Major changes in ovarian hormone concentrations occur throughout the female lifespan, such as during puberty, pregnancy, postpartum, and menopause. The menstrual cycle involves considerable fluctuations in ovarian hormones, namely a follicular surge and decline in estradiol during the periovulatory period, and a sustained peak in estradiol and progesterone in the mid-luteal phase, followed by a decrease in both ovarian hormones shortly before menstruation (Figure 1). These ovarian hormones can influence the brain via slow genomic (Tetel and Acharya, 2013) and fast non-genomic mechanisms [2; 3; 4]. Indeed, several behaviourally-relevant brain regions have been shown to be modulated by ovarian hormones in females with regular menstrual cycles [5; 6; 7]. For instance, functional neuroimaging studies have pointed to menstrual cycle effects on the hippocampus [8; 9], amygdala [10; 11; 12; 13], insula [14; 15; 16], anterior cingulate cortex [16; 17; 18], and prefrontal cortex [15; 19; 20; 21], all regions critical for emotional and cognitive processing (Dubol et al., 2021). In fact, as part of the neuroendocrine system, ovarian hormones critically regulate a wide array of psycho-behavioural functions, from circadian rhythms to stress resilience [22; 23; 24].

Ovarian hormone fluctuations play a prominent role in the pathophysiological mechanisms of menstrual-related mood disorders (MRMDs) (Rubinow and Schmidt, 2018). MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders (Yonkers and Simoni, 2018). MRMDs can lead to severe psychological symptoms, as well as functional and interpersonal impairment, which can foster suicidal ideation and behaviour [27; 28]. The premenstrual symptoms associated with MRMDs most commonly entail emotional distress, social, and occupational impairments, partly overlapping with those of major mood disorders, anxiety, and post-traumatic stress disorders [29; 30].

PMS has been described as a variety of psychological and physical symptoms, experienced during the late luteal phase of the cycle, that subside shortly following subsequent menstruation, if not better explained by other diagnosis (Biggs and Demuth, 2011). A severe form of PMS, known as premenstrual dysphoric disorder (PMDD) has been categorized as a depressive disorder and recently added to the DSM-5 (Epperson et al., 2012). On the other hand, significant worsening of the emotional and behavioural symptoms underlying an affective disorder during the luteal phase is classified as PME. Due to lack of comprehensive diagnostic schemes (Halbreich, 2004) and poor awareness in the medical and research field (Eisenlohr-Moul, 2019), individuals with PMDD are often misdiagnosed with a non-cyclical affective disorder or PME (Hantsoo et al., 2022). Several clinical conditions have been linked to PME such as depression [35; 36], bipolar disorder [37; 38], and schizophrenia (Seeman, 2012), although the syndrome is not yet defined as a diagnostic entity, with its prevalence and neurobiological underpinnings being even less investigated than those of PMDD.

A main characteristic of the hormonal fluctuation-related symptomatology in MRMDs is the temporal variance in affective, cognitive, or somatic states (Rubinow and Schmidt, 2003). This cluster of symptoms poses several methodological challenges, likely complicating the identification of the psychobiology of MRMDs. In fact, knowledge of the neurophysiological mechanisms of MRMDs is scarce. Symptomatology or symptom exacerbation is temporally linked to the luteal phase of the cycle and thus likely the luteal-phase specific surge in progesterone and estradiol (Eisenlohr-Moul, 2019). Considering that ovarian hormone concentrations in persons with PMS and PMDD have been reported to lay within standard ranges [41; 42; 43], current evidence suggests an altered central sensitivity to physiological phasic hormone fluctuations in individuals affected by MRMDs [44; 45]. Indeed, pharmacological suppression of these fluctuations with gonadotropin-releasing hormone (GnRH) agonists leads to significant remission of premenstrual symptoms in persons with PMS or PMDD [44; 46; 47; 48]. Albeit, the distinct effects of estradiol and progesterone on premenstrual symptomatology and the surrounding contribution of other phenomena such as receptor plasticity and dynamics have not yet been disentangled.

Different mechanisms have been suggested to underlie the ovarian hormone sensitivity hypothesized for MRMDs, one of which is highlighting the contribution of luteal progesterone fluctuations to premenstrual symptoms [46; 49], or those of its neuroactive metabolite allopregnanolone (Bixo et al., 2018). This has been corroborated by preliminary evidence indicating altered γ-Aminobutyric acid (GABA) function in PMDD in comparison with healthy controls. Namely, lower GABA concentrations have been found in subjects with PMDD in the luteal phase compared with healthy controls (Liu et al., 2015), Further evidence demonstrates an increase in GABA concentrations from the follicular to the mid- and late luteal phase for individuals with PMDD, while their follicular cortical GABA levels were also higher compared with controls [50; 52]. In line, a recent clinical trial showed that progesterone antagonism through the use of a selective progesterone receptor modulator treatment, which leads to stable and low levels of progesterone and mid-follicular levels of estrogen, alleviates the mood symptoms of PMDD, in particular irritability and depression (Comasco et al., 2021). In a functional neuroimaging investigation, increased fronto-cingulate activity during aggressive response to provocation was demonstrated as an effect of treatment (Kaltsouni et al., 2021). This can be interpreted as a potential beneficial enhancement of top-down regulation, namely better executive control on emotional reactivity, a mechanism that has been suggested to be altered in PMDD and to underlie poor emotional regulation in mood and anxiety disorders [54; 55; 56]. Another line of research highlights the involvement of serotonergic neurotransmission in premenstrual suffering [57; 58; 59], especially supported by the efficacy of intermittent dosing with serotonergic antidepressants as treatment for PMDD [47; 60]. Further evidence for an interplay between ovarian hormone fluctuations and the serotonergic system in MRMDs comes from a hormonal manipulation study that pharmacologically induced fluctuations in estradiol (Frokjaer et al., 2015). Decreases in estradiol were associated with depressive responses in healthy women, where worse symptom severity was correlated with both the magnitude of estradiol decline and increases in neocortical serotonin transporter binding (Frokjaer et al., 2015), suggesting less available extracellular serotonin. In individuals with PMDD, positron emission tomography (PET) studies have provided in vivo evidence of serotonergic neurotransmission alterations from the follicular to the luteal phase [62; 63], with findings suggesting a decrease in serotonin in the luteal phase. Most recently, serotonin transporter binding increase from the periovulatory to the premenstrual phase was noted in patients with PMDD, in which increased binding correlated with greater symptom severity, corroborating the hypothesis on serotonin depletion impacting depressive mood (Sacher et al., 2023). Nevertheless, evidence on the precise mechanism by which serotonergic neurotransmission may be related to MRMDs symptomatology is scarce.

Altered neurophysiological response to canonical ovarian hormones fluctuations likely induce premenstrual distress [65; 66]. To date, neuroimaging techniques with high spatial resolution have been sparsely employed to study the neural signatures of MRMDs and have been mainly focused on PMDD. These include structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) (Gu et al., 2022), and molecular imaging methods such as PET as well as single photon emission computed tomography (SPECT) [68; 69; 70]. Regarding anatomical differences, contradictory findings have been found in small samples with regards to PMDD [71; 72; 73; 74]. A recent multi-scale investigation, accounting for potential covariates, found differential anatomy of top-down control regions, such as the medial prefrontal, superior prefrontal, and orbitofrontal cortex, as well as superior and inferior parietal lobules, influencing the limbic system and visual processing areas as features of the PMDD brain in a relatively large sample (Dubol et al., 2022). These structural characteristics were additionally related to the severity of symptoms of PMDD during the luteal phase (Dubol et al., 2022). On the other hand, the sole study on white matter structure pointed to higher integrity and volume in tracts connected to the limbic brain in patients with PMDD during the symptomatic phase (Gu et al., 2022). However, it remains to be investigated whether these are state- or trait-features of the PMDD brain. Moreover, to the best of our knowledge, no studies have thus far been conducted on the structural correlates of PMS and PME.

Functional neuroimaging studies have found differences between females with and without PMDD during emotion processing and cognitive tasks; these differences include altered activity of cortico-limbic structures, such as lower activity in the anterior cingulate cortex (ACC) and prefrontal cortex (PFC) subregions, and higher amygdala reactivity to emotional stimuli pointing to increased bottom-up reactivity and depleted top-down cognitive control (Dubol et al., 2020). Menstrual cycle dependent reactivity to different emotion processing or cognitive tasks has been reported to differ in several corticolimbic regions, although the direction of effects greatly depends on the nature of the task used (Dubol et al., 2020). Moreover, current evidence suggests altered functional connectivity patterns between regions of the default mode network (DMN) in persons with PMS compared to healthy controls [75; 76], along with some alterations of amplitude in low frequency fluctuations within the precuneus, hippocampal, and inferior temporal cortex in persons with PMS compared with healthy controls (Liao et al., 2017). Preliminary evidence on altered network dynamics, exemplified as increased hippocampal-frontocortical and decreased hippocampal-premotor resting state connectivity, has been found to be associated with PME of bipolar disorder (Syan et al., 2018), along with menstrual cycle phase-independent increased functional temporal connectivity with the executive control network (Petersen et al., 2019). Nevertheless, despite the recent efforts to document menstrual cycle-dependent variations in intrinsic brain dynamics, alternatively known as functional brain organization (Friston, 2002), in healthy females [80; 81], the overall literature on dynamic functional connectivity in MRMDs falls short.

Furthermore, functional magnetic resonance imaging relies on signals derived from hemodynamic activity, which (i) is merely an indirect proxy for brain activity and (ii) demonstrates only moderate temporal resolution while incurring (iii) highly non-stationary noise at (iv) high imaging acquisition costs. While spatial resolution is limited for subcortical brain areas as the amygdala, electroencephalography (EEG) recordings afford the measurement of instantaneous neuronal electrical activity, deriving from multi-source synaptic trans-membrane currents with high temporal resolution (Michel and Murray, 2012).

In MRMDs, psychological symptoms coincide with phases of the menstrual cycle, providing an opportunity to systematically study the modulation of affective and cognitive state changes (Rubinow, 2021). Mental and behavioural states are implemented in the brain via the synchronous activity of neuronal populations, which represent direct substrates of neural information processing (Mathalon and Sohal, 2015). With EEG, electrodes are fixed to the scalp (Figure 1) and each channel records electrical activity (Louis et al., 2016). In order for the brain to produce and electrical signal that is detectable at the scalp, computational modelling has revealed it requires 10,000-50-000 synchronously active, apically projecting, neurons (Murakami and Okada, 2006). Thus EEG non-invasively records electrical activity in the brain, made up of primarily excitatory and inhibitory postsynaptic potentials in vivo, with high temporal and moderate spatial resolution (Biasiucci et al., 2019). This provides the opportunity to non-invasively explore the neural syntax underlying cognitive, affective, and behavioural dynamics in MRMDs. Interestingly, albeit not consistent, menstrual cycle dependent variations in the oscillatory activity has been shown (Thériault and Perreault, 2019).

Time or phase-locked cortical activity, (i.e. the neuronal activity that spontaneously arises at a specific time after a stimulus or falls into phase synchronicity as part of an ongoing oscillation) can be assessed by analysing event related potentials (ERPs), recorded with EEG. ERPs are high voltage fluctuations detected at the scalp relative to a reference electrode and studied based on their polarity and latency and spatial distribution or topography. ERPs provide a direct link between behaviour and stimulus- or task-related activation changes in mental processes such as memory, attention, emotion regulation, and beyond (Bridwell et al., 2018). Alternatively, the EEG can detect amplitude and frequency changes in oscillations while the brain is at rest (task-free), or when evoked (phase-locked) or induced (non-phase-locked). Neural oscillation amplitude and frequency changes are currently most often at a single source or based on coherence between sources. EEG may, therefore, serve as a candidate tool to disentangle neural markers of state retention and transition for MRMDs in a real-time fashion during task performance or at rest [90; 91].

An advantage of EEG for future translation of scientific findings to use as a clinical measurement for MRMDs includes the fact that EEG recordings are noninvasive, painless, and relatively cost effective compared to other brain imaging/recording techniques (for example, positron emission tomography (PET), MRI and magnetoencephalography (MEG) .Advancements in both EEG hardware and analysis pipelines have increased the robustness of detecting brain signal in the recorded data relative to sources of noise; indeed, physical movement, muscle, and electrical line noise can interfere with and present challenges for interpreting EEG data if not controlled. With such advancements, portable and wireless systems present a growing potential for bedside, outpatient or even home-based monitoring of the functioning brain (Niso et al., 2023).

Because EEG provides a direct index of neuronal activity, it is particularly well suited for accessing cyclical changes to neurotransmission. For example, changes in the power and peak frequency of resting state spectral bands across menstrual cycle phases have been found in several studies [93; 94; 95; 96; 97], with the most commonly reported observation being increased alpha power in the luteal phase [96; 98]. Evoked response paradigms (roving mismatch negativity and visually induced long-term potentiation) have shown that EEG is sensitive to changes in plasticity over the menstrual cycle (Sumner et al., 2018). Studies also show that EEG detects shifts in interhemispheric-transfer time; specifically, in the luteal phase there is an increase in the latency of visually evoked potentials in the hemisphere contralateral to the stimulated visual field (Hausmann et al., 2013). Induced changes to the EEG spectra include statistically significant increases in visually induced gamma oscillation frequency by ∼5 Hz in the luteal phase of healthy females (Sumner et al., 2018). Additionally, an MEG study showed reduced suppression response to induced gamma oscillations induced by moving gratings in PMDD (Manyukhina et al., 2022). Induced gamma oscillations have an established relationship with excitation and inhibition (Manyukhina et al., 2022) changes in the brain.

When the above EEG studies on changes to both evoked and induced signal over the menstrual cycle are considered alongside TMS based research [103; 104; 105], the interpretations consistently point to increased GABAergic inhibition in the healthy luteal phase. This is because the TMS studies cited [103; 104; 105] discuss their results in light of non-menstrual cycle related TMS findings based on GABAergic and glutamatergic drug interventions. The increase in GABAergic inhibition is hypothesized to be driven by progesterone’s metabolite allopregnanolone’s effects on the GABA-A receptor α4βδ subunit (Bäckström et al., 2014), which could play an important role in tonic inhibition and neuronal excitability variations. Allopregnanolone/oestradiol mediation of excitation and inhibition is one of the putative mechanisms implicated in the pathophysiology of MRMDs [107; 108]. However, further research, particularly using interventions, is required to deepen the potential inferences drawn from these data.

EEG can also be used beyond direct neurophysiological effects to study emotion and cognition. A common conceptual model in studying emotion regulation and vulnerability in psychopathology is frontal alpha asymmetry, already investigated in relation with the risk for, and treatment outcomes in, depression and anxiety disorders, among other psychopathologies [109; 110]. Frontal alpha asymmetry is most often calculated as the right-left difference in log-transformed spectral power in the alpha frequency band (8-13 Hz) between directly bilateral, frontal electrodes (Davidson, 1988). It has been studied in the context of motivation differences (defined as approach vs. avoidance behaviour in relation to some tasks) [112; 113]. High alpha asymmetry refers to lower frontal alpha power in the left relative to the right hemisphere and has been related to approach behavior, namely the willingness to explore reward sensitivity and related to increased positive affect. Low alpha asymmetry refers to the opposite pattern, namely higher left relative to right alpha power, and has been related to withdrawal and negative affect [112; 113].

In a meta-analysis, lower alpha asymmetry has been associated with depression, and dysphoric symptoms generally (Thibodeau et al., 2006). However, caution is advised over its reliability, with subsequent reviews arguing that alpha asymmetry is unlikely to become diagnostically useful despite the large numbers of studies using it (van der Vinne et al., 2017). It is suggested that, with future research, alpha asymmetry may instead be a marker for specific symptoms, such as suicidal ideation, or for differentiating depressive disorders (van der Vinne et al., 2017), or treatment response prognosis [115; 116]. Furthermore, lower alpha asymmetry has been consistently documented as a response to trauma-related stimuli in people with post-traumatic stress disorder (Meyer et al., 2015). Of relevance to the present review, changes in alpha asymmetry have also been documented across the menstrual cycle [97; 118].

There are numerous other examples of markers of mood disorders in the EEG literature related to clinically relevant phenotypes for MRMDs. Though not an exhaustive list, relevant examples include the reward positivity ERP in anhedonic depression (Proudfit, 2015), and the error-related negativity evoked response in anxiety disorders (Meyer, 2016). Alternatively, insight can be drawn from changes in EEG data features related to underlying neurobiological mechanisms. For example, bidirectional involvement of gamma and theta band activity as diagnostic classifiers or predictors of treatment response [116; 121].

While it is not within the scope of this review to consider all of the literature on EEG markers of mood disorders, it is of relevance to MRMDs that there is empirical evidence demonstrating the sensitivity of EEG to psychiatric symptomatology. Further, that there is a wealth of literature attempting to find the parameters of reliable and specific markers upon which future EEG research on MRMDs can draw, as the symptomatology profile partly overlaps with the one of mood and anxiety disorders (Halbreich, 2010). The current critical literature review aimed to summarize EEG findings on MRMDs (Figure 1), to identify gaps in this field of research, and to discuss future perspective in utilizing EEG biomarkers as diagnostic or prognostic indicators.