t=4.64, p corrected <0.0001, subpart 7, orange on Fig. 2A) (both subparts were also different from part of the other subparts in their association with time of year) (Fig. 2J, K, Supplementary Table S4.d,e). A statistical trend for an association with mood was further detected for the AAA (t=2.6, p uncorrected =0.011, p corrected =0.061, subpart 9, light green on Fig. 2A) and the intercalated nuclei (t=2.46, p uncorrected =0.016, p corrected =0.089, subpart 10, green on Fig. 2A) (Fig. 2L, M, Supplementary Table S4.d). Overall, the results suggest the activity in subparts 3 and 9 varied significantly with the time-of-year, while activity in subparts 5 and 7 varied significantly with mood. Thus, activity in selected nuclei comprising the amygdala’s medial and superior region is either related to time-of-year or mood.

Light illuminance linearly decreases the activity of the medial-superior amygdala
The next statistical analysis included the impact of illuminance variation on the activity of the amygdala subparts, still controlling for age, sex and BMI. It revealed that the illuminance affected differently the activity of the different subparts (GLMM; main effect, subpart: F6, 331.4 =7.05, p<0.0001, R²*=0.11) (Fig. 3B, Supplementary Table S5). The impact of light further depended on the stimulus types (GLMM; main effect, stimulus type: F1, 27.2 =6.32, p=0.0181, R²*=0.19), while there was no interaction between stimulus type and subparts (GLMM; interaction, subpart by stimulus type: F6, 331.4 =1.42, p=0.21) (Supplementary Table S5).

Post hoc analyses found that the BMN (p corrected =0.0001, subpart 3, dark blue, Fig. 2A), medial and cortical nuclei (p corrected <0.0001, subpart 5, violet, Fig. 2A), ATA (p corrected =0.0005, subpart 7, orange, Fig. 2A) and the AAA (p corrected =0.0002, subpart 9, light green on Fig. 2A) were significantly affected by illuminance changes, and differently so compared to many other amygdala subparts (Supplementary Table S5). Visualization of the whole brain group analysis (p <0.001 uncorrected) yielded local negative peaks bilaterally in the amygdala when comparing emotional and neutral items, mostly over the left subparts 3, 5 and 7 and the right subparts 3, 5 and 9, again supporting the finding and indicating that it does not arise from a nearby “leaking” activation (Fig. 3A). When investigating in more detail the regional impact of light in the 4 subparts, results are compatible with a linear decrease in activity with increasing illuminance in the 4 subparts (Fig. 3C, D; Supplementary Table S6). The regions that were related to time-of-year and/or mood independently of illuminance are therefore also linearly affected by illuminance variations when processing emotional stimulations.

The regional impact of light on the amygdala depends on time-of-year and mood
In the final steps, we asked whether the impact of illuminance on the different amygdala subparts varied with the time of the year and mood. The statistical analysis (controlling for age, sex and BMI), showed that the association with time-of-year and mood differed across amygdala subparts (GLMM; interaction, subpart by time-of-year: F6, 272 =2.94, p=0.0085, R²*=0.06; interaction, subpart by mood: F6, 272 =2.24, p=0.04, R²*=0.05; three-way interaction, subpart by time-of-year by mood: F6, 272 =0.87, p=0.52) (Supplementary Table S7). Post hoc tests did not point to a subpart significantly driving the associations but rather yielded statistical trends suggesting that the impact of illuminance varied with time of year within the ATA (t=−1.73, p uncorrected =0.096, p corrected =0.32, the subpart differed significantly from several other subparts) while it depended on mood in the AAA (t=2.21, p uncorrected =0.033, p corrected =0.12, the subpart differed significantly from several other subparts) (Supplementary Table S7). Visualisation of the data indicates that light impact on the ATA is maximum around summer (Fig. 3E, F), while the worse mood is associated with a larger impact of illuminance on the AAA (Fig. 3G).

Methods
This paper arises from a larger study and only describes the methods relevant to the emotional task. Refer to the following papers44,59–61 for more information about the larger study. The protocol was approved by the Ethics Committee of the Faculty of Medicine at the University of Liège. Participants gave their written informed consent and received monetary compensation. Data acquisitions took place in Liège, Belgium, between December 2020 and August 2023. Additional methodological details are provided as supplementary methods. The processed data supporting the results and analysis script included in this manuscript are publicly available via the following open repository: https://gitlab.uliege.be/CyclotronResearchCentre/Public/fasst/amygdala_7t_light. Access to the raw data is available under request to the corresponding author (GV).

Participants
Thirty-six healthy participants (23.9y±2.8; 23 women; all Caucasian) took part in the study. They were recruited though a local GDPR complain database of potentially volunteers and through local internet advertisement. Exclusion criteria were assessed through questionnaires and a semi-structured interview and were as follows: history of psychiatric and neurological disorders, sleep disorders, use of psychoactive drugs or addiction; history of ophthalmic disorders or auditory impairments; colour blindness; night shift work during the last year or recent trans-meridian travel during the last 2 months; excessive caffeine (>4 caffeine units/day) or alcohol consumption (>14 alcohol units/week); medication affecting the central nervous system; smoking; pregnancy or breastfeeding (women). Their scores on the 21-item Beck Anxiety Inventory62 and the Beck Depression Inventory-II30 were minimal or mild (<18) and minimal (<14), respectively. Questionnaires further assessed chronotype with the Horne-Östberg questionnaire63 and seasonality with the Seasonal Pattern Assessment Questionnaire31, but the latter two questionnaires were not used for the inclusion of the participants. Participants refrained from caffeinated and alcohol-containing beverages and excessive exercise for at least 3 days before the experiment. The scores on the Beck Depression Inventory-II30 were used to assess the influence of mood on the regional activity of the amygdala.

Seven datasets were missing or corrupted such that 29 participants (24y±3.1; 18 women; Supplementary Table 1) were included in the present analyses (three participants did not complete the entire task because of scan technical issue interrupting acquisition or subject wanted to exit the scanner before the end of the session, while quality check revealing an important ghosting artefact for 2 datasets and important mismatch in the coregistration of subject space to the MNI space for 2 datasets which could not be resolved following multiple attempts at the time of submitting the manuscript). At least one valid data set was acquired each month of the year and participants are relatively well spread across the 4 seasons (Winter: 4; Spring: 10; Summer: 6; Fall: 9).

Protocol
Structural brain images were acquired 1 to 2 weeks before the experiment, during a visit which served as habituation to the experimental conditions. Participants then followed a loose sleep-wake schedule (±1 h from habitual sleep/wake-up time) for 7 days, to maintain realistic entrained life conditions and avoid excessive sleep restriction (verified using wrist actigraphy-AX3, Axivity, UK-and sleep diaries). Participants arrived at the laboratory 1.5 after their habitual wake time. To standardise participants’ recent light history, they were exposed to 5 min of bright white light (1000 lux; with the chin on a chin-rest, ~15 cm away from a plastic diffuser in front of a polychromatic halogen light bulb) and were then maintained in dim light (<10 lux) for 45 min (bright and dim light levels were controlled for each participant at eye level). During the dim light period, participants were given instructions about the fMRI cognitive tasks and completed practice tasks on a luminance-controlled laptop (<10 lux). The fMRI sessions consisted of an executive task (25 min), an attentional task (15 min), and an emotional task (20 min), which is the only task included in the present paper. All participants included in this analysis were therefore scanned ~3 h after habitual wake-up time and between 8 am and 1:15 pm [Fig. 1A]. Participants always completed the executive task first, as it was the most demanding task. The order of the following two tasks was counterbalanced across participants (45% completed the emotional task before the attentional task). Hence, any bias arising from the relative position of the task could not be truly assessed in our analyses but was controlled for. While in the MR-scanner, participants were asked to keep their eyes open and try not to blink too much during the cognitive tasks. An eye-tracking system (EyeLink 1000 Plus, SR Research, Ottawa, Canada) was monitored for proper eye-opening during all data acquisitions. The MRI environment was kept in dim light (<10 lux) during the MRI scan.

Light exposure
An MRI-compatible light system designed-in-lab was developed to ensure relatively uniform and indirect illumination of participants’ eyes whilst in the MRI scanner. An 8-m long MRI-compatible dual-branched optic fibre (1-inch diameter, Setra Systems, MA, USA) transmitted light from a light box (SugarCUBE, Ushio America, CA, USA), that was stored in the MRI control room. The dual end of the optic fibre was attached to a light stand fitted at the back of the MRI coil, allowing for reproducible fixation and orientation of the optic fibre ends. The dual branches illuminated the inner walls of the head coil to indirectly illuminate the participants’ eyes. A filter wheel (Spectral Products, AB300, NM, USA) and optical fibre filters to switch between a narrowband 589 nm filter (full width at half maximum: 10 nm) and a UV long-bypass filter (433–1650 nm) filter) and alternate between a monochromatic orange light and the full output of the polychromatic blue-enriched LEDs (6500 K) (Fig. 1B and Supplementary Table S2 for in-detail light characteristics). The spectra of the lights were assessed at the level of the end of the optic fibre (AvaSpec-2048, Avantes, The Netherlands). Illuminance could not be measured directly in the magnet, but the light source was calibrated prior to the experiment (840-C power meter, Newport, Irvine, CA).

Blue-enriched light illuminances were set according to the technical characteristics of the light source and to keep the overall photon flux similar to prior 3T MRI studies of our team using the same emotional task (between ~1012 and 1014 ph/cm²/s)21. The orange light was introduced as a control visual stimulation for potential secondary whole-brain analyses. For the present region of interest analyses, we discarded colour differences between the light conditions and only considered illuminance as indexed by melEDI lux, constituting a limitation of our study.

Emotional task
The task consisted of gender discrimination of auditory vocalisations [Fig. 2C]64. Participants were asked to use a keypad to indicate what they believed the gender of the person pronouncing each token was. The gender classification was a lure task: its purpose was to trigger an emotional response, as participants were not told that 50% of the stimuli were pronounced with angry prosodies. The 240 auditory stimuli were pronounced by professional actors (50% women) and consisted of three meaningless words (“goster”, “niuvenci”, “figotleich”). The stimuli were expressed in either an angry or neutral prosody (validated by behavioural assessments64 and in previous experiments28,29). During each 30–40-s light block, four angry prosody stimuli and four neutral prosody stimuli were presented in a pseudo random order and delivered every 3–5 s. A total of 160 distinct voice stimuli (50% angry; 50% neutral) were distributed across the four light conditions. The darkness periods separating light blocks contained two angry and two neutral stimuli (80 stimuli in total). The emotional task (together with light exposure changes) was programmed with Opensesame (3.2.8)65. Participants heard the auditory stimuli through MR-compatible headphones (Sensimetrics, Malden, MA) and the volume was set by the participant before starting the tasks to ensure a good auditory perception. Participants used an MRI-compatible keypad to respond to task items (Current Designs, Philadelphia, PA). The instruction was to privilege accuracy over rapidity when responding. The auditory stimuli used in the task were matched for the duration (750 ms) and mean acoustic energy to avoid loudness effects.

Data acquisition
MRI data were acquired in a 7T MAGNETOM Terra MR (Siemens Healthineers, Erlangen, Germany) with a 32-channel receive and 1-channel transmit head coil (Nova Medical, Wilmington, MA, USA). Dielectric pads (Multiwave Imaging, Marseille, France) were placed between the subject’s head and receiver coil to homogenize the magnetic field of Radio Frequency (RF) pulses. The multi-band Gradient-Recalled Echo - Echo-Planar Imaging (GRE-EPI) sequence with axial slice orientation was set as follows: TR=2340 ms, TE=24 ms, FA=90°, no interslice gap, in-plane FoV=224 mm×224 mm, matrix size=160×160×86, voxel size=(1.4×1.4×1.4) mm3). To avoid saturation effects, the first three scans were discarded. To correct for physiological noise in the fMRI data the participants’ pulse and respiration movements were recorded using a pulse oximeter and a breathing belt (Siemens Healthineers). Following the fMRI acquisition a 2D GRE field mapping sequence to assess B0 magnetic field inhomogeneities with the following parameters: TR=5.2 ms, TEs=2.26 ms and 3.28 ms, FA=15°, bandwidth=737 Hz/pixel, matrix size=96×128, 96 axial slices, voxel size=(2×2×2) mm3, acquisition time=1:38 min. The Magnetization-Prepared with 2 RApid Gradient Echoes (MP2RAGE) sequence was set as follows: TR=4300 ms, TE=1.98 ms, FA=5°/6°, TI=940 ms/2830 ms, bandwidth=240 Hz, matrix size=256×256, 224 axial slices, acceleration factor=3, voxel size=(0.75×0.75×0.75) mm3.

Data pre-processing
For the MP2RAGE images, the background noise was removed using an extension (https://github.com/benoitberanger/mp2rage) of Statistical Parametric Mapping 12 (SPM12; https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) under Matlab R2019 (MathWorks, Natick, Massachusetts)66. Then the images were reoriented using the ‘spm_auto_reorient’ function (https://github.com/CyclotronResearchCentre/spm_auto_reorient) and corrected for intensity non-uniformity using the bias correction method implemented in the SPM12 “unified segmentation” tool67. To ensure optimal co-registration, brain extraction was done using SynthStrip68 in Freesurfer (http://surfer.nmr.mgh.harvard.edu/). The brain-extracted T1-images were used to create a T1-weighted group template using Advanced Normalization Tools (ANTs, http://stnava.github.io/ANTs/) before normalization to the Montreal Neurological Institute (MNI) space using ANTs (1 mm³ voxel; MNI152 template).

For the EPI images, autoreorientation was applied on the images first. Then, voxel-displacement maps were computed from the phase and magnitude images associated with B0 map acquisition, using the SPM fieldmap toolbox. To correct for head motion and static and dynamic susceptibility-induced variance, “Realign & Unwarp” of SPM12 was then applied to the EPI images. The realigned and distortion-corrected EPI images then underwent brain extraction using the SynthStrip and then the final images were smoothed with a Gaussian kernel characterised by a full width at half maximum of 3 mm.

Data analysis
The brain-extracted anatomical T1-images were used to create a T1-weighted group template using Advanced Normalization Tools (ANTs, http://stnava.github.io/ANTs/) before normalization to the Montreal Neurological Institute (MNI) space using ANTs (1 mm³ voxel; MNI152 template). For each subject, the first level analysis of fMRI data was performed in the native space. Before the group analysis, statistical maps were first transferred to the group template space and then the MNI space (1×1×1 mm3 resolution; MNI152 template) using ANTs. The subject level GLM included a high-pass filter with a 256 s cut-off to remove low-frequency drifts as well as movement and physiological parameters (cardiac, and respiration), which were computed with the PhysIO Toolbox (Translational Neuromodeling Unit, ETH Zurich, Switzerland)69. All these additional regressors were included as covariates of no interest.

Our statistical analyses consisted of an a priori region of interest focused on the activity of the amygdala which was estimated as part of a whole brain general linear mixed model computed with SPM12. The auditory stimuli were modelled as stick functions convolved with a canonical hemodynamic response function. There were two parts to the whole-brain analysis. In the main analysis, we assessed brain responses during the task and how they were modulated by overall changes in illuminance level. The two regressors of task events (neutral, angry) were each accompanied by a parametric modulation corresponding to the light melanopic illuminance level (0, 0.16, 37, 92, 190 mel EDI). The contrasts of interest consisted of the main effect of the task (emotional vs. neutral stimuli) and their parametric modulations (emotional vs. neutral stimuli x illuminance). In the next whole-brain analysis, we assessed the responses to the stimuli under each light condition. Separate regressors modelled each task’s event type under each light condition. The contrasts of interest consisted of the main effects of each regressor. Whole-brain group results over the entire sample were used for visualisation purposes only.

We used an amygdala atlas to segment the region into 10 subparts (bihemispheric) in the MNI subject space32, corresponding to nuclei or nucleus groups (Fig. 2.A). The REX Toolbox (https://web.mit.edu/swg/software.htm) was used to extract the activity estimates (betas) from contrast of interest in each amygdala subpart70. In the first analysis, this yielded 1 activity estimate per contrast, per stimulus type and per amygdala subpart and in the second analysis, we obtained 5 activity estimates per stimulus and subpart.

Statistics
Statistical analyses of the activity of each subpart were computed in SAS 9.4 (SAS Institute, NC, USA) and consisted of (2-sided) Generalised Linear Mixed Models (GLMM) with the subject as a random factor (intercept and slope) and were adjusted for the dependent variable distribution. As main the statistical analysis included all subparts, light conditions and stimulus types in a single model (when relevant), the significance threshold was not corrected for multiple comparisons and was set at p<0.05. Direct post hoc of the main analyses were corrected for multiple comparisons using a simulated adjustment. Activity estimates were considered outliers if >±3 SDs across emotional stimuli and light level and were removed.

The two main analyses included the average activity estimates to emotional and neutral stimuli i) independent of light and ii) modulated by illuminance as a dependent variable and the amygdala subparts and stimulus type (neutral/angry; as repeated measure with compound symmetry correlation), together with their interaction and age and sex as covariates. BMI was also part of the covariates because it could influence brain activity, including in regions involved in reward, which is related to emotional processing71. The first main analysis indicated that 3 areas did not respond to the task, these were therefore discarded from all further analyses related to time of year, mood and light acute impact.

To test for variations related to time of year and mood, both models were recomputed respectively with a time of year covariate consisting of the cosine value of the day of the year expressed in degrees of the 360-day year (365 day = 360°; 1 day = 0.986°; December 21st = 0°) and the score of Beck Depression Inventory-II30. Three-way interaction terms were included in the model [subpart x time of year x mood] as well as the three simple interactions composing the three-way interaction.

The main analysis focusing on light exposure impact used a linear parametric modulation of the changes in activity with increasing illuminance to grasp the overall impact of illuminance change. Since the latter may have missed non-linear changes in activity with changes in illuminance, a second GLMM included the four amygdala subparts that were showing an impact of overall illuminance change. The GLMM included the activity estimates of the four subparts as the dependent variable and amygdala subpart, stimulus type (neutral/angry; as repeated measure with compound symmetry correlation) and illuminance (0, 0.16, 37, 92, 190 mel EDI lux; as the repeated measures nested within stimulus type-compound symmetry correlation), together with age, sex and BMI as covariates. A three-way interaction term was included in the model [subpart x illuminance x stimulus type] as well as the three simple interactions composing the three-way interaction.

A final set of exploratory GLMMs reported in the discussion section included performance metrics as dependent variables (reaction time – ms) and included the activity of each of the four amygdala subparts that were showing an impact of overall illuminance change in separated model together with stimulus type and illuminance (repeated measures nested within stimulus type - compound symmetry correlation), together with age, sex and BMI as covariates.

Optimal sensitivity and power analyses in GLMMs remain under investigation (e.g. ref. 72). We nevertheless computed a prior sensitivity analysis to get an indication of the minimum detectable effect size in our main analyses, given our sample size. According to G*Power 3 (version 3.1.9.7)73, taking into account a power of 0.8, an error rate α of 0.05, and a sample of 29 allowed us to detect large effect sizes r>0.53 (two-sided; absolute values; CI: 0.2–0.75; R²>0.28, R² CI: 0.04–0.56) within a linear multiple regression framework including two tested predictors (illuminance effect, amygdala subpart) and up to six covariates (stimulus type, age, sex, BMI, season and affective status).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, the authors used 7 Tesla fMRI to test whether activation level in the human amygdala during emotional vs.\nneutral auditory stimuli vary with time of year, affective state, and illuminance level. The use of a strong magnetic field\nenabled characterizing the effects in 10 subregions of the amygdala.\nThe sample of 29 participants allowed the detection of medium effect sizes, and thus, it is adequate. While the authors\ncontrolled for sex and age, the age range was quite narrow (24y ±3.1) and men were underrepresented (18 women, 11\nmen). Additionally, the distribution of participants across seasons was not uniform (Winter, 4; Spring, 10; Summer, 6; Fall: 9).\nCollecting data from a larger age range and from equal representation of women and men would have been ideal, but\nconsidering the difficulty and complexity of the experiment, such limitations are well accepted in the field.\nThe study shows that activity in subregions 3 (basomedial nucleus) and 9 (anterior amygdaloid area) increased as day\nlength decreased, while activity in subregions 5 (medial and cortical nuclei) and 7 (amygdala transition area) increased as\naffective state deteriorated. Thus, activity in selected nuclei of the amygdala is either related to season or affective state.\nActivity in the four subparts that varied with season or affective state (3, 5, 7, and 9), negatively correlated with acutely-\nmodulated illuminance level, when processing emotional stimuli but not neutral stimuli. Overall, these findings the possibility\nthat photoperiod and acute light exposure might influence emotion through modulation of selected amygdalar nuclei. As\nsuch, the study could pave the way toward better understanding of the benefits of light therapy in the treatment of affective\ndisorders.\nMajor comments:\nFigure 1 – Since the order of the emotional and attentional tasks was random, how did the relative position of the emotional\ntask interact with the effect of time of year, affective state and illuminance on amygdala activation?\nLine 162 – It is not clear how df of ~500 was achieved. It would be useful to describe the number of blocks and repetitions\nthat led to the df. The sampling unit should probably be the number of participants (29 people), and thus df should not\nexceed 500. If this expectation is wrong, please explain why. This comment applies to all other instances where similarly\nlarge df is reported.\nFigures 1 and 2 – Considering the typically modest variation of activity with of season, affective state, and illuminance level,\nit would be useful to specify the effect size of the main effect GLMM and post-hoc tests.\nThe manuscript would benefit tremendously from thorough editing.\nMinor comments:\nTitle – Would probably benefit from being more informative. For example, “Activity in selective human amygdala subregions\nvaries with season, emotion, and illuminance”.\nLine 48 – “by the reduced shorter photoperiod” – not clear.\nLine 108 – “in the morning” – Specify the time range when fMRI was perfromed.\nLine 109 – It is not clear why only the ‘emotional task’ out of the three tasks performed was analyzed?\nLine 112 – The three illuminance levels (37, 92, 190 melanopic EDI) are low compared to about 10,000 lux melanopic EDI\nor more around noon outdoors. Since these low levels induced changes in amygdala activation, daylight would likely evoke\nmuch larger changes.\nFigure 1, caption – The author should state that the ‘prior light history standardization’ consisted of 5-min 1000 lux light and\nthe following 45-min under dim light.\nFigure 1H,I – The authors should describe how the sine function was fitted to data, and report measures of the goodness of\nfit.\nLine 155 – It is not clear why BMI should be controlled for. Is there any evidence for variation in brain activation according to\nBMI?\nLine 216 – Euro sign instead of (E).\nFigure 2, panels F-M – It would be useful to indicate the subpart number on each figure panel.\nFigure 2, Panels H-I – In addition to ‘day of the year’ it would be useful to include another x-axis indicating the months. This\nwill make it easier to appreciate the claim in line 185 “responses were stronger during winter”.\nSuppl. Table S4 – This table is very large, comprising multiple sections. As the table is being referred to many times, it is\nhard to identify the relevant table section. Either refer to specific sections of the table or divide the table into several smaller\ntables.\nLines 185-188 – Fig. 2A is referenced but, if I understand correctly, the results appear in panels J and K.\nLines 193-194 – “Overall, the results suggest that the activity of the nuclei composing the medial and superior region of the\namygdala (encompassing 4 to 5 subparts of our template) is related to season and/or affective state.” This claim is not\nsupported by the results. A more accurate claim would be: “Activity in subparts 3 and 9 varied significantly with season,\nwhile activity in subparts 5 and 7 varied significantly with affective state. Thus, activity in selected nuclei comprising the\namygdala’s medial and superior region is either related to season or affective state”.\nLine 300 – Again, the conclusions should be stated more carefully. “We found that the activity of medial and superior nuclei\nof the amygdala was related…” should be “We found that the activity in selected medial and superior nuclei of the amygdala\nwas related…”.\nLines 393-395 – The authors correctly suggest that “The cross-talk with the prefrontal cortex is also of interest given the\nchanges in functional connectivity previously reported following light therapy when considering the amygdala as whole.” In\naddition, given the 2022 report of the effect of light on activity in the human prefrontal cortex, at least some of the illuminance-\nevoked activity modulations observed in the amygdala might be driven by input from the prefrontal cortex, and vice versa,\nsome of the sensitivity observed in the prefrontal cortex might reflect input from the amygdala. This idea is supported by the\nevidence of effect of light on prefrontal cortex-amygdala connectivity, as indicated in the Introduction section.\nThe Discussion section is well written, addressing the main results, specifying limitations, and raising interesting\nimplications. It could probably be shortened, though.\nLines 663-670 – In the description of prior sensitivity analysis, there is no mention of the predictors: seasons and affective\nstate.", "reviewer_2": "The authors present a study investigating the impacts of varying light intensities, season, and affective state (depressive\nsymptoms) on regional activity in the amygdala using high resolution MRI. The study is well presented, and the findings\ninteresting. However, they are at times overstated given the relatively weak associations observed. Additionally, some\naspects of the method could be clearer. Specific comments below:\nAbstract:\n1. The authors could be more specific here about the continuous nature of assessments across time of year (as opposed to\ndistinct groups in each season)\n2. The results could be clearer as to the direction of the affect/season related results i.e., “the impact of light on part of these\nnuclei varied with season and affective state” could be more specific.\nIntroduction:\n3. Minor typo/grammatical error “reduced shorter photoperiod” is repetitive\n4. The paragraph starting at line 77 is a little hard to follow. Ref 21 found increased amygdala-prefrontal connectivity during\nlight exposure, not decreased (aligned with findings from ref 22 post light therapy). It would be clearer to present the primary\nfindings of the four studies described here before speculating on potential reasons for apparent differences (i.e., suppression\nof activity by light during rest, and reduced activity following light therapy, but not during the completion of an emotional\ntask). It could be clearer why the presence of an emotional task would explain these differences, though this may be more\nappropriate for the discussion where it can be explored in the context of the current findings.\nResults:\n5. Although full methods are presented later, it would be helpful to specify the time frame of ‘morning’ here.\n6. Minor typo in the caption of Figure 1 – ‘lose’ sleep-wake schedule (loose?). The prior light history standardisation is also\nnot clear, the text sounds more like it Is light exposure across those 7 days, when in fact it is only the immediate light\nhistory prior to the scan.\n7. It would be helpful to indicate local season on the graphs which show data by day of year (and/or actual changes in\nphotoperiod).\n8. The way in which affective state was measured should be noted here.\n9. Many of the associations shown in the bottom section of Figure 2 were very weak. Given that some did not survive\ncorrection, the authors should show/state this more clearly on the figure.\nDiscussion:\n10. “Affective state varies with seasons in a large portion of the healthy population” – given depression scores (the measure\nof ‘affective state’ used in this study) did not vary by season in this sample, there may be a better way to introduce the\nsummary of your findings.\n11. The discussion overall is well reasoned and written. However, at times the results, particularly those pertaining to\naffective state and season are overstated given the relatively weak relationships presented.\nMethods:\n12. Minor typo/clarification needed re: “Subject wiling to exit the scanner” – perhaps supposed to be wishing to exit?\n13. Additional information regarding the standardisation of prior light would be helpful – i.e., are these measures the\nmaximal/minimal level in the room, how was 1000 lux delivered, using a light therapy device? Additional descriptions of the\ngeometric space would be helpful to understand the actual retinal light exposure. Additionally, specification of the lighting in\nthe MRI room should be included.\n14. Can the authors speak to their decision to use the BDI as a marker of ‘affective state’? They also had BAI available,\nwhich is arguably also relevant to the regions of interest, and appears to show a greater range of scores, but another\nmeasure which may be more sensitive to normal mood variation in healthy persons would have likely been more appropriate\nthan either. I am concerned that the limited range of BDI scores may not meaningfully reflect differences in ‘affective state’ in\nthis population. It would be more appropriate to refer to this measure as depressive symptoms, rather than affective state,\nthroughout the paper. At a minimum the actual range of scores and mean/SD should be reported here in the methods, but\nideally the limitations of using this measure in an otherwise healthy population should also be discussed in the paper."}, "author_response": "The sample of 29 participants allowed the detection of medium effect sizes, and thus, it\nis adequate. While the authors controlled for sex and age, the age range was quite narrow\n(24y ±3.1) and men were underrepresented (18 women, 11 men). Additionally, the\ndistribution of participants across seasons was not uniform (Winter, 4; Spring, 10;\nSummer, 6; Fall: 9). Collecting data from a larger age range and from equal representation\nof women and men would have been ideal, but considering the difficulty and complexity\nof the experiment, such limitations are well accepted in the field.\nOverall, these findings the possibility that photoperiod and acute light exposure might\ninfluence emotion through modulation of selected amygdalar nuclei. As such, the study\ncould pave the way toward better understanding of the benefits of light therapy in the\ntreatment of affective disorders.\nWe would like to thank Reviewer 1 for their detailed evaluation of our manuscript and for\nhighlighting the strengths and limitations of the research as well as underlying its interest.\nWe believe the comments helped improving significantly the quality of our paper.\nMajor comments:\n• Figure 1 – Since the order of the emotional and attentional tasks was random, how\ndid the relative position of the emotional task interact with the effect of time of\nyear, affective state and illuminance on amygdala activation?\nWe cannot truly assess how the relative position of the emotional task interacts with the\neffect of time-of-year, mood* and illuminance as this would mean adding a factor in the\nanalyses and splitting the sample in two. However, as indicated in the method section\n(page 22/23), the order of the emotional and attentional task was counterbalanced\nacross participants so that approximately half of the sample complete the emotional first\nand the other half second (% completed the emotional task before the attentional task).\nHence, any potential bias associated to the relative positive was controlled for.\nWe added this aspect to the text (line 523-527: ‘Participants always completed the\nexecutive task first as it was the most demanding task. The order of the following two\ntasks was counterbalanced across participants (45% completed the emotional task\nbefore the attentional task). Hence, any bias arising from the relative position of the\ntask could not be truly assessed in our analyses but was controlled for.’\n* Please note, that following a comment of reviewer 2, we changed the terms “affective\nstate” to “mood”.\n1/15\n• Line 162 – It is not clear how df of ~500 was achieved. It would be useful to\ndescribe the number of blocks and repetitions that led to the df. The sampling unit\nshould probably be the number of participants (29 people), and thus df should not\nexceed 500. If this expectation is wrong, please explain why. This comment\napplies to all other instances where similarly large df is reported.\nWe report both the standard DF and those associated with the error term, the latter\ndepending on the total number of observations (minus the number of model\nparameters). Having 29 subjects x 10 amygdala subparts x 2 task conditions can\ntherefore lead to error DF close to 500 following Kenward-Roger estimation.\n• Figures 1 and 2 – Considering the typically modest variation of activity with of\nseason, affective state, and illuminance level, it would be useful to specify the\neffect size of the main effect GLMM and post-hoc tests.\nPartial effect size (R²*) of the significant association are/were reported in the main text\nand in the supplementary tables.\n• The manuscript would benefit tremendously from thorough editing.\nWe have gone through and edited the manuscript and hope to have made it clearer.\nMinor comments:\n• Title – Would probably benefit from being more informative. For example, “Activity\nin selective human amygdala subregions varies with season, emotion, and\nilluminance”.\nWe thank the reviewer for their suggestion. We modified the title to “Regional activity\nwithin the human amygdala varies with season, mood and illuminance”.\n• Line 48 – “by the reduced shorter photoperiod” – not clear.\nWe have modified the text accordingly, ‘driven in part by the shorter photoperiod taking\nplace during the fall and winter seasons’ (line 53).\n• Line 108 – “in the morning” – Specify the time range when fMRI was performed.\nWe now specify the following:\n2/15\n‘The brain activity of 29 healthy young participants (24y ±3.1; 18 women) was recorded\nduring an fMRI protocol conducted in Liège (Belgium) in the morning (scans 3 to 3.5hrs\nafter habitual wake-up time),…’ (line 119-120);\n‘Participants arrived at the laboratory 1.5h after wake-up time’ (line 129 – figure 1\ncaption & line 513 - methods);\n‘In total, 29 participants (24y ±3.1; 18 women) performed an executive (always first), an\nemotional and an attentional task (pseudo-randomly 2nd or 3rd, purple arrow; meaning\nthat the fMRI recording of the emotional task was completed 3 to 3.5h after wake-up\ntime).’ (line 133-134, caption figure 1);\n‘All participants included in this analysis were therefore scanned ~3h after habitual\nwake-up time and between 8 am and 1:15 pm [Fig.1A].’ (line 521-523, methods)\n• Line 109 – It is not clear why only the ‘emotional task’ out of the three tasks\nperformed was analyzed?\nThe emotional task was the sole focus of the paper as we wanted to investigate the\nimpact of light, season, and mood, on emotional processing in the amygdala, which is a\nkey region for this aspect of brain function. In addition, the gender discrimination of\nauditory vocalisations task we used has previously been shown to show that the\nresponses of the amygdala during the task were influenced by light1,2. We have previously\nanalysed the executive and attentional task (see the following papers3–6). We have added\nthe following to the text:\n‘We took advantage of the higher resolution of ultra-high-field (UHF) 7 Tesla (7T) fMRI to\nrecord the activity of the amygdala in healthy young adults (devoid of psychiatric\ndisorders) exposed to the light of various illuminances while engaged in an auditory\nemotional task which was previously successfully used to show an influence of light\non the activity of the amygdala7.’ line 105-109.\n‘This paper arises from a larger study and only describes the methods relevant to the\nemotional task. A full description of the methods has been previously described.’\n(line 476-477).\n• Line 112 – The three illuminance levels (37, 92, 190 melanopic EDI) are low\ncompared to about 10,000 lux melanopic EDI or more around noon outdoors.\nSince these low levels induced changes in amygdala activation, daylight would\nlikely evoke much larger changes.\nThe blue-enriched light levels for the study were set according to the technical\ncharacteristics of the light source, to avoid too much glare effect following the adaptation\n3/15\nto dim light, and to keep the overall photon flux similar to prior 3T MRI studies of our team\n(between ~1012 and 1014 ph/cm²/s)7,8. The orange light was introduced as a control visual\nstimulation for potential secondary whole-brain analyses. For the present region of\ninterest analyses, we discarded colour differences between the light conditions and only\nconsidered illuminance as indexed by mel EDI lux. As indicated in the text, this\nconstitutes a limitation of our study as it does not allow attributing the findings to a\nparticular photoreceptor class. Higher light levels, such as 10,000 lux melanopic EDI or\nmore around noon outdoors, could evoke larger changes, although photoreceptor\nadaptation (driven in part by ipRGCs) may mitigate the change in absolute illuminance.\nThe text has been modified as follows to take the latter aspect into account (line 456-\n460): “While compatible with usual indoor levels, the light we administered was far from\noutdoor levels, and we can only speculate that the effects we report are similar under\nthese outdoor conditions. The effects of outdoor light conditions may be stronger, but the\nlight adaptation processes of the retina, in part driven by ipRGCs, may mitigate\ndifferences in absolute illuminance9 .”\n• Figure 1, caption – The author should state that the ‘prior light history\nstandardization’ consisted of 5-min 1000 lux light and the following 45-min under\ndim light.\nWe have edited the figure 1 caption and included the following ‘The prior light history of\nparticipants was standardised on the morning of the MRI scan, consisting of 5-min 1000\nlux light and the following 45-min under dim (<10 lux) light.’ (line 130).\n• Figure 1H, I – The authors should describe how the sine function was fitted to data,\nand report measures of the goodness of fit.\nStatistical analyses consisted of Generalised Linear Mixed Models (GLMM) which\nincluded a time-of-year covariate consisting of the cosine value of the day of the year\nexpressed in degrees of the 365-day year (365 day = 360°; 1 day = .986°; December 21st\n= 0°). For display purposes only, the figure included a cosine function fit to the data which\ndoes not replace the outcomes of the GLMM so that its goodness of fit is not relevant to\nour analyses.\nWe added the following to the text regarding this aspect:\n‘Reaction times were not significantly affected by illuminance (main effect, illuminance;\nF = .92, p= .45) or by time-of-year (using the cosine value associated with day of the\n4,221\nyear over a year-long cycle, see methods; main effect, time-of-year; F = .12, p= .73).’\n1,25\n(line 158-159)\n4/15\n‘Cosine fits are included for display purposes only and do not replace the outcomes\nof the GLMMs.’ (line 233 – caption of figure 2)\n• Line 155 – It is not clear why BMI should be controlled for. Is there any evidence for\nvariation in brain activation according to BMI?\nBMI was included as a reflection of overall health status and because previous studies\nusing fMRI reported brain responses and brain structure could be influenced by\nparticularly in individuals with higher BMI (which we excluded), including in regions\nrelated to reward, an aspect that is related to emotional processing. Repeating the\nanalyses without BMI as a covariate does not influence the statistical outcome of our\nanalyses. We updated the text regarding this aspect as follows (line 657-658): ’ BMI was\nalso part of the covariates because it could influence brain activity, including in regions\ninvolved in reward, which is related to emotional processing10.’\n• Line 216 – Euro sign instead of (E).\nResponse: We have edited the text accordingly (line 227).\n• Figure 2, panels F-M – It would be useful to indicate the subpart number on each\nfigure panel.\nWe have edited the Figure 2 so that the subpart number and name is included as a title\nfor each panel. (See Figure 2 – F-M)\n• Figure 2, Panels H-I – In addition to ‘day of the year’ it would be useful to include\nanother x-axis indicating the months. This will make it easier to appreciate the\nclaim in line 185 “responses were stronger during winter”.\nWe have updated the figure to include a coloured panel that shows the changing of the\nseasons (Spring, Summer, Autumn, Winter). See Figure 2.H-I.\n• Suppl. Table S4 – This table is very large, comprising multiple sections. As the\ntable is being referred to many times, it is hard to identify the relevant table\nsection. Either refer to specific sections of the table or divide the table into several\nsmaller tables.\nWe have added section headers to supplementary table S4 (a to e) so it is easier for\nreaders to follow. We have updated the main manuscript to reflect this.\n5/15\n• Lines 185-188 – Fig. 2A is referenced but if I understand correctly, the results\nappear in panels J and K.\nWe have edited the figure labelling in text so it is clearer that we are referring to Fig. 2A to\norient the reader about which subpart we are discussing. The figure results are\nreferenced later in the sentence. “Post hoc tests then revealed worse affective state was\nsignificantly associated … (t= 4.28, p = .0002, subpart 5, violet on Fig. 2A) and\ncorrected\namygdala transition areas (ATA, t= 4.64, p <.0001, subpart 7, orange on Fig. 2A)\ncorrected\n(both subparts were also different from part of the other subparts in their association with\ntime-of-year) (Fig. 2J-K, Suppl. Table S4).” Lines 202, 203, 205.\nAdditionally, we have corrected the following figure labelling in the text (lines 173 & 179):\n“…task was successful in triggering emotional responses in the amygdala with higher\nresponse to emotional than to neutral stimuli (GLMM; main effect, stimulus type, F =\n1, 27.74\n9.93; p= .0039, R²*= .26; Fig. 2D) (Suppl. Table S3). … visualization, responses\nsignificantly differed across amygdala subparts (GLMM; main effect, subpart, F =\n9, 499.9\n2.13; p= .0256, R²*= .04; Fig. 2E)”.\n• Lines 193-194 – “Overall, the results suggest that the activity of the nuclei\ncomposing the medial and superior region of the amygdala (encompassing 4 to 5\nsubparts of our template) is related to season and/or affective state.” This claim is\nnot supported by the results. A more accurate claim would be: “Activity in\nsubparts 3 and 9 varied significantly with season, while activity in subparts 5 and\n7 varied significantly with affective state. Thus, activity in selected nuclei\ncomprising the amygdala’s medial and superior region is either related to season\nor affective state”.\nWe have changed this so it is more accurately stated to “Overall, the results suggest the\nactivity in subparts 3 and 9 varied significantly with the time of the year, while activity in\nsubparts 5 and 7 varied significantly with mood. Thus, activity in selected nuclei\ncomprising the amygdala’s medial and superior region is either related to time-of-year or\nmood.”. (line 208-211).\n• Line 300 – Again, the conclusions should be stated more carefully. “We found that\nthe activity of medial and superior nuclei of the amygdala was related…” should\nbe “We found that the activity in selected medial and superior nuclei of the\namygdala was related…”.\n6/15\nWe have changed the wording so that it is clearer to the reader. ‘We found that the activity\nin selected medial and superior nuclei of the amygdala was related to either time-of-year\nor mood.’ (line 319).\n• Lines 393-395 – The authors correctly suggest that “The cross-talk with the\nprefrontal cortex is also of interest given the changes in functional connectivity\npreviously reported following light therapy when considering the amygdala as\nwhole.” In addition, given the 2022 report of the effect of light on activity in the\nhuman prefrontal cortex, at least some of the illuminance-evoked activity\nmodulations observed in the amygdala might be driven by input from the\nprefrontal cortex, and vice versa, some of the sensitivity observed in the prefrontal\ncortex might reflect input from the amygdala. This idea is supported by the\nevidence of effect of light on prefrontal cortex-amygdala connectivity, as indicated\nin the Introduction section.\nWe modified the discussion as follows to take the comment into account (line 405):\n“Ultimately, although we favour an impact of light on the amygdala through a direct or\nindirect projection of the retina to the medial nuclei, all four subparts could equally\ncontribute to the decrease BOLD signal we detect under higher illuminance.11 Given the\nchanges in functional connectivity previously reported following light therapy when\nconsidering the amygdala as a whole,12 part of our results may be mediated by an\nimpact of light on the prefrontal cortex or on cross-talk with the prefrontal cortex. In\naddition, our findings support that the reduced responsiveness of (part of) the\namygdala does not require a week of light therapy and is already present acutely,\nduring the exposure. The sizes of the effects we detected are small, such that light\nexposure may be beneficial to the emotional state through a repeated effect on the\nBMN and medial parts of the amygdala (or on the prefrontal cortex). Again, complex\nconnectivity studies considering light exposure at different timescales are required\nto test these hypotheses and to assess how our findings fit within a larger network of\n(most often) small brain regions.13,14 “ lines 406-418\n• The Discussion section is well written, addressing the main results, specifying\nlimitations, and raising interesting implications. It could probably be shortened,\nthough.\nWe have gone through the discussion and edited it, so it is shorter and more concise.\n• Lines 663-670 – In the description of prior sensitivity analysis, there is no mention\nof the predictors: seasons and affective state.\nWe had initially computed the sensitivity analyses only for our first analyses which\nincluded 4 covariates. We added season and affective state, which together with the\n7/15\ncorrection of an initial error, yields a sensitivity for large effect size of r > .53 and R² > .28\n(instead of r > .47 and R² > .22).\n8/15\nReviewer #2 (Remarks to the Author):\nThe authors present a study investigating the impacts of varying light intensities, season,\nand affective state (depressive symptoms) on regional activity in the amygdala using high\nresolution MRI. The study is well presented, and the findings interesting. However, they\nare at times overstated given the relatively weak associations observed. Additionally,\nsome aspects of the method could be clearer. Specific comments below:\nResponse: We would like to thank Reviewer 2 for their thoughtful and detailed evaluation\nof our work, which helped, in our view, improve the text.\nAbstract:\n1. The authors could be more specific here about the continuous nature of\nassessments across time of year (as opposed to distinct groups in each season)\nWe edited the abstract accordingly.\n“We first considered time-of-year changes in activity that are related to the slow change\nin photoperiod. We find that the response to emotional stimuli of selected medial and\nsuperior nuclei of the amygdala peaked around the start of winter or increased with\nworse mood status. We further assessed how alternating short exposures to light of\ndifferent illuminance acutely affected the regional activity of the amygdala….” Line 38 &\n42.\nAdditionally, we have edited the manuscript so it is clear we are measuring seasonality in\nterms of days of the year. We have used ‘time-of-year’ instead of ‘season’, so it is clear\nabout the continuous nature of the time-of-year assessment.\n2. The results could be clearer as to the direction of the affect/season related results\ni.e., “the impact of light on part of these nuclei varied with season and affective\nstate” could be more specific.\nWe have edited the results section so it is more specific: “We find that the activity in\nselected medial and superior nuclei of the amygdala peaked around the start of winter\nor increased with worse mood status” (lines 40-41) and “Importantly also, the impact\nof light on part of these nuclei peaked around the start of summer or decreased with\nworse mood.” (line 45-46)\nIntroduction:\n3. Minor typo/grammatical error “reduced shorter photoperiod” is repetitive\n9/15\nWe have fixed the manuscript accordingly (line 48).\n4. The paragraph starting at line 77 is a little hard to follow. Ref 21 found increased\namygdala-prefrontal connectivity during light exposure, not decreased (aligned\nwith findings from ref 22 post light therapy). It would be clearer to present the\nprimary findings of the four studies described here before speculating on potential\nreasons for apparent differences (i.e., suppression of activity by light during rest,\nand reduced activity following light therapy, but not during the completion of an\nemotional task). It could be clearer why the presence of an emotional task would\nexplain these differences, though this may be more appropriate for the discussion\nwhere it can be explored in the context of the current findings.\nWe have re-written this section to clarify these aspects while still keeping the\nintroduction concise (Lines 81 to 97): “In vivo neuroimaging studies on the topic in\nHumans are scarce and have reported contrasting results. Functional Magnetic\nResonance Imaging (fMRI) showed that blue-wavelength (473 nm) light enhanced the\nresponses to emotional stimuli in the amygdala as well as its crosstalk with the\nhypothalamus.7 In contrast, in a resting-state fMRI paradigm, exposure to a warm\npolychromatic white light (~2800K; 100 lux) suppressed amygdala activity compared\nto darkness (<1 lux) while its connectivity with the ventromedial prefrontal cortex\nwas enhanced.15 Furthermore, a dose-dependent effect of three weeks of bright-\nlight therapy was reported to reduce threat-related reactivity of the amygdala and to\nincrease its functional connectivity with medial prefrontal cortex, suggesting these\nchanges may be part of the mechanism that mediates the beneficial effects of\nbright-light therapy.16 The difference between studies may arise from differences in\nthe light sources, from being engaged in a cognitive task vs. at rest and from\nconsidering shorter or longer impacts of light. They may also be due to the context-\ndependent response of the different nuclei of the amygdala that could not be\naddressed given the data resolution. Moving away from light’s impact, seasonal\ndifferences in the volume of subregions of the human amygdala were reported, with\na peak in the summer. However, there was no association between mood measures\nand amygdala subregions volumes and photoperiod.17 “\nResults:\n5. Although full methods are presented later, it would be helpful to specify the time\nframe of ‘morning’ here.\nWe now specify the following:\n10/15\n‘The brain activity of 29 healthy young participants (24y ±3.1; 18 women) was recorded\nduring an fMRI protocol conducted in Liège (Belgium) in the morning (scans 3 to 3.5hrs\nafter habitual wake-up time),…’ (line 119-120);\n‘Participants arrived at the laboratory 1.5h after wake-up time’ (line 129 – figure 1\ncaption & line 513 - methods);\n‘In total, 29 participants (24y ±3.1; 18 women) performed an executive (always first), an\nemotional and an attentional task (pseudo-randomly 2nd or 3rd, purple arrow; meaning\nthat the fMRI recording of the emotional task was completed 3 to 3.5h after wake-up\ntime).’ (line 133-134, caption figure 1);\n‘All participants included in this analysis were therefore scanned ~3h after habitual\nwake-up time and between 8 am and 1:15 pm [Fig.1A].’ (line 521-523, methods).\n6. Minor typo in the caption of Figure 1 – ‘lose’ sleep-wake schedule (loose?). The\nprior light history standardisation is also not clear, the text sounds more like it Is\nlight exposure across those 7 days, when in fact it is only the immediate light\nhistory prior to the scan.\nWe corrected the typo (line 136) and modified the figure caption as follows (line xx)\n‘Participants followed 7-days of regular loose sleep-wake schedule (+- 1h, verified using\nactigraphy) before the MRI scan. The prior light history of participants was standardised\non the morning of the MRI scan, consisting of 5-min 1000 lux light and the following 45-\nmin under dim (<10 lux) light.’ Line128-130.\n7. It would be helpful to indicate local season on the graphs which show data by day\nof year (and/or actual changes in photoperiod).\nWe added coloured panels to the figure to represent the seasons. See Figure 2.H-I.\n8. The way in which affective state was measured should be noted here.\nWe have clarified this aspect as follows (line xx ): We further assessed variation in the\nmood of our participants, based on scores on the Beck Depression Inventory-II,18\nand…”. (line 163).\n9. Many of the associations shown in the bottom section of Figure 2 were very weak.\nGiven that some did not survive correction, the authors should show/state this\nmore clearly on the figure.\nWe stress that, as stated in the text, the associations between time-of-year and the BMN\nand AAA as well as between mood and the medial and cortical nuclei and ATA survived\n11/15\ncorrection for multiple comparisons. We do not consider them weak even though effects\nmay be small. As also stated in the text (line 413): “The sizes of the effects we detected\nare small such that light exposure may be beneficial to the emotional state through a\nrepeated effect on the BMN and medial parts of the amygdala (or on the prefrontal\ncortex).”\nWe now make clearer in the figure captions that some associations did not survive\ncorrection for multiple testing. Line 238 – caption figure 2 ‘A similar statistical trend (that\ndid not survive correction for multiple comparisons) was detected in the anterior\namygdaloid area (L; subpart 9) and the intercalated nuclei (M; subpart 10).’ Lines 285 &\n289 – caption of Figure 3: ‘as indicated by a statistical trend (that did not survive\ncorrection for multiple comparisons).’\nDiscussion:\n10. “Affective state varies with seasons in a large portion of the healthy population” –\ngiven depression scores (the measure of ‘affective state’ used in this study) did not\nvary by season in this sample, there may be a better way to introduce the\nsummary of your findings.\nWe have changed this sentence to the following “Cognitive brain function varies with\nseasons, and psychiatric symptoms in patients also show seasonal variations.19,20” (Line\n308-309).\n11. The discussion overall is well-reasoned and written. However, at times, the\nresults, particularly those pertaining to affective state and season, are overstated\ngiven the relatively weak relationships presented.\nThank you for your comment. We have edited the discussion so the results a presented\nin a more balanced way. For instance, we now state\n(line 377-384): “Light may impact mood through both the direct and indirect routes.\nTesting whether the timescale (acute vs long-term photoperiod) changes the\npathways involved could be important for determining how light affects mood. The\nacute impact could be mainly mediated by a direct projection to the medial amygdala. In\ncontrast, since seasonal affective disorder was found to be associated with a\nmisalignment of the circadian clock that can be corrected by light therapy19,21, the\nlonger-term impact of photoperiod could indirectly reach the BMN through the SCN\nand/or the lateral hypothalamus. Future studies including connectivity analyses\nshould test these hypotheses.”\n(line 385-392): “Importantly, the impact of light on the amygdala was not uniform across\nits subparts at the different times of the year and with respect to mood (as indicated by\n12/15\nsignificant interaction terms), at least when light is delivered in the morning. Statistical\ntrends potentially pointed towards the nuclei driving these non-uniform responses\nto light. The variation with time-of-year may be driven by the ATA, which showed a larger\nimpact of light around summer solstice, while the variation related to worse mood\nmay arise from the AAA. Future research should test whether these non-uniform\nresponses to light contribute to the effectiveness of light therapy.”\nMethods:\n12. Minor typo/clarification needed re: “Subject wiling to exit the scanner” – perhaps\nsupposed to be wishing to exit?\nWe have changed this to ‘subject wanted to exit the scanner before the end of the\nsession’ - (line 502)\n13. Additional information regarding the standardisation of prior light would be helpful\n– i.e., are these measures the maximal/minimal level in the room, how was 1000\nlux delivered, using a light therapy device? Additional descriptions of the\ngeometric space would be helpful to understand the actual retinal light exposure.\nAdditionally, the specification of the lighting in the MRI room should be included.\nWe have added the following into the methods section (line 515-518): “To standardise\nparticipants' recent light history, they were exposed to 5min of bright white light (1000 lux;\nwith the chin on a chin-rest, ~15cm away from a plastic diffuser in front of a\npolychromatic halogen light bulb) and were then maintained in dim light (<10 lux) for\n45min (bright and dim light levels were controlled for each participants at eye level).”\n14. Can the authors speak to their decision to use the BDI as a marker of ‘affective\nstate’? They also had BAI available, which is arguably also relevant to the regions\nof interest, and appears to show a greater range of scores, but another measure\nwhich may be more sensitive to normal mood variation in healthy persons would\nhave likely been more appropriate than either. I am concerned that the limited\nrange of BDI scores may not meaningfully reflect differences in ‘affective state’ in\nthis population. It would be more appropriate to refer to this measure as\ndepressive symptoms, rather than affective state, throughout the paper. At a\nminimum the actual range of scores and mean/SD should be reported here in the\nmethods, but ideally the limitations of using this measure in an otherwise healthy\npopulation should also be discussed in the paper.\n13/15\nWe have edited the paper so ‘affective state’ has been edited to ‘mood’ and we clearly\nmention at first that it is assessed through the BDI. This term is preferred to depressive\nsymptoms as the use of this terminology could lead the reader to think that we included\npatients when all our participants were healthy and free of psychiatric diagnosis. The\nreviewer is right when stating that BAI could also be a measure of interest. BAI has\nhowever been reported to be strongly associated with BDI and as its name indicate is\nrelated to anxiety rather than mood which is truly what we are interested in given that light\ntherapy and photoperiod are most known to affect mood disorders and subclinical mood\nvariations. “Furthermore, we used BDI to measure mood in a young healthy\npopulation, implying non-clinical variations in mood scores. We cannot generalise\nour results to populations with depression or other psychiatric conditions.” (line 464-\n466)\nReferences\n1. Sander, D. et al. Emotion and attention interactions in social cognition: Brain\nregions involved in processing anger prosody. Neuroimage 28, 848–858 (2005).\n2. Grandjean, D. et al. The voices of wrath: Brain responses to angry prosody in\nmeaningless speech. Nat Neurosci 8, 145–146 (2005).\n3. Campbell, I. et al. Regional response to light illuminance across the human\nhypothalamus. Elife 13, (2024).\n4. Campbell, I. et al. Impact of light on task-evoked pupil responses during cognitive\ntasks. J Sleep Res 33, (2024).\n5. Beckers, E. et al. Impact of repeated short light exposures on sustained pupil\nresponses in an fMRI environment. J Sleep Res 33, (2024).\n6. Paparella, I. et al. Light modulates task-dependent thalamo-cortical connectivity\nduring an auditory attentional task. Commun Biol 6, (2023).\n7. Vandewalle, G. et al. Spectral quality of light modulates emotional\nbrainresponses in humans. Proc Natl Acad Sci U S A 107, 19549–19554 (2010).\n8. Vandewalle, G. et al. Abnormal Hypothalamic Response to Light in Seasonal\nAffective Disorder. Biol Psychiatry 70, 954–961 (2011).\n9. Allen, A. E. et al. Melanopsin-driven light adaptation in mouse vision. Current\nBiology 24, 2481–2490 (2014).\n10. Makaronidis, J. M. & Batterham, R. L. Obesity, body weight regulation and the\nbrain: Insights from fMRi. British Journal of Radiology 91, (2018).\n14/15\n11. Whalen, P. J. & Phelps, E. A. The Human Amygdala. The Human Amygdala\n(Guilford Press, 2009).\n12. Fisher, P. M. et al. Three-week bright-light intervention has dose-related effects on\nthreat-related corticolimbic reactivity and functional coupling. Biol Psychiatry 76,\n332–339 (2014).\n13. Huang, L. et al. A Visual Circuit Related to Habenula Underlies the Antidepressive\nEffects of Light Therapy. Neuron 102, 128-142.e8 (2019).\n14. An, K. et al. A circadian rhythm-gated subcortical pathway for nighttime-light-\ninduced depressive-like behaviors in mice. Nat Neurosci 23, 869–880 (2020).\n15. McGlashan, E. M., Poudel, G. R., Jamadar, S. D., Phillips, A. J. K. & Cain, S. W.\nAfraid of the dark: Light acutely suppresses activity in the human amygdala. PLoS\nOne 16, (2021).\n16. Fisher, P. M. et al. Three-week bright-light intervention has dose-related effects on\nthreat-related corticolimbic reactivity and functional coupling. Biol Psychiatry 76,\n332–339 (2014).\n17. Majrashi, N. A., Alyami, A. S., Shubayr, N. A., Alenezi, M. M. & Waiter, G. D.\nAmygdala and subregion volumes are associated with photoperiod and seasonal\ndepressive symptoms: A cross-sectional study in the UK Biobank cohort.\nEuropean Journal of Neuroscience 55, 1388–1404 (2022).\n18. Beck, A. T., Ward, C. H., Mendelson, M., Mock, J. & Erbaugh, J. An inventory for\nmeasuring depression. Arch Gen Psychiatry 4, 561–571 (1961).\n19. Zhang, R. & Volkow, N. D. Seasonality of brain function: role in psychiatric\ndisorders. Translational Psychiatry 2023 13:1 13, 1–11 (2023).\n20. Meyer, C. et al. Seasonality in human cognitive brain responses. Proc Natl Acad\nSci U S A 113, 3066–3071 (2016).\n21. Lavoie, M. P. et al. Evidence of a Biological Effect of Light Therapy on the Retina of\nPatients with Seasonal Affective Disorder. Biol Psychiatry 66, 253–258 (2009).\n15/15"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my concerns, and the revised manuscript is significantly improved. I have no further comments.", "reviewer_2": "The authors have thoughtfully revised their manuscript. One minor note regarding the new figure panel (Fig 3f) - the embedded figure does not appear to be the new version. I would also suggest adding the local season \"names\" to the figure in addition to colours for ease of interpretation."}, "author_response": "Response : We would like to thank the reviewer #1 for taking the time to review our\nmanuscript.\nResponse : We would like to thank the reviewer #2 for taking the time to review our\nmanuscript. We have edited the combined Figure 3 so it includes the updaed version\nof Fig.3F. We have included the following - ‘Seasons represented by background\ncolours (winter – blue, spring – green, summer – yellow, autumn – orange) ’ - in the\nfigure ledgend for Figure 2H,I and 3F, so it is easier to interpret."}]

Decision letter