Seasonal changes in environmental conditions, particularly variations in daylight length (photoperiod), temperature, and light exposure, have long been found to influence human physiology, behavior, and mental health. These changes impact mood, sleep, cognition, and biological rhythms throughout the year [1, 2]. Clinically, seasonal fluctuations are most prominently observed in seasonal affective disorder (SAD), a subtype of depression that affects 1–10% of the population in temperate regions and is characterized by recurrent depressive episodes during fall and winter, typically triggered by reduced daylight exposure [3, 4]. However, subclinical seasonal variations in mood, sleep patterns, cognitive performance, and social behavior occur across the general population, suggesting that seasonal environmental changes exert broad influences on brain function [5].

Human brain function is affected by intrinsic neurobiological mechanisms as well as external environmental influences. Among these, seasonal variations in photoperiod, ambient temperature, and climatic patterns are increasingly recognized as important modulators of neural activity and behavior [2, 6]. The neurobiological substrates of these seasonal effects are being explored increasingly often through neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI), which has allowed researchers to examine how seasonal and environmental factors modulate brain activity (Fig. 1). fMRI has revolutionized our understanding of brain function by enabling the non-invasive measurement of neural activity through blood-oxygen-level-dependent (BOLD) signals [7]. Functional connectivity is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions. In recent years, an increasing body of neuroimaging studies has explored functional connectivity by measuring the level of co-activation of resting-state fMRI (rs-fMRI) time-series between brain regions [8]. rs-fMRI enables the study of intrinsic functional connectivity so that the temporal synchronization of neural activity between anatomically distinct brain regions can be captured while the individual is not engaged in a task. rs-fMRI measures spontaneous fluctuations in BOLD signals, which enables the mapping of intrinsic brain networks such as the default mode network (DMN), sensorimotor network (SMN), and salience network (SN) [7, 8]. These networks are critical for fundamental functions, including emotion regulation, cognition, and self-referential thought.

Fig. 1.

Functional magnetic resonance imaging (fMRI) methodology for detecting brain activity. fMRI uses the blood-oxygen-level-dependent (BOLD) signal to non-invasively detect brain activity. Neural activation increases local blood flow, altering oxygenation levels that are captured through high-field magnetic resonance imaging (MRI) scanners. This schematic diagram was created by the author using Adobe Illustrator 2024 (Adobe Inc., San Jose, CA, USA) and BioRender.com (https://www.biorender.com/). The author has no financial or personal relationship with BioRender.com, and the use of this tool does not imply any endorsement.

Recent studies suggest that functional connectivity within and between these networks shows seasonal modulation. Studies have shown seasonal changes in brain metabolism [6], functional network strength [9], and regional activation patterns, especially in regions associated with affect regulation, sensory processing, and memory [10]. A previous study reported robust seasonal variation in both global and regional functional connectivity, with peak connectivity in autumn and the lowest levels in summer [9]. Changes were observed in the DMN, SMN, and ventral attention network (VAN), as well as in white matter (WM) connectivity metrics such as network density and efficiency. Similarly, Zhang et al. [11] and Triana et al. [12] identified strong seasonal patterns in the SMN over a multi-year single-subject dataset, linked to photoperiod-related molecular changes. These results highlight a reproducible seasonal signature in functional neural network.

Moreover, environmental factors such as temperature and daylight length have been implicated as modulators of neural activity. Granés et al. [10] found that higher ambient temperatures were associated with reduced functional connectivity in the medial parietal, hippocampal, and SN among preadolescents. These environmental effects on brain function are not merely incidental, and they may have critical developmental, behavioral, and clinical implications.

The neurobiological pathways through which seasonal environmental changes influence brain function involve multiple interconnected systems. Photoperiod variations affect the suprachiasmatic nucleus, the brain’s master circadian clock, which regulates melatonin secretion and downstream hormonal cascades [2]. Temperature changes influence peripheral and central thermoreceptors, affecting hypothalamic regulatory centers and their connections to cortical and limbic regions [6]. These environmental inputs converge on brain networks critical for mood regulation, attention, and social cognition. The DMN, comprising the medial prefrontal cortex, posterior cingulate cortex, and angular gyrus, serves as a hub for self-referential processing and has been consistently implicated in mood disorders [8]. Similarly, the SN, anchored by the anterior insula and dorsal anterior cingulate cortex, plays crucial roles in attention and emotional processing [7].

However, not all studies support the presence of true biological seasonal effects. Di et al. [13] cautioned that scanner-related factors such as signal drift might mimic seasonal variation, emphasizing the need for methodological rigor and scanner quality control in such analyses. Despite these challenges, understanding the seasonality of brain function has important implications for clinical neuroimaging. The magnitude of seasonal effects observed in functional connectivity is comparable to that seen in several neurological and psychiatric conditions [9], raising concerns about the potential for confounding effects in clinical trials or diagnostic imaging conducted across different seasons.

Therefore, this review aims to synthesize current evidence concerning seasonal effects on brain functional connectivity as measured by fMRI. Specifically, this study (1) identifies which brain networks exhibit seasonal variation, (2) examines relationships between environmental variables and neural connectivity, (3) assesses the methodological strengths and limitations of existing studies, and (4) explores implications for both clinical and research settings in neuroscience. Despite its neuroscientific focus, this review holds considerable relevance for hospital-based clinical practice. Seasonal modulation of brain connectivity may influence neuroimaging interpretation, treatment timing, and research design within hospital settings, particularly in radiology, psychiatry, and neurology departments. By addressing its aims, this review seeks to establish seasonal neuroscience as a critical consideration in both basic and clinical neuroscience while providing a roadmap for future investigations in this rapidly evolving field.

This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, and a checklist is provided as supplementary material (Supplementary File 1). The systematic review was registered in the Internationa Prospective Register of Systematic Reviews (PROSPERO) database (CRD420261390073, https://www.crd.york.ac.uk/PROSPERO/view/CRD420261390073). This comprehensive review employed a systematic approach to identify and synthesize studies examining seasonal effects on brain functional connectivity (Fig. 2). The search strategy was designed to encompass the multidisciplinary scope of this emerging research area, integrating terminology from neuroscience, environmental science, clinical psychiatry, and related fields. Multiple databases were thoroughly searched to ensure extensive coverage. These included primary databases such as PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE) (https://pubmed.ncbi.nlm.nih.gov/), Scopus (https://www.scopus.com/), and Web of Science (https://www.webofscience.com/). The search utilized a detailed and structured strategy, combining medical subject headings (MeSH) terms and free-text keywords with Boolean operators. The primary search string was constructed as follows: (“seasonal” OR “photoperiod” OR “circadian” OR “daylight” OR “temperature” OR “climate”) AND (“brain” OR “neural” OR “neuroimaging”) AND (“functional connectivity” OR “resting state” OR “fMRI” OR “network” OR “BOLD”) AND (“seasonal affective” OR “environmental neuroscience” OR “seasonal variation”). This comprehensive and precise search strategy facilitated the identification of relevant articles, ensuring that critical studies spanning diverse, yet interconnected disciplines were considered.

Fig. 2.

Flowchart depicting the systematic literature search and study selection process. Figure created by the author using Microsoft Visio (Microsoft Corporation, Redmond, WA, USA, https://www.microsoft.com/en-us/microsoft-365/visio/flowchart-software). Abbreviation: MEDLINE, Medical Literature Analysis and Retrieval System Online.

Studies were included if they met all of the following criteria: (1) published in peer-reviewed journals between 2010 and 2025; (2) employed fMRI, including rs-fMRI or task-based paradigms; (3) investigated the effects of seasonal, circadian, or environmental variables, such as photoperiod, temperature, or time of day, on brain functional connectivity; and (4) involved human participants with clearly reported sample sizes. Additional exclusion criteria were applied to refine the study pool by eliminating studies that, while meeting some inclusion parameters, lacked relevance or methodological appropriateness. Specifically, studies were excluded if they: (1) were not published in English, (2) were conducted solely on animals or in vitro models, (3) lacked functional neuroimaging data (e.g., used only structural magnetic resonance imaging (MRI) or behavioral outcomes), (4) were single-subject or case reports, or (5) did not include any environmental or temporal variables related to seasonality.

Due to the heterogeneity of imaging methodologies, seasonal variables, and outcome measures across the included studies, a quantitative meta-analysis was not feasible. Therefore, a narrative synthesis approach was employed to integrate findings across studies.

For each included study, the following information was systematically extracted: study characteristics (authors, year, country, study design), sample characteristics (sample size, age range, gender distribution, clinical status), environmental variables (photoperiod, temperature, season definitions), neuroimaging parameters (scanner type, field strength, acquisition parameters), analysis method (connectivity metrics), and key findings (affected brain networks, seasonal patterns).

The reviewed studies demonstrate that seasonal and environmental factors exert significant modulatory effects on brain functional connectivity across diverse populations and methodological frameworks. These effects emerge from both rs-fMRI and task-based fMRI paradigms, encompassing daylight duration, ambient temperature, time of day, and environmental stimuli exposure.

5.4 Circadian and Time of Day Modulation

Circadian timing critically influences connectivity patterns. Orban et al. [20] examined over 800 adults scanned between 8:00 AM and 10:00 PM, observing paradoxical reductions in global signal fluctuation and decreased SMN and VN connectivity as days progressed, suggesting circadian-driven changes alter baseline network dynamics. Hu et al. [22] found later daily scans associated with lower network efficiency, clustering, and stability in visual, dorsal attention, and salience networks. Weekend and summer scans showed distinct profiles, highlighting circadian-seasonal interactions in network organization. Racicot et al. [17] demonstrated functional significance through 40–50 scanning sessions per participant, finding limbic network connectivity variations (amygdala, hippocampus) explained significant within-subject variance in affective states, suggesting environmental context meaningfully shapes emotional brain architecture.

Seasonal changes in brain network connectivity are demonstrated in Fig. 3 below. Environmental factors (photoperiod, temperature, time of day) modulate brain functional connectivity, resulting in weaker (top) or stronger (bottom) inter-network coupling patterns. The arrows in the figure indicate environmental influences on neural network organization, demonstrating the dynamic relationship between external conditions and brain connectivity strength.

Fig. 3.

Seasonal changes in brain network connectivity. Arrows indicate environmental influences on neural network organization, demonstrating the dynamic relationship between external conditions and brain connectivity strength. This figure was created by the author using Adobe Illustrator 2024 (Adobe Inc., San Jose, CA, USA) and BioRender.com (https://www.biorender.com/).

Limbic network connectivity variations show functional significance for affective states. Racicot et al. [17] used 40–50 scanning sessions per participant to demonstrate that limbic network connectivity variations (involving amygdala and hippocampus) explained 15% and 11% of within-subject variance in positive and negative effects, respectively. Monroe et al. [19] identified increased central autonomic network (CAN) connectivity in soccer players exposed to repetitive seasonal impacts, particularly in left insula-orbitofrontal pathways linked to impaired cognitive control. Granés et al. [10] found temperature effects on hippocampal connectivity in children, indicating environmental sensitivity of limbic structures during development.

Across the included studies, certain consistent patterns emerge: (1) the DMN and SMNs show robust autumn peaks with summer minimums, closely tied to photoperiod [6, 9, 11]; (2) the SN demonstrates temperature sensitivity with inverse patterns to the DMN [10]; (3) the AN shows variable patterns that could reflect different functional roles [6, 20, 22]; (4) the VN responds to both seasonal photoperiod and daily circadian rhythms [11, 15, 20]; and (5) limbic networks show meaningful relationships with affective states across environmental fluctuations [17, 19]. These patterns indicate network-specific environmental sensitivities that may have adaptive and clinical implications.

Table 3 provides a systematic comparison of seasonal changes across different brain networks and their relationships with environmental variables by summarizing the key findings related to research objectives (1) and (2).

Table 4 provides a comprehensive framework comparing different research approaches used in environmental neuroscience studies to systematically address research objective (3) regarding methodological strengths and limitations.

The methodological approaches employed across environmental neuroscience studies reveal some strengths and critical gaps that influence the interpretation and clinical translation of the results (Table 4). While most studies utilized appropriate 3.0T MRI systems and standard demographic controls, significant methodological heterogeneity limits direct cross-study comparisons. The predominance of regional meteorological data over individual environmental measurements introduces exposure misclassification that may attenuate true effect sizes. Most critically, only 13% of studies included technical controls such as phantom scans to verify scanner stability across seasons, despite evidence that environmental factors can influence scanner performance. The balanced representation of cross-sectional and longitudinal designs provides complementary perspectives, though the superior causal inference capabilities of longitudinal approaches make them preferable for establishing environmental-neural relationships. Sample sizes were appropriately distributed across small, medium, and large studies, though power calculations specific to seasonal effect sizes are rare. Future research should prioritize Tier 1 methodological requirements while incorporating Tier 2 enhancements to strengthen causal inference and clinical applicability.

Seasonal connectivity effects are comparable to those associated with psychiatric and neurological conditions [9], raising concerns for clinical neuroimaging if seasonal changes are not controlled. This could confound group comparisons or longitudinal assessments, potentially masking or artificially enhancing patient-control differences. These findings offer insights into mood disorder pathophysiology and intervention timing guidance. Understanding seasonal neural adaptations could inform educational practices, workplace policies, and clinical treatments, thereby optimizing year-round cognitive performance. The research reinforces the environmental neuroscience value for public health strategies, particularly regarding climate change’s impacts on the population’s cognitive function. Future research should standardize acquisition protocols, employ larger diverse samples, and control for biological and technical confounders to isolate environmental rhythm signatures and clinical implications.

This review synthesized findings from most available fMRI studies investigating how seasonal and circadian rhythms modulate brain functional connectivity. The evidence suggests that both macro-temporal (seasonal) and micro-temporal (circadian) environmental rhythms have measurable and sometimes profound influences on intrinsic brain network dynamics. These effects appear to be widespread across cognitive, sensory, and affective circuits, with implications for both healthy function and neuropsychiatric vulnerability. One of the most consistent findings was the seasonal modulation of the DMN, a system central to self-referential thinking and internal mentation [26]. Previous studies demonstrated that DMN connectivity fluctuates with seasonal shifts in photoperiod and solar exposure, reaching peaks in autumn and declining in summer [6, 9]. This observation aligns with prior research showing that mood, cognition, and the levels of neurochemicals such as serotonin vary seasonally in healthy individuals [27, 28]. Functional neuroimaging studies have linked reduced daylight exposure to diminished serotonin transporter binding [4], which may partially mediate seasonal fluctuations in mood and brain connectivity.

Circadian rhythms, intrinsic approximately 24-hour biological oscillations entrained by environmental cues, were also found to influence network architecture. Orban et al. [20] and Hu et al. [22] reported diurnal decreases in sensory and attentional network connectivity, demonstrating the effects of time of day on brain functional organization. These findings are consistent with studies in chronobiology showing circadian modulation of attention, executive function, and cortical excitability [29, 30]. Notably, Zhang et al. [11] and Racicot et al. [17] used high-density sampling to demonstrate that these effects are not merely interindividual but are observable within single participants across days and seasons.

Importantly, alterations in functional connectivity were also evident in non-clinical and developmental populations. Granés et al. [10] showed that higher ambient temperatures reduced connectivity in SN and medial parietal networks in children, supporting the notion that thermal stress may impair brain function in vulnerable populations. This complements emerging research in environmental neuroscience suggesting that heat exposure impairs working memory and executive performance via changes in neurovascular and metabolic efficiency [31]. Given that children’s brains are undergoing rapid development, seasonal and environmental variation may have long-lasting implications for educational and cognitive outcomes. The relationship between environmental modulation and psychiatric risk was particularly salient in studies of SAD. Abou Elseoud et al. [15] demonstrated enhanced connectivity in visual and attentional circuits during winter in SAD patients, which may represent compensatory mechanisms to reduce sensory input. Such alterations in connectivity may reflect a breakdown in the regulation of affective salience and cognitive control under seasonal stress. Moreover, clinical research has shown that seasonal fluctuations in brain activity may precede mood symptoms, suggesting a neurobiological diathesis that interacts with environmental context [4, 17].

In the domain of sports neuroscience, repeated subconcussive exposure across athletic seasons also altered intrinsic connectivity. Previous studies show DMN and AN changes in athletes over time [14, 18, 19]. These findings mirror neuropsychological evidence indicating that even subclinical head trauma can disrupt functional network efficiency and lead to cumulative neurocognitive deficits [32]. Given the seasonal structure of many athletic programs, it is crucial that future fMRI studies in sports consider time of year and accumulated exposure in their designs. However, these findings should be interpreted with caution. Di et al. [13] demonstrated that environmental variables such as weather conditions could predict imaging results using phantom and anatomical scans, suggesting that some seasonal fMRI patterns may result from scanner instability or technical drift. These artifacts can mimic neural activity if not rigorously controlled. This reinforces calls in the neuroimaging community for multi-site replication, routine phantom calibration, and the standardization of environmental variables during scanning [33].

In summary, the evidence underscores the dynamic interplay between environmental rhythms and brain functional architecture. While the precise mechanisms remain under investigation, likely contributors include photoperiod-induced hormonal shifts (e.g., melatonin, cortisol), seasonal changes in serotonin and dopamine function, and environmental influences on vascular tone and metabolism [34]. The findings have wide-reaching implications not only for clinical diagnostics and brain health but also for neuroimaging research protocols, which must increasingly account for these “invisible” environmental variables.

Despite growing evidence for environmental modulation of functional connectivity, the current literature has several limitations. First, there is considerable heterogeneity in methodology, including differences in sample size, scanner strength, preprocessing pipelines, and statistical thresholds. This limited our ability to make direct comparisons between studies and complicates meta-analytic synthesis. Second, although most studies controlled for major confounders such as age and gender, few of them addressed behavioral or physiological factors, such as sleep, caffeine intake, or mood at the time of scanning, which may themselves be seasonally modulated. Third, environmental data such as daylight or temperature were often inferred from regional averages and were not always verified at the individual level, introducing potential measurement bias. Fourth, the majority of studies used cross-sectional designs, which are prone to interindividual variability. While longitudinal studies offer greater sensitivity to change, only a subset of included studies adopted this design. Furthermore, dense-sampling studies, though informative, remain underpowered for generalization. Finally, very few studies included diverse age ranges, socioeconomic backgrounds, or non-Western populations, limiting the global applicability of the findings. There is also a notable gap in the integration of multimodal imaging or physiological metrics, such as melatonin levels or cortisol rhythms, which could clarify underlying mechanisms.

This review highlights that seasonal and circadian factors have a measurable influence on brain functional connectivity, as detected through rs-fMRI and task-based fMRI. The evidence spans across healthy and clinical populations, from children to adults, and includes both cross-sectional and longitudinal designs. Seasonal influences appear most prominently in the DMN, as well as the sensory and limbic networks, while circadian effects are evident in attention and sensorimotor systems. These findings underscore the need to account for time of year and time of day in basic and clinical neuroscience research. To advance this field, future studies should incorporate rigorous methodological controls, larger and more diverse cohorts, multimodal physiological measures, and standardized scan timing protocols. Doing so will help disentangle genuine neurobiological rhythms from technical artifacts and foster a more nuanced understanding of how the brain aligns with environmental cycles.

• Seasonal and circadian factors, particularly photoperiod and temperature, have clinically significant effects on brain functional connectivity across multiple networks, with magnitudes comparable to neurological and psychiatric disorders.

• The DMN and SMN show seasonal modulation with autumn peaks and summer minimums correlating with photoperiod, while salience networks demonstrate inverse temperature-related patterns.

• Time of day effects reveal paradoxical reductions in global signal and sensorimotor/visual connectivity from morning to evening, emphasizing the need to control scan timing in neuroimaging protocols.

• Scanner stability artifacts can mimic biological seasonal effects, necessitating rigorous technical controls, including phantom scans and multi-site replication, to distinguish genuine neurobiological rhythms from confounds.

• Seasonal connectivity variations have critical implications for clinical neuroimaging interpretation and research design, as uncontrolled seasonal factors may confound patient-control comparisons or mask treatment effects.

• Future research should prioritize longitudinal designs with dense sampling, individual-level environmental measurements, multimodal assessments, and standardized protocols accounting for seasonal and circadian timing.

This systematic review is based on previously published studies. The data used in this systematic review are available from the corresponding author upon reasonable request.

All elements of the study and subsequent write-up were carried out by the author [NAM]. The author read and approved the final manuscript. The author has participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Not applicable.

The author gratefully acknowledges the contributions of research assistants from the Diagnostic Radiography Technology (DRT) Department who supported the literature screening and data extraction stages of this review. Sincere appreciation is also extended to colleagues from the College of Nursing and Health Sciences for their valuable methodological guidance. The author also thanks Jazan University, Saudi Arabia, for providing the online resources and institutional support necessary to complete this research.

The author gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through Project number: JU-202502140-DGSSR-RP-2025.

The author declares no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/BJHM53980.